Interference Management In LTE-Advanced Heterogeneous Networks ...... User 1 is placed close to the Pico-eNB so we will call it âcenter Pico userâ, this is not ...
Interference Management in LTE-Advanced Heterogeneous Networks Using Almost Blank Subframes
HISHAM EL SHAER
Master's Degree Project Stockholm, Sweden
XREESB 2012:006
Interference Management In LTE-Advanced Heterogeneous Networks Using Almost Blank Subframes
Hisham El Shaer March 2012
Degree Project in Signal Processing Stockholm, Sweden 2012
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Abstract Long term evolution (LTE) is the standard that the Third-generation Partnership Project (3GPP) developed to be an evolution of UMTS. LTE offers higher throughput and lower latency than UMTS and this is mainly due to the larger spectrum used in LTE but in terms of spectrum efficiency LTE does not offer a lot of improvements compared to UMTS. The reason for that is that current technologies such as UMTS and LTE are approaching the theoretical boundaries in terms of spectral efficiency. Since spectrum has become a scarce resource nowadays, new ways have to be found to improve the network performance and one of the studied approaches to do that is to enhance the network topology. The concept of heterogeneous networks has attracted a lot of interest recently as a way to improve the performance of the network. The heterogeneous networks approach consists of complementing the Macro layer with low power nodes such as Micro or Pico base stations. This approach has been considered a way to improve the capacity and data rate in the areas covered by these low power nodes; they are mostly distributed depending on the areas that generate higher traffic. Since cell selection for the users is based on the downlink power level and due to the transmitting power differences between Macro and Pico nodes, Pico nodes might be under-utilized, meaning that a low number of users are attached to the Pico nodes. As a solution to this problem an offset to the received power measurements used in cell selection is applied allowing more users to be attached to the Pico nodes, this solution is called ‘Range Extension’ which refers to the extended coverage area of the Pico nodes. The problem with Range Extension is that it drastically increases the interference that the Macro nodes impose on the Pico nodes users in the Range Extension area in terms of data and control channels. Enhanced Inter-Cell Interference Coordination (eICIC) schemes have been proposed to combat the heavy interference in the Range Extension case ranging from frequency domain schemes like carrier aggregation to time domain schemes like Almost Blank Subframes (ABS). The focus of this thesis will be on the ABS solution which consists of reserving a group of subframes during which the Macro nodes are partially muted allowing the users in the range extension area to be served with lower interference. The objective of this thesis work is to introduce a closed form expression to calculate the Almost Blank Subframes allocation in order to maximize the normalized cell-edge users throughput. The derivations are carried out for a simplified model of a telecommunications network. The expression will be validated with simulations involving different users and Pico nodes distributions and different channel models (ITU channel models and Spatial Channel Models). Another goal is to try to have a deeper understanding and concrete conclusions about the different heterogeneous deployments.
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Acknowledgment This work would not have been possible to complete without the support of many people to whom I want to show my gratitude. First of all I would like to dedicate this thesis to my soon to be born daughter. I would like to thank my family and my wife for their continuous support and patience. I also want to thank my supervisor at Ericsson Niklas Wernersson and my manager Maria Edvardsson for their help and guidance during the project. Finally I want to thank my supervisor at KTH Mats Bengtsson for his support before and during the thesis.
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Table of Contents 1.
Introduction .......................................................................................................................................... 6 1.1
Brief about LTE .............................................................................................................................. 6
1.1.1
LTE requirements .................................................................................................................. 7
1.1.2
LTE downlink transmission scheme ...................................................................................... 7
1.1.3
Cyclic-Prefix insertion............................................................................................................ 8
1.1.4
Spectrum flexibility ............................................................................................................... 9
1.1.5
Physical resources ............................................................................................................... 10
1.1.6
Enhancements introduced in LTE advanced (Release 10) .................................................. 12
1.2
Introduction to Heterogeneous Networks (HetNets) ................................................................. 14
1.2.1 1.3 2.
3.
Motivation and description of HetNets .............................................................................. 15
Goal of the thesis ........................................................................................................................ 16
Range extension and associated problems......................................................................................... 17 2.1
Range extension introduction ..................................................................................................... 17
2.2
Range extension advantages ...................................................................................................... 18
2.3
Interference effects associated to range extension ................................................................... 18
Inter-cell interference available solutions .......................................................................................... 19 3.1
Frequency domain multiplexing inter-cell interference coordination scheme .......................... 19
3.2 Time domain multiplexing inter-cell interference coordination scheme (Almost Blank Subframes) .............................................................................................................................................. 20 4.
Range extension with almost blank sub-frames (ABS) ....................................................................... 22 4.1
Common reference signals (CRS) interference ........................................................................... 22
4.2
Proposed formula to calculate the ABS ratio to maximize the performance. ............................ 23
4.2.1
General model..................................................................................................................... 24
4.2.2
Simulations validating the previous results ........................................................................ 30
4.2.3
Example to validate the general model results .................................................................. 37
4.2.4
Example to validate the general model results (without the assumption of Ptotal_Pico) ...... 40
4.3 Summary ........................................................................................................................................... 43 5.
System simulation results ................................................................................................................... 44 5.1
The Raptor simulator .................................................................................................................. 44
5.2
System simulation assumptions.................................................................................................. 45
5.3
Simulation results ....................................................................................................................... 45 4
5.3.1 Who wins and who loses in terms of throughput in a heterogeneous network deployment? ....................................................................................................................................... 45 5.3.2
Simulations demonstrating the benefits of using ABS........................................................ 48
5.3.3 Simulations validating the ABS ratio formula for different users and Pico-eNBs distributions. ....................................................................................................................................... 53 5.3.4
Does having a high range extension give a better performance? ...................................... 68
6.
Conclusions ......................................................................................................................................... 71
7.
Future work ......................................................................................................................................... 71
8.
List of Acronyms .................................................................................................................................. 72
9.
References .......................................................................................................................................... 73
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1. Introduction In this section an introduction about Long Term Evolution (LTE) will be presented focusing only on the downlink since the thesis work mainly focuses on the downlink1 transmission, then an explanation of the heterogeneous networks (HetNets) concept, its motivation and its different types will be introduced. Finally the goal of the thesis and the contributions done in it will be introduced.
1.1 Brief about LTE Through the past few years the mobile broadband technology was released making it possible for applications such as live streaming, online gaming and mobile TV to be used on mobile handsets. However, the data rate requirements for these applications have grown exponentially. The Third-generation Partnership Project (3GPP)2 started working on solutions to fulfill the need for high data rates and came up with HSPA3 which is currently used in 3G phones for the before mentioned applications. In order to ensure the competitiveness of its standards in the future, 3GPP developed the Long Term Evolution (LTE) to be the 4th generation of mobile telephony. LTE as defined by the 3GPP [12] is the evolution of the 3rd generation of mobile communications (UMTS). The main goal of LTE is to introduce a new radio access technology with a focus on high data rates, low latency and packet optimized radio access technology, LTE is also referred to as E-UTRAN (Evolved UMTS Terrestrial Radio Access Networks). In December 2008, the LTE specification was published as part of Release 8 and the first implementation of the standard was deployed in 2009. The first release of LTE, namely release 8, supports radio network delay less than 5ms and multiple input multiple output (MIMO) antenna techniques which allow achieving very high data rates. Later on in December 2009 release 9 has been introduced with extensions to various features that existed in release 8 such as Closed Subscriber Group (CSG) and Self Organizing Network (SON). It added also new features such as Location Services (LCS) and Multimedia Broadcast Multicast Services (MBMS). Finally release 10 has been introduced in March 2011 which is also called LTE-Advanced and it added new features such as carrier aggregation, relaying and heterogeneous deployments which will be all discussed in details later. The rest of this LTE introduction will focus on the LTE requirements, the downlink transmission scheme and the spectrum flexibility.
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Downlink refers to the communication from the base station to the mobile user. 3GPP is a collaboration between groups of telecommunications associations with the goal of standardizing, developing and maintaining of a globally 3rd generation mobile phone system. 3 HSPA is short for High Speed Packet Access which is an amalgamation of the 2 protocols High Speed Downlink Packet Access (HSDPA) and High Speed Uplink Packet Access (HSUPA) 2
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1.1.1 LTE requirements The main requirements for an LTE system were identified in [1] and the most important ones can be summarized in the following points. -
Data rate: Peak data rates of 100 Mbps (downlink) and 50 Mbps (uplink) for a 20 MHz spectrum allocation.
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Throughput: The target downlink average user throughput per MHz is enhanced 3 to 4 times compared to release 64. The target for uplink average user throughput per MHz is enhanced 2 to 3 times compared to release 6.
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Bandwidth: Scalable bandwidths of 5, 10, 15 and 20 MHz shall be supported. Also smaller bandwidths smaller than 5 MHz shall be supported for more flexibility like 1.4 MHz and 3 MHz.
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Interworking: Interworking with existing UTRAN/GERAN5 and non-3GPP systems.
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Mobility: The system should be optimized for low mobile speeds (0-15 km/h) but should also support higher mobile speeds including high speed train environments.
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Coverage: The targets stated above should be met for 5 km cells6 and some degradation in throughput and spectrum efficiency for 30 km cells. Finally 100 km cells and larger are not covered by the specifications.
1.1.2 LTE downlink transmission scheme The LTE downlink transmission scheme is based on Orthogonal Frequency Division Multiplexing (OFDM) where the available spectrum is divided into multiple carriers called subcarriers. Data symbols are independently modulated and transmitted over orthogonal subcarriers where modulation schemes such as QPSK, 16QAM and 64 QAM are used. The subcarriers being orthogonal means that there is no interference between the subcarriers. OFDM transmission is a block based transmission where during each OFDM symbol interval N modulation symbols are transmitted in parallel. In practice an OFDM signal can be generated using IFFT (Inverse Fast Fourier Transform) digital signal processing which is an efficient way to generate an OFDM signal. Figure 1 illustrates an OFDM transmitter where OFDM modulation is done by means of IFFT processing [5]. As a first step the bits from the encoder are modulated into symbols, then these symbols are passed to a serial to parallel converter to be able to process the N symbols through the IFFT modulator
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3GPP standards are structured as releases, release 6 added mainly HSUPA and MBMS. UTRAN and GERAN are responsible for the specifications of the Radio Access part of UMTS (3G) and GSM/EDGE (2G) respectively. 6 A cell is the term used to describe the coverage area of a single base station and is usually illustrated by a hexagonal shape. 5
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simultaneously, then the IFFT samples are passed to a parallel to serial converter and to a digital to analog converter which sends the signal to the up-converter to be transmitted.
Parallel to serial converter
Figure 1: OFDM modulation by means of IFFT processing
1.1.3 Cyclic-Prefix insertion The main advantage of an OFDM signal is that it can be demodulated without any interference between the subcarriers due to the orthogonality between them. However, considering a time dispersive channel7, the orthogonality between the subcarriers will, at least, be partly lost. This loss of orthogonality in the time dispersive channel is due to the fact that the demodulator correlation interval of one path will overlap with the symbol boundary of another path as shown in Figure 2.
Figure 2: Time dispersion and received signal timing
Cyclic-prefix insertion implies that the last part of the OFDM symbol is copied and inserted at the beginning of the OFDM symbol as shown in Figure 3, so cyclic-prefix basically increases the length of the OFDM symbol from Tu to Tu+Tcp, where Tcp is the length of the cyclic-prefix which in turn reduces the OFDM symbol rate.
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Time dispersive channels are channels where multi-path exists and it is characterized by its time delay spread which is the total time interval during which reflections with significant energy reach the receiver.
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Figure 3: Cyclic prefix insertion
Cyclic-prefix preserves the orthogonality between the subcarriers in the case of a time dispersive channel as long as the span of the time shift or time difference between symbols is shorter than the cyclic prefix length. The problem with cyclic-prefix is that only a part of the received signal power is utilized by the OFDM modulator, so there is a power loss. Also there is a loss in terms of bandwidth as the symbol rate is reduced due to the insertion of the cyclic-prefix. One way to combat this loss of bandwidth is to reduce the subcarrier spacing. A detailed description of OFDM and cyclic-prefix is given in [2] and [18].
1.1.4 Spectrum flexibility Spectrum flexibility is one of the main characteristics of LTE radio-access technology. The main reason of this spectrum flexibility is to allow for the deployment of LTE radio-access in different frequency bands with different sizes since spectrum has become a scarce resource. This flexibility includes 2 main areas as follows. 1.1.4.1
Flexibility in duplex arrangements
One important aspect of LTE is the possibility to operate in both paired and unpaired spectrum. Paired frequency bands mean that the uplink and downlink transmissions use separate frequency bands while unpaired spectrum means that uplink and downlink transmissions share the same frequency band. LTE supports both frequency and time division based duplex arrangements. Frequency-Division Duplex (FDD), as shown in Figure 4, implies that uplink and downlink transmissions take place in different and sufficiently separated frequency bands. Time-Division Duplex (TDD), as shown in Figure 4, implies that uplink and downlink operate in different non-overlapping time slots.
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Figure 4: TDD and FDD operation
1.1.4.2
Bandwidth flexibility
Another important aspect in the LTE operation is the possibility to operate in different transmission bandwidths in uplink and downlink. The reason for that is that the amount of spectrum available for LTE deployment can vary a lot between frequency bands and also depending on the operator. Also this bandwidth flexibility gives the possibility for gradual frequency bands migration from other radio-access technologies.
1.1.5 Physical resources 1.1.5.1
LTE time domain structure
Downlink transmissions are organized in (radio) frames of length 10 ms which, in turn, are divided into 10 equally sized subframes of 1ms duration each. As illustrated in Figure 5, each subframe consists of 2 time slots of length Tslot=0.5 ms, where each time slot consists of a number of OFDM symbols including cyclic prefix.
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Figure 5: LTE frame structure
1.1.5.2
LTE frequency domain structure
A resource element is the smallest physical resource in LTE and it consists of one subcarrier during one OFDM symbol, resource elements are grouped into resource blocks. A resource block has a duration of 0.5 ms (one slot) and a bandwidth of 180 KHz (12 subcarriers) so each resource block consists of 12x7 = 84 resource elements in the case of normal cyclic prefix and 12x6 = 72 in the case of extended cyclic prefix. The LTE physical layer specification allows for a carrier to consist of any number of resource blocks in the frequency domain, ranging from a minimum of 6 resource blocks up to a maximum of 110 resource blocks which can be translated in frequency to a range between 1 MHz and 20 MHz with very fine granularity that allows for the spectrum flexibility discussed before. The time-frequency physical resources in LTE are shown in Figure 6.
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Frequency Time Figure 6: LTE frequency domain structure
1.1.6 Enhancements introduced in LTE advanced (Release 10) The most important target for LTE release 10 was to be able to fulfill the IMT-Advanced requirements. IMT is a global broadband multimedia international mobile telecommunication system that the ITU (International Telecommunication Union) has been coordinating along with governments, industry and private sector. IMT-Advanced is the term that ITU uses to describe radio-access technologies beyond IMT-2000. Some of the IMT-Advanced requirements are listed as follows [4]: -
Support for channel bandwidth up to 40 MHz. Peak spectral efficiencies of 15 bit/s/Hz in downlink (corresponding to peak rate of 600 Mbit/s). Peak spectral efficiencies of 6.75 bit/s/Hz in uplink (corresponding to peak rate of 270 Mbit/s). Control plane latency of less than 100 ms. User plane latency of less than 10 ms.
The main reason for LTE release 10 to be called LTE-Advanced is that its radio-access technology is fully compliant with the IMT-advanced requirements. In the following we introduce some of the most important enhancements and features introduced in LTEAdvance.
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1.1.6.1
Carrier aggregation
As mentioned before the previous releases of LTE have introduced a lot of flexibility in terms of bandwidth as it allows operating in bandwidths ranging from 1 MHz to 20 MHz in both paired and unpaired modes. In LTE release 10 the transmission bandwidth can be further extended using “carrier aggregation”. The main idea is to aggregate several component carriers and jointly use them for transmission to and from single terminals. Up to 5 transmission components can be aggregated whether they belong to the same frequency range or not and this feature allows the transmission bandwidth to reach 100 MHz, it also allows to make use of the fragmented spectrum, as operators with fragmented spectrum can use this feature to offer high data-rates by combining all the small spectrum fragments into a sufficiently large component. 1.1.6.2
Extended multi –antenna transmission
In LTE release 10, downlink spatial multiplexing has been expanded to support up to 8 transmission layers so together with carrier aggregation a downlink data rate of up to 30 bit/s/Hz can be achieved. In terms of uplink, spatial multiplexing of up to 4 layers is supported by release 10, this allows for an uplink data-rate of 15 bit/s/Hz. 1.1.6.3
Relaying
Relaying implies that the mobile node is connected to its serving cell through a relay node that is wirelessly connected to the serving node using the LTE radio-interface technology. From a mobile node perspective the relay node is invisible as the mobile node can only see that it is connected to the serving base station. This feature has the advantage of improving the coverage especially in indoor environments. 1.1.6.4
Heterogeneous deployments
Heterogeneous deployments refer to deployments where we have base stations with different transmission powers and coverage areas sharing, fully or partially, the same set of frequencies and having an overlapping geographical coverage. An example of Heterogeneous networks is having a Pico-eNB8 placed in the coverage area of a Macro-eNB9. Heterogeneous networks, also called HetNets, were supported by release 8 and 9 but release 10 introduced improved inter-cell interference handling making HetNet scenarios more robust. The rest of this report will focus on HetNets and the Enhanced Inter-Cell Interference Coordination (eICIC) used by release 10 to combat the interference caused by the Macro-eNBs to the Pico-eNB users.
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Pico-eNB is a low transmitting power base station that has limited coverage and will be explained in details later. Macro-eNB is the normal base station which is called eNB (short for evolved node B.) in LTE.
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1.2 Introduction to Heterogeneous Networks (HetNets) Mobile broadband traffic has been growing very fast through the past few years; it surpassed voice traffic and is expected to grow much faster in the future. This growth is mainly driven by new services and the evolution of terminals capabilities. Annual traffic is predicted to double annually during the next five years so that by 2014 the average user traffic will be about 1 GB of data per month compared to 100 or 200 MB now [5]. The mobile industry has been striving to improve data rates indoors and outdoors to be able to meet the evolution of mobile services. There are several options that can be considered to increase the network capacity and meet traffic and data rates demands such as: -
Improving the Macro layer: Upgrading the radio access of existing sites whether HSPA or LTE would increase the data rates, this can be done by adding more spectrum which can notably enhance the downlink data rates although the enhancement is negligible in the uplink. Another option would be to add more antennas or enhance the processing within and between the nodes. But at some point the capacity and data rates enhancements introduced by improving the radio access of the nodes would be insufficient.
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Densifying the Macro layer: Increasing the number of Macro sites in urban and dense areas has been a popular approach taken by operators to combat the traffic increase, it has the advantage of decreasing the distance between the user and the serving base station so the uplink data rate is largely enhanced and of course it has a big effect on the downlink data rates as well. The problem with this approach is that it is very expensive to add more Macro sites in terms of cost, finding suitable locations to deploy new sites and interference as we are placing high power nodes closer to each other.
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Heterogeneous networks: This approach consists of complementing the Macro layer with low power nodes such as Micro and Pico base stations. This approach has been considered a way to improve the capacity and data rate in the areas covered by these low power nodes; they are mostly distributed in an unplanned manner depending on the areas that generate higher traffic.
Through the rest of the report we will focus on Heterogeneous networks and specifically on the Pico base stations deployments that will be referred to as Pico-eNB for the rest of the report and will be described in details in the following section.
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1.2.1 Motivation and description of HetNets The concept of Heterogeneous networks has attracted a lot of interest recently to optimize the performance of the network. Spectral efficiency of current systems like WCDMA and LTE is approaching theoretical boundaries [13], we can see that from the fact that LTE release 8 does not offer a lot of improvements in terms of spectral efficiency compared to UMTS, instead LTE improves system performance by using more spectrum and since spectrum has been a scarce resource in the past few years a different approach must be considered to improve network performance. The main approach to enhance the performance is to improve the network topology. This is done in the scenario of Heterogeneous networks by overlaying the planned network of high power Macro base stations with smaller low power Pico base stations that are distributed in an unplanned manner or simply in hotspots where a lot of traffic is generated. These deployments can improve the overall capacity and the cell edge users10 performance. [2]
Figure 7: Heterogeneous network using Pico-eNBs
1.2.1.1
Properties of Pico base stations:
1) 2) 3) 4)
They have a transmission power of 1W. They can be deployed to eliminate coverage holes. Offer high data rate and capacity where they are deployed. Offloading the Macro-eNBs by serving some users that used to belong to the Macro-eNBs, which allows the Macro-eNB to serve better its users. 5) Due to their low transmission power and small physical size they can offer flexible site acquisitions. In the following section we will explain how to optimize the performance of Pico-eNBs and the problems that face this approach.
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Cell-edge users, in this report, are defined to be the worst 5% of the total number of users in terms of capacity or throughput.
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1.3 Goal of the thesis The goal of this thesis is to find a closed form expression for the Almost Blank Subframes (ABS) allocation that optimizes the network performance in terms of cell edge users throughput. Most of the previous work has focused on the allocation of ABS depending on the ratio between the number of Macro users per cell and the number of range extension users per Pico cell as in [14] and [17] which basically means that the ABS allocation depends on the ratio of the number of the Macro users to the number of range extension users belonging to each Pico-eNB or just choosing the Pico-eNB with the maximum number of range extension users and applying that to all the Pico-eNBs. Through this thesis we will deduce a formula, theoretically and using simulations, for the ABS allocation that depends on the ratio of the number of Macro users to the total number of range extension Pico users in a cell and it will be proven that it gives a better performance in terms of cell edge users throughput. The main contributions of this thesis can be summarized in the following points. 1. Running system simulations in order to have solid conclusions about HetNets, concerning the users who experience an increase or decrease of throughput after adding the Pico layer and the reasons behind that. Also extract some conclusions about the Almost Blank Subframes as a TDM interference coordination scheme in terms of its advantages and the winners and losers in this scenario. 2. Deduce a closed form expression for the ABS allocation that optimizes the performance in terms of cell edge users throughput while keeping a fair level of normalized throughput. The deduction will be done theoretically and will be validated using system level simulations. 3. Implement a graphical interface for the Raptor system simulator, which is the simulator I am working on in Ericsson. This graphical interface will be used to illustrate a hexagonal cellular network featuring the Macro-eNBs, Pico-eNBs and the simulated users. It allows focusing on a specific user or group of users and studying their statistics. An example of this graphical interface will be presented in the simulations section.
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2. Range extension and associated problems 2.1 Range extension introduction Cell selection in LTE is based on terminal measurements of the received power of the downlink signal or more specifically the cell specific reference (CRS) downlink signaling. However; in a heterogeneous network we have different types of base stations that have different transmission powers including different powers of CRS. This approach for cell selection would be unfair to the low power nodes (Pico-eNBs) as most probably the terminal will choose the higher power base stations (Macro-eNBs) even if the path loss to the Pico-eNB is smaller and this will not be optimal in terms of: - Uplink coverage: as the terminal has a lower path loss to the Pico-eNB but instead it will select the Macro-eNB. - Downlink capacity: Pico-eNBs will be under-utilized as fewer users are connected to them while the Macro-eNBs could be overloaded even if Macro-eNBs and Pico-eNBs are using the same resources in terms of spectrum, so the cell-splitting gain is not large and the resources are not well utilized. - Interference: due to the high transmission power of the Macro-eNBs, then the Macro-eNB transmission is associated with a high interference to the Pico-eNB users which denies them to use the same physical resources. As a solution for the first 2 points cell selection could be dependent on estimates of the uplink path loss, which in practice can be done by applying a cell-specific offset to the received power measurements used in typical cell selection. This offset would somehow compensate for the transmitting power differences between the Macro-eNBs and Pico-eNBs; it would also extend the coverage area of the Pico-eNB, or in other words extend the area where the Pico-eNB is selected. This area is called “Range Extension” and is illustrated in Figure 8.
Figure 8: range extension area illustration
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2.2 Range extension advantages 1) Applying range extension would maximize the achievable uplink SINR which in turn maximizes the uplink data rate. 2) The terminal transmit power would be reduced as the path loss to the Pico-eNB is lower than the one to the Macro-eNB so the interference to other cells would be reduced and the uplink system efficiency would be improved. 3) It also allows more users to be connected to the Pico-eNB, thus increasing the cell splitting gain. 4) Since the Macro-eNB transmits to fewer users then the interference it applies on the Pico-eNB is reduced and the Pico-eNBs can reuse the resources more efficiently so the downlink system efficiency is maximized as well.
2.3 Interference effects associated to range extension Due to the difference in transmission powers of the Macro-eNBs and the Pico-eNBs, in the range extension area, illustrated in Figure 8, where the Pico-eNB is selected by the terminal while the downlink power received by that terminal from the Macro-eNB is much higher than the power it receives from the Pico-eNB, this makes the users in the range extension area more prone to interference from the MacroeNB. So along with the benefits of range extension comes the disadvantage of the high inter-cell interference that the Macro layer imposes on the users in the range extension area of the Pico layer. Figure 9 illustrates the comparison of 2 users connected to the Pico-eNB where: -
-
User 1 is placed close to the Pico-eNB so we will call it “center Pico user”, this is not affected very much by the Macro-eNB interference as the downlink received power from the Pico-eNB is higher than the one received from the Macro-eNB. User 2 is placed farther from the Pico-eNB, in the range extension area, and as discussed before this user endures a severe interference from the Macro-eNB.
Solutions for the high interference levels in the range extension area will be discussed in the next section.
Figure 9: range extension interference
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3. Inter-cell interference available solutions The enhanced Inter-Cell Interference Coordination (eICIC) in heterogeneous networks introduced in LTE-Advanced has been a hot topic lately as without an efficient inter-cell interference scheme the range extension concept loses its advantage and efficiency. The problem with ICIC schemes in releases 8 and 9 was that they were only considering data channels and did not focus on the interference between control channels, so LTE release 10 solves this problem with the solutions in the following subsections. The solutions are mainly divided into frequency domain solutions such as carrier aggregation and time domain solutions such as almost blank subframes (ABS), and they will be discussed in details in the following.
3.1 Frequency domain multiplexing inter-cell interference coordination scheme The main FDM interference cancellation method used in LTE-Advanced is carrier aggregation; this feature has been discussed in section 1.1.6.1 which is one of the most important features of LTEAdvanced and it basically enables an LTE-Advanced user equipment (UE) to be connected to several carriers simultaneously. Carrier aggregation not only allows resource allocation across carriers but also allows scheduler based fast switching between carriers without time consuming handovers, which means that a node can schedule its control information on a carrier and its data information on another carrier. An example of that concept in a HetNet scenario is to partition the available spectrum into, for example, 2 separate component carriers, and assign the primary component carrier (f1) and the second component carrier (f2) to different network layers at a time as shown in Figure 10 .
Figure 10: Illustration of eIIC based on carrier aggregation
In the example we have 2 component carriers f1 and f2 where 5 subframes are shown in each carrier. There are 2 cases, the case of Macro layer usage and the case of Pico layer usage; the subframes are distributed in control part, the blue part, and data part. The control part in the example only illustrates the PDCCH, PCFICH and PHICH11 at the beginning of the subframes. 11
See list of acronyms.
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As shown Figure 10 the Macro layer can schedule its control information on f1 but can still schedule its users on both f1 and f2 so by scheduling control and data information for both Macro and Pico layers on different component carriers, interference on control and data can be avoided. It is also possible to schedule center Pico-eNB users12 data information on the same carrier that the Macro layer schedules its users as shown in the third subframe in Figure 10, as the interference from the Macro layer on center Pico-eNB users can be tolerated, while Pico-eNB users in the range extension areas are still scheduled in the other carrier where the Macro-eNB users are not scheduled. The disadvantage of carrier aggregation with cross carrier scheduling is that it is only supported by release 10 terminals and onwards so this feature cannot be used by release 8 and 9 terminals.
3.2 Time domain multiplexing inter-cell interference coordination scheme (Almost Blank Subframes) In this approach transmissions from Macro-eNBs inflicting high interference onto Pico-eNBs users are periodically muted (stopped) during entire subframes, this way the Pico-eNB users that are suffering from a high level of interference from the aggressor Macro-eNB have a chance to be served. However this muting is not complete as certain control signals are still transmitted which are: -
Common reference symbols (CRS) which will be explained later Primary and secondary synchronization signals (PSS and SSS) Physical broadcast channel (PBCH) SIB-113 and paging with their associated PDCCH.
These control channels have to be transmitted even in the muted subframes to avoid radio link failure or for reasons of backwards compatibility, so muted subframes should be avoided in subframes where PSS, SSS, SIB-1 and paging are transmitted or in other words subframes #0, #1, #5 and #9. Since these muted subframes are not totally blank they are called Almost Blank Subframes (ABS). The basic idea is to have some subframes during which the Macro-eNB is not allowed to transmit data allowing the range extension Pico-eNB users, who were suffering from interference from the Macro-eNB transmission, to transmit with better conditions. The outline of ABS has been specified by the 3GPP in [15]. ABS have specific patterns that are configured and communicated between the eNBs over the X2 interface. These patterns are signaled in the form of bitmaps of length 40 subframes, i.e. spanning over 4 frames and they can be configured dynamically by the network using self-optimizing networks (SON) feature to optimize the ABS ratio according to some criterion that can be the cell-edge users throughput or load balancing for instance and of course keeping in mind the above mentioned subframes that should be avoided.
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Center Pico-eNB users are the users connected to the Pico-eNB but that are not in the range extension area. See acronyms list.
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Figure 11: Illustration of TDM ICIC
As shown in Figure 11, TDM ICIC using ABS causes a lot of variation in terms of interference between the subframes, this fact can be used in the sense that the users that suffer from a high level of interference should be served during these ABS while the users that are closer to the transmitting node or that are not very much affected by interference can be served during the non-ABS subframes. So for a Pico-eNB cell, users are categorized into 2 groups in terms of ABS usage this time: -
Users in the range extension area and these users suffer from a high level of interference as explained before so these users should only be served during the ABS. Users closer to the Pico-eNB that are called center Pico users and they are not heavily affected by the interference from the Macro-eNB due to the good channel they maintain with their serving node. So these users can be served by any subframe whether ABS or non-ABS.
One of the properties of LTE release 10 is that it allows eNBs to restrict the channel measurements done by the users attached to them to a specific set or pattern of subframes. The reason for that is that if the channel state information (CSI) measurements which are responsible of reporting the channel conditions were to be done jointly for ABS and non-ABS, they will not accurately reflect the interference of either type of subframes. So the terminals are configured with different CSI-measurement subsets corresponding to the subframes that the terminal is allowed to use. Users belonging to the range extension area are only allowed to report CSI measurements for the ABS as they are only allowed to transmit during these subframes. Users belonging to the center Pico-eNB area transmit 2 different subsets of the CSI measurements, one for the ABS and another for the non-ABS as they are allowed to transmit through all the subframes. CRS interference will be discussed in the following section.
21
4. Range extension with almost blank sub-frames (ABS) 4.1 Common reference signals (CRS) interference Common reference signals (CRS) are transmitted in every downlink subframe and in every resource block in the frequency domain, so they cover the entire cell bandwidth. CRS can be used by the terminal for channel estimation for coherent demodulation of downlink physical channels [2]. They can also be used by the terminal to acquire channel state information (CSI) which is used as the basis for cell selection and handover decisions.
Figure 12: Structure of CRS within a pair of resource blocks
As shown in Figure 12, the structure of a single cell-specific reference signal consists of reference symbols of predefined values inserted within the first and third last OFDM symbol of each slot, so within each resource block pair there are 8 reference symbols, also the number of different reference signals in a cell corresponds to the number of antenna ports available in the cell. CRS is considered as the most important cause of interference in ABS as CRS exists in every resource block as shown in Figure 12. CRS can be eliminated with different strategies that are explained in [16]: - Using Multicast-Broadcast Single Frequency Network Subframe: which is a specific subframe where CRS is not transmitted in the data part but is still transmitted in the control part. - Interference cancellation of CRS from Macro-eNB cells: Using techniques to cancel the CRS effect such as successive interference cancellation. - Puncturing of resource elements in which Macro-eNB transmits CRS: which means not considering the resource elements where CRS is present. Throughout the rest of the report we will consider perfect CRS interference cancellation and we will focus on optimizing the ABS ratio.
22
4.2 Proposed formula to calculate the ABS ratio to maximize the performance. In this section we will deduce a closed form expression for the ABS (Almost Blank Subframes) allocation percentage or ratio14 that maximizes the performance of the network in terms of cell-edge users capacity. As was stated before the ABS configuration is communicated between the nodes using a 40 subframes pattern, so by optimizing the ABS ratio we mean optimizing the number of subframes that are considered as ABS in this pattern. In the following example a round robin scheduler is considered where Macro-eNB users and center PicoeNB users are only allowed to be scheduled in the non-ABS while the range extension Pico-eNB users are only allowed to be scheduled in the ABS. The constraint on the center Pico-eNB users is introduced for simplicity and to allow the range extension users some fairness in using the ABS because in reality ABS are shared between center and range extension Pico-eNB users and it becomes harder to determine which users are scheduled in the ABS. First we start by an introduction about round robin scheduler and why it is used in this example. Round robin is a simple scheduling method that is based on assigning the resources to the terminals in turn, one after another, which means that all the users have equal chances to be scheduled without considering their CQI (channel quality indicator) which is explained in the flow chart in Figure 13.
Figure 13: Flow chart explaining the round robin scheduler
14
By this we mean the number of subframes that are used as ABS out of the total number of subframes in the pattern, so if we use 10 subframes out of 40 as ABS the ratio would be 0.25.
23
The reason for using round robin scheduling is its simplicity and that it is very convenient to use in a theoretical example to ensure that all the users have the same chance of being scheduled and then comparing users in terms of capacity and throughput for instance. The rest of this section will be divided into 3 parts; the first one is a general model that is used to deduce a general formula for calculating α which is the ABS ratio, and the second part consists of simulations that validate the theoretical results and finally an example with a specific setup of the model in the first part, which means specifying the path loss model, transmitting power and position for each node, which is also used to validate the results.
4.2.1 General model Considering a simple setup having a 1 cell network with the following features: a. This cell contains 1 Macro-eNB and a certain number Npico of Pico-eNBs. The Pico-eNBs are randomly distributed in the cell. b. The users are randomly distributed throughout the cell area. c. All Pico-eNBs have the same number of users in the range extension area. d. Round robin scheduler is used as explained in the previous section If we consider a channel model15 that is only impaired by additive white Gaussian noise (AWGN) and interference, then the ith user capacity16 will be according to the following equation ‖ ‖
(1)
where hi is the channel gain, SINRi is the signal to interference and noise ratio and BW is the bandwidth which is considered to be 1 Hertz through the whole example for simplicity, also the number of subframes is assumed to be 1. The following notation will be used in the deduction. Macro-eNB transmission power Pico-eNB transmission power Channel gain from Macro-eNB to the ith user Channel gain from the kth Pico-eNB to the ith user number of ues per Macro-eNB number of Pico-eNBs number of center Pico-eNB ues per Pico-eNB number of range extension ues per Pico-eNB Almost blank subrames ratio the noise in the system
P1 P2 (hm_ue)i (hp_ue)k,i Nm Npico Np_c Np_re α (Alpha) N0
Table 1
15
Here every user has a different channel to each node (Picos and Macro) so each user has a vector (Npico+1) long of channels that are only impaired by AWGN and interference. 16 Channel capacity is defined as being the tighter upper bound of the amount of information that can be transmitted over a communication channel.
24
As explained before, cell selection is based on the downlink reference signal power measurements so the users attached to the Macro-eNB (Nm) have a higher downlink power coming from the Macro-eNB than the Pico-eNBs, While center Pico-eNB users (Np_c) receive the reference signals from the Pico-eNB with a higher power than the signals coming from the Macro-eNB. Finally for the range extension Pico-eNB users (Np_re), although they receive the reference signals from the Macro-eNB with a higher power but due to the range extension offset, that was explained before, these users are attached to the Pico-eNB. So using the above notation the capacity for the users attached to the different nodes can be formulated as follows starting by the ith Macro-eNB user capacity in equation (2). ‖
∑
‖
‖
‖
(2) .
(3)
Then the capacity of the ith center Pico-eNB user attached to the kth Pico-eNB ‖
‖
‖
‖
(4)
∑
‖
‖
.
(5)
And finally the ith range extension Pico-eNB user attached to the kth Pico-eNB ‖
∑
‖
‖
‖
(6) .
(7)
We can plot the users capacity in equations (2), (4) and (6) as a function of α, so by choosing one user from each group (Macro, center Pico and range extension Pico) and specifying values for the different parameters (channel gains, P1, P2, Nm, Np_c and Np_re) we get the plot in Figure 14.
25
60 macro user capacity center pico user capacity range extension user capacity
50
capacity
Capacity (bits/sec)
40
30
20
10
0
0
0.1
0.2
0.3
0.4
0.5 alpha
0.6
0.7
0.8
0.9
1
Figure 14: Plot of the capacity of Macro-eNB, center Pico-eNB and range extension users against α
So in order to maximize the cell edge users capacity17 we need to find the intersection point between the lowest range extension capacity line, corresponding to the range extension user having the lowest capacity, and the first line it intersects with which is the lowest Macro-eNB or center Pico-eNB user capacity line, corresponding to the Macro-eNB or center Pico-eNB user having the lowest capacity. So we can define the intersection point, which is basically found by a search over , using the following criterion: {
}
(8)
In this case we will not consider the center Pico-eNB capacity line, so we will only focus on the range extension and Macro-eNB users as in reality center Pico-eNB users are not affected by the ABS ratio, but here we assume that center Pico-eNB users are only allowed to transmit during non-ABS to make the scheduler simpler and giving the Macro-eNB user and Pico-eNB range extension user an equal chance to be scheduled. We will denote the Macro-eNB user having the lowest capacity by user “m” having the following capacity ‖
∑
‖
‖
(9)
‖
.
(10)
17
In this model we maximize the worst user (0% worst user) capacity instead of the cell edge users (5% worst users) capacity for simplicity.
26
We will denote the range extension user having the lowest capacity by user “n” and assuming that this user belongs to the kth Pico-eNB with the following capacity ‖
∑
‖
‖
(11) .
‖
(12)
The intersection point can be acquired analytically by equating equations (9) and (11) in order to find the optimum alpha that maximizes the cell edge capacity as follows ‖
‖
‖
‖
(13)
And by reordering the previous equation we get the following equation which can be considered as the optimal value of α in order to optimize the 0% worst user throughput.
‖
(14)
‖ ‖
‖
Since the mth Macro-eNB user capacity is given by eq. (9) so considering that only this Macro-eNB user gets all the resources all the time then the capacity would be given by the following expression, i.e. putting the number of users to 1 in eq. (1)18. ‖
‖
.
Which we can call the maximum Macro-eNB user capacity, so is the same as only assuming that the Macro-eNB is only serving this user m, this is why it is called because this is the maximum capacity that this user can reach. And doing the same for the nth range extension Pico-eNB user ‖ Then
‖
(15) but
(16)
can be expressed as .
(17)
From this equation we can clearly see that alpha depends on 2 factors: 1. The ratio between the number of Macro-eNB ues to the number of range extension ues per Pico-eNB. 2. The ratio between the maximum capacity of a range extension user and the maximum capacity of a Macro-eNB user . 18
This is exactly as if we have only one Macro-eNB user so this user will use the available resources (subframes) all the time.
27
Focusing on the second factor and trying to simplify it, starting with the maximum Macro-eNB user capacity ‖
‖
∑
‖
‖
(18)
Since the noise value is very small we can neglect it also assuming the value of P 1 to be very large so (‖‖ ‖ P1) is much bigger than the term in the denominator then we can approximate the previous equation to ‖
‖
∑
‖
‖
.
(19)
Normally most users attached to the Macro-eNB are placed close to it, although some Macro-eNB users are placed very close to the Pico-eNB due to the high transmission power of the Macro-eNB but we will consider only the users closer to the Macro-eNB, who are the majority, and assuming that the interference to these users is dominated by one or at most two Pico-eNBs while the rest cause negligible interference. Under this assumption we can approximate the interference term ∑ ‖ ‖ with a constant (I) since it is assumed to be independent on the number of Pico-eNBs and is dominated by the interference caused by the closest 1 or 2 interferer Pico-eNBs. ‖ Since denoted by C1.
‖
).
(20)
is assumed to be independent on Npico so it can be considered as a constant and can be
Now focusing on the second term which is ‖
.
‖
.
(21)
Inserting the SINR3 expression ‖
‖
∑
‖
‖
(22)
Assuming that we have a very large Npico then N0 can be neglected, considering that P2 ≠0, and the interference term in the denominator would be larger than the numerator so the previous equation can be approximated to ‖ ∑
‖ ‖
‖
(23)
where k is the serving Pico-eNB for the range extension user.
28
Since the Pico-eNBs are distributed randomly in the cell so and can be considered as independent and identically distributed (IID) random variables. Also since we are trying to optimize the capacity and we are assuming a large Npico so optimizing would be the same as optimizing its expected value so we can replace by as follows ‖ ∑
‖ ‖
‖ ∑
‖
‖ ‖
‖
.
(24)
Since all the values of can be considered as independent identically distributed (IID) random variables having the same mean value and can be expressed as ‖
19
∑
‖
‖
‖ ‖
‖
.
(25)
can be considered as a constant value so ‖ (
and finally the term
‖
‖
‖
‖ )
‖
‖
(26)
‖
can be considered as a constant and can be denoted by C2 and since
Npico is assumed very large so
and .
can be expressed as (27)
Finally . So
(28)
can be expressed as (29)
where Nre*Npico is equal to the total number of range extension users which can be denoted by Nre_total.. Finally is expressed by .
19
It is known that
(30)
but we will use this approximation anyway to simplify the problem. Also the
variance of the values has been found to be very small, in the order of 10 -14, which verifies the approximations done in this equation.
29
So if the values of C2 and C1 are assumed to be approximately equal, which will be shown in the following sections, then we can introduce which is considered, according to simulations, to be the optimized value that gives the optimal or suboptimal value of
and is expressed by:
.
(31)
is proportional to the ratio between the number of users attached to the Macro-eNB and the total number of range extension users attached to the Pico-eNBs. This means that the ABS ratio
4.2.2 Simulations validating the previous results In this section a small MATLAB system simulator that performs Monte Carlo20 simulations [11] will be introduced to verify the results in the previous section specifically equations (29) and (31) as they are considered the most important results in the deduction. The simulations consist of a 1 cell network with a Macro cell at a predefined position and a specific number of Pico-eNBs and users are dropped randomly throughout the cell area. The path loss is calculated according to 2 models, the ITU channel model and the Spatial Channel Model (SCM) which will be explained in details in the following. -
ITU channel model: we will use the urban Macro-eNB (UMa), for Macro-eNB users, and urban micro (UMi), for Pico-eNB users, models in [6].
Assuming that all users have line of sight to the serving base station so the path loss in dB for Macro-eNB users will be calculated according to for d < 160 m
(32)
for d > 160 m
(33)
where d is the distance between the user and the node, h’BS = 24m, h’UT = 0.5 m and fc=1 GHz. And for Pico-eNB users the path loss is given as for d < 120 m
(34)
for d > 120 m
(35)
where d is the distance between the user and the node, h’BS = 9m, h’UT = 0.5 m and fc=1 GHz.
20
Monte Carlo method is a class of computational algorithms that depends on repeated random sampling to compute its results which in our case means to drop the users and Pico-eNBs repeatedly and in a random way to compute the end result.
30
-
Spatial channel model: This model will be calculated according to the equations in [10] and assuming no line of sight for both Macro-eNB and Pico-eNBs.
For the Macro-eNB users the path loss in dB is given by d PL[dB] 44.9 6.55log10 hbs log10 ( ) 45.5 1000 35.46 1.1hms log10 ( fc ) 13.82log10 (hbs ) 0.7hms C
(36)
where hbs is the base station antenna height in meters, hms is the MS antenna height in meters, fc the carrier frequency in MHz, d is the distance between the BS and the user in meters, and C is a constant which is equal to 3dB for urban Macro-eNB. These parameters are set to hbs = 32m, hms = 1.5m and fc=1900MHz. And the path loss for Pico-eNB users is given by PL = -55.9 + 38*log10(d) + (24.5 + 1.5*fc/925)*log10(fc)
(37)
where fc = 1900 MHz. The idea is to use the Monte Carlo method to compare the optimum alpha, given by equation (14), with the deduced alpha in (29) and (31). In order to do that, an average of 100 drops21, with a random realization for the positioning of the Pico-eNBs and users for each drop, will be used to calculate an average value of alpha and this process will be repeated 500 times so that we will have 500 calculated alpha for each equation at the end then we compare the results. 4.2.2.1
Validating the alpha expression:
In this section we will validate the α expression given by equation (29), the idea is to calculate the value of α according to equations (29) and the optimum value of α according to equation (14), this process will be iterated 500 times, as explained before, so at the end we will have 2 vectors of α, each consisting of 500 values, that we can compare and if the values in both vectors are approximately equal, then equation (29) can be validated to give an optimal value for α. Since in the deduction we assume having a large number of Pico-eNBs, we will drop 100 Pico-eNBs and 200 users randomly and alpha will be calculated according to equations (14) and (29) and both values will be compared, listed in Table 2 are the parameters used in this simulation. Cell area22 Macro-eNB position Pico-eNBs positions Users positions Macro-eNB transmitting power Pico-eNB transmitting power Number of drops
50m x 50m X:0 Y:25 Random but keeping a minimum distance of 10 m from the Macro-eNB. Random 40 W 1W 100 Table 2
21
A drop is defined as one simulation run over a certain time period. The reason for having a very small cell area is to decrease the distance between the Pico-eNB-eNBs to increase the interference between them to fulfill the assumption of having a very big number of Pico-eNB-eNBs in the cell. 22
31
The only problem in equation (29) is that the value of ‖ ‖ in C2 is not known; also the assumption that all the Pico-eNBs have the same number of range extension users is not present in this simulation so we will go back 1 step to equation (25) which was given by ‖
23
∑
and will consider given by
‖ ‖
(38)
‖
to be the constant and will be denoted by
so the value of alpha will be
(39) 4.2.2.1.1
ITU channel model
We start by the ITU channel model. Figure 15 represents the PDFs of the 500 alpha values calculated from equations (14) and (39). And it shows that the PDFs are concentrated at very close values. 120 Optimal Alpha according to eq (14) Alpha according to eq (29) 100
80
60
40
20
0
0.65
0.7
0.75
0.8
0.85
Alpha
Figure 15: PDFs of Alpha according to eq (14) (Blue) and Alpha according to eq (39) (Green)
Figure 16 represents a plot of the alpha value in both cases for 500 iterations; each iteration is an average of 100 drops. If we compare both values at any of the 500 measurements we will see that the difference between them is always less than 0.1 which means that the value of alpha calculated in equation (39) gives the optimal or the suboptimal value of the ABS ratio24. It can be seen from these results that the result from equation (39) can be validated to give the optimal or suboptimal ABS ratio for the ITU channel model.
24
It will be shown in the simulations section that if the formula gives a solution that is 0.1 less or more than the optimal one this solution is the suboptimal one, which means that it is the second best solution, and is very close to the optimal solution.
32
0.85 Optimal Alpha according to eq (14) Alpha according to eq (31) 0.8
Alpha
0.75
0.7
0.65
0
50
100
150
200
250 Itteration number
300
350
400
450
500
Figure 16: Plot of the 100 values of Alpha according to eq (14) (Blue) and Alpha according to eq (40) (Green)
4.2.2.1.2
Spatial channel model
In this part we will repeat the previous simulation but using the Spatial Channel Model instead of the ITU channel model. Figure 17 represents the PDFs of the results from equations (14) and (39). And it shows that the pdfs are concentrated at very close values. 140
120
Optimal Alpha according to eq (14) Alpha according to eq (29)
100
80
60
40
20
0 0.25
0.3
0.35
0.4
0.45
0.5 Alpha
0.55
0.6
0.65
0.7
0.75
Figure 17: PDFs of Alpha according to eq (14) (Blue) and Alpha according to eq (39) (Green)
Figure 18 represents the plot of the alpha value in both cases for 500 iterations; each iteration is an average of 100 drops. If we compare both values at any of the 500 measurements we will see that the difference between them is always less than 0.1 which means that the value of alpha calculated in equation (39) gives the optimal or the suboptimal ratio of ABS. It can be seen from these results that the result from equation (39) can be validated to give the optimal or suboptimal ABS ratio for the spatial channel model. 33
0.75 Optimal Alpha according to eq (14) Alpha according to eq (31)
0.7 0.65 0.6
Alpha
0.55 0.5 0.45 0.4 0.35 0.3 0.25
0
50
100
150
200
250 300 Itteration number
350
400
450
500
Figure 18: Plot of the 100 values of Alpha according to eq (14) (Blue) and Alpha according to eq (40) (Green)
In this section equation (39) which is the same as equation (29) has shown to be giving results very close to those of equation (14) which, in turn, shows that equation (29) gives the optimal ABS ratio in terms of cell edge users throughput in the case of the ITU channel model and spatial channel model. It is also worth noting that the difference between the alpha values according to equation (29) and equation (14) is higher in the case of Spatial Channel Model compared to the ITU channel model and this is due to the fact that the path loss in the case of SCM is lower than in the case of ITU channel model, which means that the interference in the ITU case is higher, so by putting the values of the channel gains according to SCM in equation (14) we get a larger value of alpha. 4.2.2.2
Validating the alpha expression:
In this section we will validate α expression given by equation (31), the idea is to calculate the value of α according to equations (31) and the optimum value of α according to equation (14), this process will be iterated 500 times, as explained before, so at the end we will have 2 vectors of α, each with 500 values, that we can compare and if the values in both vectors are close enough then equation (31) can be validated to give an optimal value for α. For this part we use a more realistic example where we drop 6 Pico-eNBs placed randomly in the cell, in addition 200 users are dropped randomly throughout the cell area. The simulation parameters are listed in Table 3. Cell area Macro-eNB position Pico-eNBs positions
500m x 500m X:0 Y:250 Random but keeping a minimum distance of 70 m from the Macro-eNB and the other Pico-eNBs. Random 40 W 1W 100
Users positions Macro-eNB transmitting power Pico-eNB transmitting power Number of drops Table 3
34
4.2.2.2.1
ITU channel model
We start by the ITU channel model. Figure 19 represents the PDFs of the results from both equations. And it shows that the PDFs are almost coinciding.
140 Optimal Alpha according to eq (14) Alpha according to eq (31) 120
100
80
60
40
20
0 0.5
0.52
0.54
0.56
0.58 0.6 Alpha
0.62
0.64
0.66
0.68
Figure 19: PDFs of Alpha according to eq (14) (Blue) and Alpha according to eq (31) (Red)
Figure 20 represents the plot of the alpha value in both cases for 500 iterations, each iteration is a 100 drops. 0.68 Optimal Alpha according to eq (14) Alpha according to eq (31)
0.66 0.64 0.62
Alpha
0.6 0.58 0.56 0.54 0.52 0.5 0.48
0
50
100
150
200
250 Itteration number
300
350
400
450
500
Figure 20: Plot of the 100 values of Alpha according to eq (14) (Blue) and Alpha according to eq (31) (Red)
35
These results show that the result in equation (31) is very close to the optimum value given by (14) when using the ITU channel model therefore it can be validated. 4.2.2.2.2
Spatial Channel Model (SCM)
In this part we will repeat the previous simulation but using the spatial channel model instead of the ITU channel model. Figure 21 represents the PDFs of the results from equations (14) and (31). And it shows that the PDFs are concentrated at very close values. 120 Optimal Alpha according to eq (14) Alpha according to eq (31) 100
80
60
40
20
0
0.58
0.6
0.62
0.64
0.66
0.68
0.7
0.72
0.74
Alpha
Figure 21: PDFs of Alpha according to eq (14) (Blue) and Alpha according to eq (31) (Red)
Figure 22 represents the plot of the alpha value in both cases for 500 iterations, each iteration is an average of 100 drop and as seen the values resulting of both equations are very close. 0.74 Optimal Alpha according to eq (14) Alpha according to eq (31)
0.72 0.7 0.68
Alpha
0.66 0.64 0.62 0.6 0.58 0.56 0.54
0
50
100
150
200
250 Itteration number
300
350
400
450
500
Figure 22: Plot of the 100 values of Alpha according to eq (14) (Blue) and Alpha according to eq (31) (Red)
36
These results show that the result in equation (31) is very close to the optimum value given by (14) when using the spatial channel model therefore it can be validated. Same as the previous case, the difference between the alpha values according to equation (31) and equation (14) is higher in the case of Spatial Channel Model compared to the ITU channel model and this is due to the fact that the path loss in the case of SCM is lower than in the case of ITU channel model, which means that the interference in the ITU case is higher, so by putting the values of the channel gains according to SCM in equation (14) we get a larger value of alpha. To summarize, it has been shown that the theoretical deductions in equations (29) and (31) can be validated to give optimal or suboptimal results for the ABS ratio using Monte-Carlo simulations. In the following section an example that is a special case of the general model used in the deduction will be introduced to elaborate more on the theoretical results.
4.2.3 Example to validate the general model results Through this example we will apply the previous theoretical model into a more practical scenario using specific path loss distributions, Pico-eNB distributions and nodes transmitting powers. 4.2.3.1
Defining the network topology and new parameters used in this example
In this subsection the network topology and different parameters used in the example are stated. a) We use a 1 cell network which contains 1 Macro-eNB and 2 groups of Pico-eNBs where group 1 are the Pico-eNBs closer to the Macro-eNB and group 2 are the Pico-eNBs farther from the Macro-eNB and we will start the example by 4 Pico-eNBs, 2 in each group, as shown in Figure 23 and then we will generalize the model for any number of Pico-eNBs (Npico). b) Same as the general model we will consider only the Macro-eNB user that has the lowest capacity and the range extension Pico-eNB user that has the lowest capacity. c)
We will assume, for simplicity, that the Pico user has the same path loss from all the other PicoeNBs. This can be the case when we have only 4 Pico-eNBs as they are equidistant, see Figure 23, but we will assume that this can be extended to any number of Pico-eNBs which is a strong assumption but it can be motivated due to the fact that we are not considering inter-cell interference in this example but in reality if we have a large number of Pico-eNBs (Npico), as we will assume later, then the Pico user placed at the cell border suffers from a larger inter-cell interference than the PicoeNB user placed in the cell center for instance, in that sense we can assume a close interference value for all the Pico-eNB users.
d) path loss from group 1 Pico-eNBs to Macro-eNB user path loss from group 2 Pico-eNBs to Macro-eNB user
hp1_ue hp2_ue
Table 4
37
e) Transmission powers for the different nodes. P1 P2 Ptotal_Pico
40 W 1W 4W
Table 5
f) Defining the distances between the different nodes. Distance between Macro-eNB and Macro-eNB user Distance between Pico-eNB and center Pico-eNB user Distance between Pico-eNB and range extension Pico-eNB user Distance between Macro-eNB and group 1 Pico-eNBs center user Distance between Macro-eNB and group 2 Pico-eNBs center user Distance between Pico-eNBs and other Pico-eNBs center user Distance between Pico-eNBs and other Pico-eNBs range extension user Distance between group 1 Pico-eNBs and Macro-eNB user Distance between group 2 Pico-eNBs and Macro-eNB user
40m 10m 20m 110m 150m 40m 30m 80m 120m
Table 6
Figure 23: Macro-eNB and Pico-eNBs in a cell
4.2.3.2
Calculating the values of C1 and C2 according to the example.
The path loss is calculated according to the urban Macro-eNB (UMa), for Macro-eNB users, and urban micro (UMi), for Pico-eNB users, which belong to the ITU channel model in [6] and they were explained in details in 4.2.2. After defining the different parameters for this example we calculate the values of C1 and C2 25according to this example to find a closed formula for . We start by C1 which is given by
25
C1 and C2 are the same as the ones deduced in section 4.2.1 but adapted to the scenario of the example.
38
‖
‖
).
(40)
Putting the value of the interference according to the specifications of the example, the becomes ‖
‖
‖
‖
‖
‖
.
(41)
And since we have 2 groups of Pico-eNBs as shown in Figure 23 we can assume that the number of PicoeNBs increases to form 2 circles for group 1 and 2 to maintain the distance from the Macro-eNB as shown in Figure 2426.
Figure 24: Increasing the number of picos in the cell
So the previous equation can be rewritten as follows ‖
‖
(‖
‖
‖ )
‖
.
(42)
Assuming that there is a specific constant budget for the total power transmitted by all the Pico-eNBs which can is denoted by so this equation can be re-written as follows ‖
‖
(‖
Now considering C2 which was equal to
‖ ‖ ‖
‖ )
‖ ‖ ‖
, but since
.
(43)
and
have specific
values in this example then they are no longer random variables and C2 can be expressed as
26
The reason for this distribution of Pico-eNBs is to simplify the equations by having only 2 channel gain values (one for the first group of Pico-eNBs and the other for the second group of Pico-eNBs).
39
‖
‖
‖
‖
.
(44)
Now calculating the values of C1 and C2 according to the path loss and transmitting powers stated above. C1 = 8.7025
C2 =8.6194
So So this example shows that the optimum value of
can be expressed according to equation (45).
(45)
4.2.4 Example to validate the general model results (without the assumption of Ptotal_Pico) In this example the previous example is repeated but without the constraint of Ptotal_pico, so we try to generalize the validation of the result for by removing the assumption that we have a budget for the Pico-eNBs transmitting power so we go back to equation [19] and rewrite it according to our example as follows ‖
‖
(‖
‖
‖
‖ )
.
(46)
And introducing the values for the power and path loss stated before. .
(47)
Also for the range extension user maximum capacity given by
‖
‖
‖
‖ (
)
.
(48)
It is simplified to (
)
.
So the ratio
(49) can be expressed in terms of
(
)
as follows
.
In order to understand this expression we plot it against Np and plot with it
(50) for comparison. 40
1 C-re-max/C-macro-max 1/Np
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0
10
20
30
40 50 60 number of picos (Np)
70
Figure 25: Comparison between
From this figure we see that expression for
is very close to
80
90
100
and
even for small Npico, so this validates the
that we deduced before which is given by eq. (30).
This result shows that is dependent on the total number of range extension users in a cell. But logically it should be dependent on the number of range extension users per Pico-eNB instead of the total number since the resources are reused for each Pico-eNB but this can be explained in the next figure where we plot the maximum range extension user capacity and also the Macro-eNB user maximum capacity against the number of Pico-eNBs, which means that we plot the user capacity while changing the number of PicoeNBs in the cell and see how the capacity behaves. Range extension user capacity against the number of picos range extension user capacity against the numner of picos
140
120
Capacity (bits/sec) capacity
100
80
60
40
20
0
2
3
4
5 6 number of picos (Np)
7
8
Figure 26: Plot of the range extension user capacity against the number of picos.
41
macro user maximum capacity against the number of picos 250
200
capacity
Capacity (bits/sec)
150
100
50
0
2
3
4
5 6 number of picos (Np)
7
8
Figure 27: Plot of the Macro user maximum capacity against the number of picos.
From Figure 26 we see that the range extension user capacity decreases when increasing the number of Pico-eNBs in the cell. This means that the range extension user capacity is interference limited and that the capacity depends very much on the interference coming from other Pico-eNBs which in turn depends on the number of Pico-eNBs. Also from Figure 27 it is obvious that the Macro-eNB user capacity is almost not affected by the number of Pico-eNBs or in other words the interference caused by the PicoeNBs to the Macro-eNB users is not significant. So this explains the dependence of the alpha calculations on the number of Pico-eNBs or more generally the total number of range extension users in the cell as will be shown in the next section. Now we validate the result by trying different user distributions for the same Pico-eNBs distribution (4 Pico-eNBs) and compare the alpha we get by simulation and that we get using equation (31). As shown in the following example: Considering case1, for example, we have 36 Macro-eNB users, 4 range extension users and 10 center Pico-eNB users.
42
45 macro user capacity center pico user capacity range extension user capacity
40 35
capacity
Capacity (bits/sec)
30 25 20 15 10 5 0
0
0.1
0.2
0.3
0.4
0.5 alpha
0.6
0.7
0.8
0.9
1
Figure 28
The optimized alpha according to simulations, see the intersection point in the figure, is 0.085 while calculating the alpha value according to the formula gives 0.1. (α =1/ (1+ (36/4)) =0.1). The rest of the results are listed in the following table Nrof_Macro-eNB_users
Nrof_re_users/Pico-eNB
Sim_alpha
Calculated_alpha(
36 32 28
1 2 3
0.085 0.176 0.27
0.1 0.2 0.3
24 20 16 12 8 4
4 5 6 7 8 9
0.363 0.462 0.563 0.671 0.78 0.887
0.4 0.5 0.6 0.7 0.8 0.9
)
Table 7
As seen from Table 7 the simulation results for
are very close to the value of
calculated from (31).
4.3 Summary from equation (31) is applicable in interference limited situations, i.e. situations where Pico-eNBs are causing interference to each other. Through section 4 a closed form expression for has been deduced and it has been tested to be valid in the case of the ITU channel model and the Spatial Channel Model (SCM), but it might not be the best solution in cases where there is no interference between Pico-eNBs. As a conclusion, from the last subsection, the value of
The equation in (31) will be tested more in the next section where simulations are conducted using more realistic channel models and bigger networks. 43
5. System simulation results This section is for the simulation results, we start by introducing the simulator used in this section which is the Raptor simulator, then listing the assumed simulation parameters used and finally presenting the simulations in different subsections.
5.1 The Raptor simulator All the simulations in this project are performed using a simulator called ‘Raptor’ which is a property of Ericsson. Raptor is an LTE-Advanced system simulator which means that it performs physical layer simulations. The simulator is divided into 3 parts: 1) Input parameter files: MATLAB files containing all the simulation parameters that will be used as input to the simulator. 2) Main simulator: the main body of the simulator which is developed in C++ and this simulator generates MATLAB result files. 3) Graphical interface: MATLAB graphical interface that processes the MATLAB result files to illustrate the results in the form of CDFs, bar charts and scatter plots as will be shown in the next section. I contributed mainly in creating my own input parameter files and optimizing the graphical interface to show more illustrative plots.
44
5.2 System simulation assumptions The criterion that we focus on optimizing is the cell-edge users throughput27 while keeping a fair level of average throughput. The simulation assumptions are listed in the following table28: Parameter Network topology Number of ue’s Number of Pico-eNBs Deployments Traffic model Range extension offset Downlink scheduling Carrier frequency Path loss mode Downlink link adaptation CRS interference modeling Total bandwidth Antenna tilting
Description 21 cell network (i.e. 7 three-sector sites) 30 ue’s per cell From 2 to 10 per cell depending on the tested scenario and all the PicoeNBs are outdoors and located at predefined locations. Configuration 129 and 4b30 [7] Full buffer 31 From 0 to 18 dB depending on the scenario Proportional fair scheduler [2] 2 GHz ITU Channel Model and Spatial Channel Model (SCM) Ideal link adaptation32 Assuming perfect CRS interference cancellation. 20 MHz According to TR36.819- 12 degrees for Macro-eNB, 0 degrees for PicoeNB Table 8
5.3
Simulation results
5.3.1 Who wins and who loses in terms of throughput in a heterogeneous network deployment? Who are the winners and losers in terms of throughput in a Macro-Pico heterogeneous deployment is a very crucial question, we mean by winners or losers the users who experience an increase or decrease of throughput when adding the Pico layer to the Macro layer. To answer this question we will compare the following 2 network deployments: 1) Macro-eNB only deployment: we only have 1 Macro-eNB per cell. 2) Macro-eNB + Pico-eNB deployment: we have 1 Macro-eNB and 4 Pico-eNBs per cell, with no range extension applied to the Pico-eNBs. 27
Cell edge users are the 5% worst users in terms of throughput. For the detailed simulation specifications check Annex A of [8] and [9]. 29 Configuration 1: All users are distributed uniformly in the cell 30 Configuration 4b: 2/3 of the users are distributed in hotspots concentrated around the Pico-eNBs and the rest 1/3 are distributed uniformly in the cell. 31 Full buffer mode means that the Nodes are transmitting all the time to their users as if there is always data to transfer 32 In this mode we assume that the transmitting node has perfect knowledge of the channel in the downlink, this is used to avoid having 2 sets of CQI reports for ABS and non-ABS. 28
45
Figure 29 represents a comparison between the ‘Macro only ’case users throughput (right) and the ‘Macro+Pico’ case users throughput (left). The colors represent the throughput where blue is the minimum and red is the maximum. It is obvious that in the ‘Macro+Pico’ case the throughput is much better which is mainly due to the cell splitting gain.
Throughput Mbps
Throughput Mbps
Figure 29: Throughput comparison between case 1 and case 2 where blue is the minimum and red is the maximum
Figure 30 represents a scatter plot having as x-axis the throughput of the users in the Macro-eNB + PicoeNB case and as y-axis the users throughput in the Macro-eNB only scenario. It is obvious that most of the users have a throughput increase when adding the Pico-eNBs, except some low throughput (cell edge) users who lose from the addition of Pico-eNBs. All the users comparison
(Macro only) users throughput (bps/Hz) No-ABS case ks((bps/Hz)throughput (bps/Hz)
6
5
4
3
2
1
0
0
1
2
3
4
5
6
7
case (Macro+Pico) usersABS throughput (bps/Hz)
Figure 30: Case 1 and case2 users throughput comparison
46
Figure 31 represents the users who experience a decrease of throughput, the losers, when adding the Pico layer. The majority of the losers are Macro-eNB users (the red ones). And as seen from Figure 30 these users are all cell edge users, as they have the lowest throughput, which means that they have low signal to interference and noise ratio (SINR) channel with the Macro-eNB and this makes them more prone to interference coming from the Pico-eNBs. So although the interference from the Pico-eNBs is small it can still affect low SINR Macro-eNB users. throughput losers Macro:Red 600
14
Pico:Blue RE: Green
8
13
400
7
15
9
200 17
0
2
5
16
1
18
4
3
6
-200 20
11
-400
19
10
21
-600 -800
-600
-400
12
-200
0
200
400
600
800
Figure 31: Illustration of the users experiencing a decrease of throughput after adding the Pico-eNB layer, Macro-eNB users (red) and Pico-eNB users (blue)
As a conclusion, adding the Pico-eNB layer increases the throughput for the majority of users except the Macro-eNB cell edge users which are affected by the interference coming from the Pico-eNBs. 5.3.1.1
Same example with the addition of an 8 dB range extension to the Pico-eNBs in the Macro+Pico case
Here we are comparing the following 2 scenarios:
1) Macro-eNB only deployment: we only have 1 Macro-eNB per cell 2) Macro-eNB + Pico-eNB + range extension deployment: we have 1 Macro-eNB and 4 Pico-eNBs per cell and applying an 8 dB range extension to the Pico-eNBs. Figure 32 represents the users that are losing throughput when adding the Pico layer with range extension. As seen, most of the losers are range extension users, which means that these users suffer from a high interference from the Macro-eNBs. This shows the importance of using almost blank subframes (ABS) to protect the range extension users from the high interference coming from the Macro-eNBs.
47
throughput losers Macro:Red 600
14
Pico:Blue RE: Green
8
13
400
7
15
9
200 17
0
2
5
16
1
18
3
4
6
-200 20
11
-400
19
10
21
-600 -800
-600
-400
12
-200
0
200
400
600
800
Figure 32: Illustration of the users losing throughput after adding the Pico-eNB layer with range extension, Macro-eNB users (red), Pico-eNB users (blue) and range extension users (green)
5.3.2 Simulations demonstrating the benefits of using ABS Through this example we will compare 2 scenarios:
1) Macro-eNB + Pico-eNB + range extension deployment 2) Macro-eNB + Pico-eNB + range extension + ABS of ratio (0.3), from equation (31), deployment. Figure 33, same as before, is showing the users whose throughput has decreased due to the use of an ABS ratio of 0.3. As seen most of these users are Macro-eNB users (red ones). This can be explained by the fact that after applying ABS the Macro-eNB users are only allowed to use 70% of the available subframes which, in turn, decreases the Macro-eNB users throughput. throughput losers Macro:Red 600
14
Pico:Blue RE: Green
8
13
400
7
15
9
200 17
0
2
5
16
1
18
3
4
6
-200 20
11
-400
19
10
21
-600 -800
-600
-400
12
-200
0
200
400
600
800
Figure 33: Illustration of the users losing throughput after applying ABS, Macro-eNB users (red), Pico-eNB users (blue) and range extension users (green)
48
Also from the throughput comparison in Figure 34 it can be seen that the majority, or at least more than half, of the users have a better throughput when applying ABS. All the users comparison
(Non-ABS case) users throughput (bps/Hz) No-ABS case ks((bps/Hz)throughput (bps/Hz)
6
5
4
3
2
1
0
0
1
2
3 ABS case
4
5
6
(ABS case) users throughput (bps/Hz)
Figure 34: Users throughput comparison between the ABS case and Non-ABS case
To have more insight on the results in Figure 34 we will divide it into 3 figures representing the MacroeNB users, center Pico-eNB users and range extension users as follows. Figure 35 shows that all the Macro-eNB users have a constant decrease of throughput when applying ABS, this decrease factor is equal to 0.3 (the ABS ratio used), which is logical because after applying the ABS the Macro-eNB users are not allowed to transmit during 30% of the subframes which is translated into a constant rate of throughput decrease which explains the straight line. macro users comparison 1.8
(Non-ABS case) users throughput (bps/Hz) No-ABS case ks((bps/Hz)throughput (bps/Hz)
1.6 1.4 1.2 1
0.8 0.6 0.4 0.2 0
0
0.2
0.4
0.6 0.8 ABS case
1
1.2
1.4
(ABS case) users throughput (bps/Hz)
Figure 35: Macro-eNB users throughput comparison between the ABS case and Non-ABS case
49
Figure 36 shows that most of the center Pico-eNB users have a better throughput after applying the ABS which can be explained by: 1) range extension users are scheduled only on 30% of subframes allowing the center Pico-eNB users to be scheduled more often, as they are using the same resources, so instead of the range extension users being scheduled in all the subframes they only use 30% of it allowing more chances to the center Pico-eNB users. 2) Macro-eNB users are scheduled only on 70% of the subframes so they cause less interference to the Pico-eNB users allowing them to have a better throughput. pico users comparison
(Non-ABS case) users throughput (bps/Hz) No-ABS case ks((bps/Hz)throughput (bps/Hz)
6
5
4
3
2
1
0
0
1
2
3 ABS case
4
5
6
(ABS case) users throughput (bps/Hz)
Figure 36: Center-Pico-eNB users throughput comparison between the ABS case and Non-ABS case
Finally Figure 37 shows the range extension users where almost all of them have an increase of throughput when using ABS, which is explained by the fact that they are partially immune to the high interference caused by the Macro-eNBs before using ABS, so they have better SINR and better throughput. It can also be seen that the increase of the range extension users throughput is higher than the decrease of the Macro-eNB users throughput and this can be explained by the fact that the resources are reused by every Pico-eNB’s users while in the case of Macro-eNB users it is shared by all the Macro-eNB users, so the reuse rate is higher when the resources are exploited by range extension users. The reason for not having a straight line for the range extension users, similar to the Macro-eNB users is that the ABS are shared between the range extension users and the center Pico-eNB users which means that there is no fixed gain.
50
re users comparison 1
(Non-ABS case) users throughput (bps/Hz) No-ABS case ks((bps/Hz)throughput (bps/Hz)
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0
0.1
0.2
0.3
0.4
0.5 0.6 ABS case
0.7
0.8
0.9
1
(ABS case) users throughput (bps/Hz)
Figure 37: Range extension Pico-eNB users throughput comparison between the ABS case and Non-ABS case
Figure 38 shows the throughput CDF for both cases and as we see the ABS case has a slightly better performance along the whole curve. Empirical CDF 1 ABS case No-ABS case
0.9 0.8 0.7
F(x)
0.6 0.5 0.4 0.3 0.2 0.1 0
0
1
2
3 x
4
5
6
Figure 38: Throughput (bps/Hz) CDF of the ABS case and No-ABS case
Figure 39 illustrates the normalized throughput of the cell edge users in the four cases (Macro only, Macro + Pico, Macro + Pico + RE and Macro + Pico + RE + ABS), where the percentage represents the difference of each case with the (Macro only) case. We see that the fourth case (Macro + Pico + RE + ABS) has the best cell edge throughput. Also the (Macro + Pico) case has a higher cell edge throughput than the (Macro+Pico+RE) case which shows that using range extension without ABS is not effective as range extension users suffer from a high interference level from the Macro-eNB.
51
Normalized cell-edge user throughput bps/Hz/user 0.12
0.1
0.08
0.06
0.04
0.02
0
0.025 0%
0.08 220%
0.064 156%
0.106 324%
Macro only
Macro+Pico
Macro+Pico+RE
Macro+Pico+RE+ABS
Figure 39: Cell edge throughput for the 4 cases.
Finally Figure 40 shows the normalized throughput per user for the 4 cases it can be seen that the 3 cases having the Pico-eNB layer have almost equal throughput while the Macro-eNB only case has a very low normal throughput. Normalized user throughput bps/Hz/user 0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1 0.088
0.43
0.401
0.424
0%
388.636%
355.682%
381.818%
Macro only
Macro+Pico
Macro+Pico+RE
Macro+Pico+RE+ABS
0.05
0
Figure 40: Normalized throughput per user for the 4 cases.
52
5.3.3 Simulations validating the ABS ratio formula for different users and Pico-eNBs distributions. In this section we will present simulations validating the ABS ratio formula given by equation (31) (
) that was deduced in section 4.2. The strategy will be to test several Pico-eNBs and
users distributions with different range extension values and check if the formula holds. The results from equation (31) will be compared to the results from the same equation but using the maximum number of range extension users per cell ( ) instead of the total number of range extension users ( per cell. The new equation is given by: (51) We will focus on the ITU channel model but at the end of the section some results for the Spatial Channel Model (SCM) will be shown to validate the theory for this model. As mentioned before the criterion to be optimized is the normalized throughput of the cell edge users and the average throughput per user, in general the formula gives the optimal or the suboptimal solution which is acceptable as well as will be seen in the results. Each simulation consists of 5 drops, 2 seconds in total, and this is done to have enough information in order to get reliable results, so since we have 30 users per cell per drop then 1 drop will consist of 630 users and each simulation will consist of 3150 users. For each simulation the following 11 cases will be compared 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
No range extension No ABS ABS=0.1 ABS=0.2 ABS=0.3 ABS=0.4 ABS=0.5 ABS=0.6 ABS=0.7 ABS=0.8 ABS=0.9
53
5.3.3.1
Results using 4 Pico-eNBs and configuration4b with different range extension values (ITU channel model)
5.3.3.1.1
Range extension: 4dB
The total number of Macro-eNB users, for all the drops, is 712 and the total number of range extension users, for all the drops, is 16333. So calculating the optimum ABS ratio according to eq. (31) gives which can be rounded to . Figure 41 represents the normalized cell edge users throughput for the different ABS configurations and the percentage on each bar represents the difference between each case and the No-RE case in percentage, As can be seen the best cell edge throughput is given for ABS ratio=0.2 which is 16.8% higher than the no range extension case. Calculating α using equation (51) gives , which has lower cell edge users throughput (-16%) than the value calculated using equation (31). Normalized cell-edge user throughput bps/Hz/user 0.12
0.1
0.08
0.06
0.04
0.02
0
0.086 0%
0.086 0.62%
0.086 0.49%
0.1 16.852%
0.099 15.895%
0.092 7.462%
0.066 -23.312%
0.066 -22.687%
0.045 -47.155%
0.03 -64.992%
0.015 -82.669%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
Figure 41: Normalized cell edge users throughput Normalized user throughput bps/Hz/user 0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.536
0.522
0.537
0.536
0.537
0.539
0.556
0.543
0.565
0.573
0.58
0%
-2.573%
0.207%
0.011%
0.126%
0.466%
3.744%
1.25%
5.327%
6.817%
8.211%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
Figure 42: Normalized user throughput 33
The statistics are calculated for all the users in the 21 cell cluster, as a sort of averaging since all the cells have the same number of users, but it can be calculated per cell as well.
54
Figure 42 represents the normalized user throughput and it shows minor changes between all the cases except the last 3 cases where the difference with the no range extension case is between 5.7% and 8.2% but these cases have very low cell edge throughput. This is explained by the fact that we have a low number of range extension users and the ABS ratio is very high (70% to 90%) so this allows the range extension users to be scheduled more frequently and to have a very high throughput, which explains the high normal throughput, while the number of Macro-eNB users is much higher and they are only allowed to be scheduled in (10% to 30%) of the subframes so they have a very low throughput and most of the cell edge users are Macro-eNB users, which explains the low cell edge throughput. Figure 43 shows the cell edge users in the ABS=90% case and it shows that all the cell edge users are Macro-eNB users. Cell Edge users. Macro: Blue, Pico: green, RE: red
Subframe distribution 11:Cell Edge Users. Macro:Blue,Pico:green,RE:red 600
14
8
13
400
7
15
9
200 17
0
2
5
16
1
18
3
4
6
-200 20
11
-400
19
10
21
-600 -800
-600
-400
12
-200
0
200
400
600
800
Figure 43: Cell edge users distributed among the 3 groups (Macro-eNB, center Pico-eNB and range extension) depending on the color.
Figure 44 represents the throughput CDF for the 11 cases, it can be seen that the ABS=20% case has the highest throughput for the first 10% users and maintaining a moderate throughput for the rest of the users while for the ABS=90% case it has the lowest throughput for the first 30% users, which are mostly Macro-eNB users, while it has the highest throughput for the 40% to 95% users and since the main criteria to optimize is the cell edge throughput it is very obvious that the optimum ABS value is 20% as given by the formula in (31).
55
Normalised User Throughput 100 No range extension No ABS ABS: 10% ABS: 20% ABS: 30% ABS: 40% ABS: 50% ABS: 60% ABS: 70% ABS: 80% ABS: 90%
90 80
C.D.F. [%]
70 60 50 40 30 20 10 0
0
0.5
1
1.5 Normalised User Throughput [bps/Hz]
2
2.5
3
Figure 44: throughput CDFs
5.3.3.1.2
Range extension: 16dB
The total number of Macro-eNB users, for all the drops, is 243 and the total number of range extension users, for all the drops, is 632. So calculating the optimum ABS ratio according to eq. (31) gives which can be rounded to . Figure 45 represents the normalized cell edge users throughput for the different ABS configurations, as can be seen the best cell edge throughput is given for ABS percentage=70% which is 26.29% higher than the no range extension case. Calculating α using equation (51) gives , which has lower cell edge users throughput (-18%) than the value calculated using equation (31). Normalized cell-edge user throughput bps/Hz/user 0.12
0.1
0.08
0.06
0.04
0.02
0
0.086 0%
0.02 -76.863%
0.035 -59.198%
0.066 -22.659%
0.087 0.963%
0.097 13.724%
0.093 8.461%
0.107 25.213%
0.108 26.29%
0.103 20.074%
0.074 -13.476%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
Figure 45: Normalized cell edge users throughput
56
Figure 46 represents the normalized user throughput and it shows that the ABS=70% case has a relatively high normalized throughput which is 3.2% higher that the no range extension case. Normalized user throughput bps/Hz/user 0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.536
0.477
0.553
0.539
0.53
0.524
0.551
0.52
0.554
0.559
0.565
0%
-10.997%
3.215%
0.509%
-1.21%
-2.203%
2.768%
-2.95%
3.283%
4.289%
5.311%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
Figure 46: Normalized user throughput
Figure 47 represents the throughput CDF for all the cases and we can see that the ABS=70% case maintains a very good throughput for almost 75% of the users which clearly shows that this case is the optimal one. Normalised User Throughput 100 No range extension No ABS ABS: 10% ABS: 20% ABS: 30% ABS: 40% ABS: 50% ABS: 60% ABS: 70% ABS: 80% ABS: 90%
90
80
70
C.D.F. [%]
60
50
40
30
20
10
0
0
0.5
1
1.5 2 Normalised User Throughput [bps/Hz]
2.5
3
3.5
Figure 47: Throughput CDF for the 11 cases
5.3.3.1.3
Results for all the range extension values that have been tested
Here we will compare 3 different ABS values and the throughput resulting from them, the ABS values are the ABS ratio according to equation (31), the ABS ratio according to equation (51) and the optimal ABS ratio according to simulations for 6 different values of range extension. As seen from Figure 48, the 57
values of α according to equation (31) (blue line) and the optimal α according to simulations (green line) are almost the same, while the α values according to equation (51) (orange line) are quite far from the optimal values. Figure 49 represents the throughput values for the 3 results of α, same as Figure 48 but the y-axis represents the throughput not the ABS ratio, and it can be seen that the throughput resulting from the values of α according to equation (31) (blue line) and the optimal α according to simulations (green line) are almost the same while the throughput resulting from the α values according to equation (51) (orange line) is quite far from the optimal values. 0.8 Optimal ABS ratio according to the formula(total nrof RE ues) 0.7
Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
0.6
ABS ratio
0.5 0.4 0.3 0.2 0.1 0 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 48: Results for the ABS ratio for the 3 cases using 6 different range extension values
0.11
0.105
Throughput (Mbps)
0.1
0.095
0.09
0.085
0.08
Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
0.075 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 49: Throughput results for the 3 cases for 6 range extension values
58
5.3.3.2
Results using 4 Pico-eNBs and configuration 1 with different range extension values (ITU channel model)
Doing the same comparison as the previous subsection but for configuration 1 instead of configuration 4b. 0.7 Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
0.6
ABS ratio
0.5
0.4
0.3
0.2
0.1
0 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16d B
RE:18dB
Figure 50: Results for the ABS ratio in both cases for 6 range extension values
0.08
0.075
Throughput (Mbps)
0.07
0.065
0.06
0.055 Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues) 0.05 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 51: Throughput results for the 3 cases for 6 range extension values
59
As seen from Figure 50 and Figure 51, the α values and the resulting throughput according to equation (51) are far from the optimal values. The α values and the resulting throughput according to equation (31) are very close to the optimal α values and the resulting throughput according to simulations except for the range extension values 12 dB and 18 dB where there is an 0.1 difference between the optimum alpha value and the value calculated by equation (31) which is translated to a slight difference in the resulting throughput and in that case the result from equation (31) is considered as a suboptimal solution as it has the closest value to the optimal solution. Taking as example for that the result of the range extension 18dB in Figure 52 which shows the cell edge users throughput, the optimum value is the one for the ABS ratio 0.6 and the theoretical, according to equation (31), value is for the ABS ratio 0.7 and it can be seen that this value is the closest one to the optimal one and can be considered as a suboptimal solution, also looking at the normalized throughout in Figure 53 we see that the normal throughput of the ABS ratio 0.7 has a higher value than the one of the ABS ratio 0.6 which can compensate for the lower cell edge throughput. These results show that this the ABS ratio 0.7 can be considered as a suboptimal solution. Normalized cell-edge user throughput bps/Hz/user 0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0.056
0.013
0.023
0.044
0.06
0.07
0.065
0.08
0.076
0.067
0.042
0%
-76.425%
-58.814%
-20.782%
7.049%
24.692%
15.756%
42.018%
35.451%
18.958%
-24.223%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
Figure 52: Normalized cell edge users throughput
60
Normalized user throughput bps/Hz/user 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15
0.43
0.353
0.444
0.431
0.42
0.414
0.441
0.41
0.442
0.447
0.454
0.1
0%
-17.888%
3.193%
0.095%
-2.264%
-3.745%
2.481%
-4.662%
2.733%
4.015%
5.49%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
0.05 0
Figure 53: Normalized user throughput
5.3.3.3
Results using 2 Pico-eNBs and configuration 4b with different range extension values (ITU channel model)
In this section the number of Pico-eNBs will be changed to 2 Pico-eNBs using configuration 4b and the same check will be done to verify the consistency of the formula. The comparison is done for 6 different range extension values. Figure 54 and Figure 55 show that the theoretical results, equation (31), are very close to the optimal ones, while the results of equation (51) give worse results than those of equation (31). 0.7 Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
0.6
ABS ratio
0.5
0.4
0.3
0.2
0.1
0 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 54: Results for the ABS ratio in both cases for 6 range extension values
61
0.085
Throughput (Mbps)
0.08
0.075
0.07
0.065 Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues) 0.06 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 55: Throughput results for the 3 cases for 6 range extension values
5.3.3.4
Results using 2 Pico-eNBs and configuration 1 with different range extension values (ITU channel model)
Here the same check as the previous section is done but using configuration 1 for the users distribution. Figure 56 and Figure 57 show that the ABS ratio and the resulting throughput according to equation (31) coincide very much with the optimal ones 0.7
0.6
Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
ABS ratio
0.5
0.4
0.3
0.2
0.1
0 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 56: Results for the ABS ratio in both cases for 6 range extension values
62
0.056
0.054
Throughput (Mbps)
0.052
0.05
0.048
0.046
0.044 Optimal ABS ratio according to the formula(total nrof RE ues)
0.042
Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
0.04 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 57: Throughput results for the 3 cases for 6 range extension values
5.3.3.5
Results using 10 Pico-eNBs and configuration4b with different range extension values (ITU channel model)
In this section the number of Pico-eNBs will be changed to 10 Pico-eNBs using configuration 4b and the same check will be done to verify the consistency of the formula. The comparison is done for 6 different range extension values. Figure 58 and Figure 59 illustrate that the theoretical results for the ABS ratio and the throughput, equation (31), for the 6 range extension values give very close values to the optimal ones, while the results according to equation (51) give worse results than those of equation (31). 0.9 0.8
Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
0.7
ABS ratio
0.6 0.5 0.4 0.3 0.2 0.1 0 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 58: Results for the ABS ratio in both cases for 6 range extension values
63
0.16
0.15
Throughput (Mbps)
0.14
0.13
0.12
0.11 Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues) 0.1 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 59: Throughput results for the 3 cases for 6 range extension values
5.3.3.6
Results using 10 Pico-eNBs and configuration1 with different range extension values (ITU channel model)
Here the same check as the previous section is done but using configuration 1 for the users distribution and increasing the number of Pico-eNBs to 10. Figure 60 and Figure 61 show that the ABS ratio values and the resulting throughput according to equation (31) coincide with the optimal ones except for 2 results, corresponding to range extension 4 dB and 18 dB, that coincide with the suboptimal ones. Figure 62 illustrates the cell edge throughput for the 4 dB range extension and it shows that the suboptimal solution (ABS= 20%) is very close to the optimal solution (ABS=30%). It is worth mentioning that the α values resulting from equation (51) get further from the optimal solution as the number of Pico-eNBs is increased.
64
0.8 Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
0.7
0.6
ABS ratio
0.5
0.4
0.3
0.2
0.1
0 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 60: Results for the ABS ratio in both cases for 6 range extension values 0.14
0.13
Throughput (Mbps)
0.12
0.11
0.1
0.09
0.08
RE:4dB
Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues) RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 61: Throughput results for the 3 cases for 6 range extension values
65
Normalized cell-edge user throughput bps/Hz/user 0.12
0.1
0.08
0.06
0.04
0.02
0
0.098 0%
0.11 0.088 11.638% -10.244%
No-RE
No-ABS
0.111 13.355%
0.115 17.108%
0.109 0.087 0.082 11.067% -11.372% -16.308%
0.059 -39.52%
0.04 0.02 -59.281% -79.821%
ABS:10% ABS:20% ABS:30% ABS:40% ABS:50% ABS:60% ABS:70% ABS:80% ABS:90%
Figure 62: Normalized cell edge users throughput
5.3.3.7
Results using 4 Pico-eNBs and configuration4b with different range extension values (Spatial channel model) This test is the same as the one in section 5.3.3.1 but using the Spatial Channel Model (SCM) instead of the ITU channel model. Figure 63 and Figure 64 show that the ABS ratio values and the resulting throughput according to equation (31) give the optimal ABS ratio in all cases except in the range extension 12 dB and 16 dB where it gives the suboptimal solution but still the results are better than the ones resulting from equation (51). 0.8 Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
0.7 0.6
ABS ratio
0.5 0.4 0.3 0.2 0.1 0 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 63: Results for the ABS ratio in both cases for 6 range extension values
66
0.075
0.07
Optimal ABS ratio according to the formula(total nrof RE ues) Optimal ABS ratio according to simulations Optimal ABS ratio according to the formula(Max. nrof RE ues)
Throughput (Mbps)
0.065
0.06
0.055
0.05
0.045
0.04 RE:4dB
RE:6dB
RE:8dB RE:12dB Range extension value
RE:16dB
RE:18dB
Figure 64: Throughput results for the 3 cases for 6 range extension values
Figure 65 illustrates the cell edge users normalized throughput for the range extension=16 dB case and it shows that the ABS ratio given by the formula (ABS=0.7) is the closest to the optimal value (ABS =0.6) . Normalized cell-edge user throughput bps/Hz/user 0.07
0.06
0.05
0.04
0.03
0.02 0.046 0.01
0
0%
No-RE
0.019
0.015
0.03
-57.451% -67.445% -34.041%
No-ABS
0.044
0.056
0.057
0.068
0.061
0.05
0.028
-3.43%
22.398%
24.691%
48.066%
33.903%
10.035%
-38.392%
ABS:10% ABS:20% ABS:30% ABS:40% ABS:50% ABS:60% ABS:70% ABS:80% ABS:90%
Figure 65: Normalized cell edge users throughput
Figure 53 shows that the normal throughput of the ABS ratio 0.7 has a higher value than that of the ABS ratio 0.6 which can compensate for the lower cell edge throughput. These results show that the ABS ratio 0.7 can be considered as a suboptimal solution. 67
Normalized user throughput bps/Hz/user 0.4
0.35
0.3
0.25
0.2
0.15
0.364
0.302
0.376
0.363
0.351
0.342
0.345
0.33
0.351
0.358
0.365
0.1
0%
-17.062%
3.169%
-0.376%
-3.531%
-6.108%
-5.279%
-9.459%
-3.568%
-1.77%
0.296%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
0.05
0
Figure 66: Normalized user throughput
5.3.3.8
Summary
The results in section 5.3.3 have shown that the ABS ratio resulting from equation (31) give the optimal or sub optimal value of the ABS ratio in terms of normalized cell edge users throughput. It has been tested for different users and Pico-eNBs distributions and also different channel models. It has also been shown that the ABS ratio resulting from equation (31) gives much better results than the ABS ratio resulting from equation (51) especially for a big number of Pico-eNBs.
5.3.4 Does having a high range extension give a better performance? In this section we will study the benefits of having a high range extension value. We will consider a case having (4 Pico-eNBs, configuration 4b and 18 dB range extension). If we consider the optimized value of the ABS ratio according to equation (31) (ABS=70%) we see that it has a high cell edge users normalized throughput in Figure 67 and a high normalized throughput as well in Figure 68.
68
Normalized cell-edge user throughput bps/Hz/user 0.12
0.1
0.08
0.06
0.04
0.02
0
0.086 0%
0.017 -80.279%
0.032 -62.369%
0.061 -28.277%
0.081 -5.436%
0.092 7.311%
0.091 6.073%
0.103 20.211%
0.109 27.621%
0.107 24.626%
0.085 -0.278%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
Figure 67: Normalized cell edge users throughput Normalized user throughput bps/Hz/user 0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.536
0.476
0.557
0.542
0.532
0.526
0.551
0.521
0.553
0.558
0.564
0%
-11.255%
3.885%
1.04%
-0.799%
-1.854%
2.8%
-2.79%
3.132%
4.15%
5.098%
No-RE
No-ABS
ABS:10%
ABS:20%
ABS:30%
ABS:40%
ABS:50%
ABS:60%
ABS:70%
ABS:80%
ABS:90%
Figure 68: Normalized user throughput
Also considering the throughput CDF in Figure 69 that shows the ABS=70% case and the following 2 cases having the best throughput for the first 70% of the users and if we compare that to the case when we used a range extension of 4 dB in section 5.3.3.1 where the optimized value of of the ABS ratio (20%) had the highest throughput for only 40% of the users in Figure 44.
69
Normalised User Throughput 100 No range extension No ABS ABS: 10% ABS: 20% ABS: 30% ABS: 40% ABS: 50% ABS: 60% ABS: 70% ABS: 80% ABS: 90%
90 80
C.D.F. [%]
70 60 50 40 30 20 10 0
0
0.5
1
1.5
2 2.5 Normalised User Throughput [bps/Hz]
3
3.5
4
Figure 69: Throughput CDF for the 11 cases.
This shows that if we compare the optimum ABS ratio cases for different range extension values we find that having a higher range extension value gives higher normalized throughput value. This can be be due to 2 reasons: 1) ABS are reused by every Pico-eNB in the cell to serve its range extension users instead of being used only by the Macro-eNB, in other words a non-ABS is used by the Macro-eNB to serve its users but an ABS is used by each and every Pico-eNB in the cell to serve its range extension users, along with the center pico users, which means that the reuse of this subframe is higher which in turn increase the range extension users throughput. 2) Having a high ABS ratio means that the Macro-eNB is only allowed to transmit in a small number of subframes, which means that the interference that the Macro-eNB imposes on the center Pico-eNB users is reduced allowing these users to be served with better conditions and to have higher throughput, which explains the high normalized throughput.
70
6. Conclusions In this thesis work a study about htereogeneous networks has been presented with a special focus on the optimization of the Almost Blank Subframes (ABS) allocation ratio in heterogeneous LTE-Advanced networks using range extension. The optimization criterion was the cell edge users normalized throughput while keeping a moderate level of normal user throughput. A closed form expression to calculate the optimal or sub-optimal ABS allocation ratio has been deduced theoretically, this formula depends on the ratio of the number of Macro-eNB users to the total number of Pico-eNB range extension users in a cell or in a complete network and it has been validated using a simple system simulator performing Monte Carlo simulations followed by an example that explains and validates the deduction. Also system simulations using the Raptor simulator have been performed to validate the formula using different channel models, users distributions, Pico-eNBs numbers and range extension values. All the simulations were conducted in the full buffer mode. This formula has been proven to work in interference limited scenarios (ITU channel model, SCM) but it will not be optimal for interference free scenarios. Other general conclusions about HetNets have been deduced from simulations such as the users experiencing an increase or decrease of throughput in a HetNet scenario where it has been shown that most of the users are winners except for a few users attached to the Macro-eNB who are affected by interference from the Pico-eNBs. These users are mostly cell edge users having bad channel conditions which explains being affected by the small interference that the Pico-eNB imposes on them. Also it has been shown by simulations that using range extension without ABS is not beneficiary as not using range extension gives better results due to the high interference that the Macro-eNB imposes on the range extension users. Finally the use of a high range extension value has shown to give better results than the use of low range extension value, using the optimal ABS ratio in both cases.
7. Future work The periodicity of applying the formula is still to be tested and by periodicity we mean how often should the formula be applied in order to optimize the performance. CRS interference cancellation is an important challenge in the use of Almost Blank Subframes as CRS is considered to be a big source of interference in a HetNet scenario. Some solutions are being studied to combat CRS interference such as successive interference cancellation or the puncturing of CRS resource elements, these solutions are still being tested and will be included in the Further Enhanced Inter-Cell Interference Coordination (FEICIC) in LTE release 11.
71
8. List of Acronyms 3GPP AWGN BCH BSC CA CP CQI CRC CSI DL eNB E-UTRAN FDD GERAN GPRS HSPA ICIC IFFT IMT-Advanced ITU LTE MBMS MIMO OFDMA PBCH PCH PRB PSS QAM QPSK RE RR RS SIB SIC SINR SSS TDD UE UTRAN X2
Third Generation Partnership Project Additive White Gaussian Noise Broadcast Channel Base Station Controller Carrier Aggregation Cyclic Prefix Channel Quality Indicator Cyclic Redundancy Check Channel State Information Downlink E-UTRAN NodeB Evolved UTRAN Frequency Division Duplex GSM/EDGE Radio Access Network General Packet Radio Services High-Speed Packet Access Inter-Cell Interference Cancellation Inverse Fast Fourier Transform International Mobile Telecommunications Advanced International Telecommunications Union Long Term Evolution Multimedia Boradcast Multicast Services Multiple-Input Multiple-Output Orthogonal Frequency Division Multiple Access Physical Broadcast Channel Paging Channel Physical Resource Block Primary Synchronization Channel Quadrature Amplitude Modulation Quadrature Phase-Shift Keying Resource Element Round Robin Reference Symbol System Information Block Successive Interference Cancellation Signal-to-Interference-and-Noise Ratio Secondary Synchronization Signal Time Division Duplex User Equipment, the 3GPP name for the mobile terminal Universal Terrestrial Radio Access Network The interface between eNodeBs. 72
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[2]
Erik Dahlman, Stefan Parkvall & Johan Skold, ‘‘4G: LTE/LTE-advanced for Mobile Broadband’’, Academic Press, 2011.
[3]
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[4]
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[5]
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[6]
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[8]
3GPP, 3rd generation partnership project; Technical specification group radio access network; further enhancements for E-UTRA physical layer aspects (Release 9), 3GPP TR 36.814, March 2010.
[9]
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[10]
3GPP, 3rd generation partnership project; Technical specification group radio access network; spatial channel model for multiple input multiple output (MIMO) simulations. Release 6, 3GPP TR25.996 V6.1.0 (2003-09).
[11]
Anderson, H.L. “Metropolis, Monte Carlo and the MANIAC”. Los Almos Science 14: 96-108, 1986.
[12]
3GPP, 3rd generation partnership project; Technical specification group radio access network; ''Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall Description'', 3GPP TS36.300, September 2008.
[13]
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3GPP, 3rd generation partnership project; Technical specification group radio access network; ‘‘Way forward on time-domain extension of Rel 8/9 backhaul-based ICIC’’, R1-105779, October 2010.
[16]
3GPP, 3rd generation partnership project; Technical specification group radio access network; ‘‘Techniques to help optimizing the CRE gains’’, R1-106383, Qualcomm Incorporated, USA, November 2010.
[17]
3GPP, 3rd generation partnership project; Technical specification group radio access network; ‘‘PDSCH performance evaluation for FeICIC’’, R1-113085, Samsung, China, October2011.
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