design and performance evaluation of radio resource

DESIGN AND PERFORMANCE EVALUATION OF

RADIO RESOURCE MANAGEMENT

IN

OFDMA NETWORKS

Javad Zolfaghari

Institute for Theoretical Information TechnologyRWTH Aachen University

DESIGN AND PERFORMANCE EVALUATION OF

RADIO RESOURCE MANAGEMENT

IN

OFDMA NETWORKS

Javad Zolfaghari

A thesis submitted to theDepartment of Signal Theory and Communications,Technical University of Catalonia-Barcelona Tech,

reviewed by Dr. Michael Reyer and Dr. Anna Umbert on 14th December 2013

in partial fulfillment of the requirements for the degree of master in Information andCommunications TechnologyMaster of Science inElectrical Engineering, Information and Communications Technology

Institute for Theoretical Information TechnologyRWTH Aachen University

I assure the single handed composition of this master’s thesis only supported by declaredresources. Information derived from those resources has been acknowledged in the textand references are given in the bibliography.

Aachen, 14th December 2013

Abstract

Inter-cell interference is of great interest and one of the essential issues for wireless oper-ators who want to provide full coverage within their service area and guarantee a certainQuality of Service (QoS) to all users. In this thesis the resource allocation problem inan OFDMA based multi-cell network is formulated. Since no algorithm with polynomialrunning time exists due to non-linearity and combinatorial nature of underlying problemwe adopted a heuristic approach with low computational cost and acceptable performanceloss. We address specifically sharing available radio resources among users in terms ofbandwidth allocation in order to suppress inter-cell interference. Besides, cell assignmentand power allocation is foreseen. Performance of proposed schemes were evaluated bysimulation network properly aligned with 3GPP LTE network.

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Acknowledgements

I would like to express my gratitude and special thanks to my supervisor, Dr. MichealReyer for his enthusiasm, patience and kindness. I have learned many aspects of carry-ing out research in the field of wireless communications in the Institute of TheoreticalTechnology from his comments, advises and suggestions, and from our fruitful discussions.Without his encouraging and enlightening guidance, knowledge, persistent support thiswork would not have been successful. Many thanks are given to Dr. Anna Umbert fromTechnical University of Catalonia who reviewed this thesis and all staffs who have helpedme in one way or another and made the time of my master thesis pleasurable and memor-able at the institute of Ti. Finally, I would like to give my heartfelt gratitude to my parentsand specially my brother for his endless love, encouragement and support throughout mylife.

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Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Long Term Evolution (LTE) 52.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Multiple access technology in the downlink: OFDM and OFDMA . . . . . . 6

2.2.1 OFDM Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2.2 OFDM Transmitter and Receiver . . . . . . . . . . . . . . . . . . . . 72.2.3 Comparison of OFDM and Code Division Multiple Access (CDMA) 82.2.4 OFDMA Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3 Multiple access technology in the uplink: SC-FDMA . . . . . . . . . . . . . 112.4 Multiple antenna techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.5 Radio access modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.5.1 Transmission bandwidths . . . . . . . . . . . . . . . . . . . . . . . . 142.5.2 FDD and TDD LTE frequency bands . . . . . . . . . . . . . . . . . 14

2.6 LTE Air Interface Protocol Aspects . . . . . . . . . . . . . . . . . . . . . . . 162.6.1 Physical channels and modulation . . . . . . . . . . . . . . . . . . . 172.6.2 Frame structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.6.3 Resource element and resource block . . . . . . . . . . . . . . . . . . 18

2.7 System Architecture Evolution (SAE) . . . . . . . . . . . . . . . . . . . . . 192.8 Self Organizing Network (SON) . . . . . . . . . . . . . . . . . . . . . . . . . 20

3 Literature review of radio resource allocation in OFDMA networks 233.1 Single cell multi-user system model . . . . . . . . . . . . . . . . . . . . . . . 233.2 Rate Adaptive (RA) Radio Resource Allocation (RRA) . . . . . . . . . . . 25

3.2.1 Schemes for single-cell OFDMA systems . . . . . . . . . . . . . . . . 253.2.2 Distributed RA RRA schemes in OFDMA relay networks . . . . . . 27

3.3 Margin adaptive (MA) radio resource allocation . . . . . . . . . . . . . . . . 273.4 Adaptive resource allocation in cellular networks . . . . . . . . . . . . . . . 283.5 Network Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.5.1 Single user water-filling . . . . . . . . . . . . . . . . . . . . . . . . . 293.5.2 Multi-user water-filling . . . . . . . . . . . . . . . . . . . . . . . . . . 303.5.3 Rate region maximization . . . . . . . . . . . . . . . . . . . . . . . . 31

xi

Contents

4 Resource allocation in downlink multi-cell OFDMA networks 334.1 Problem formulation and system model . . . . . . . . . . . . . . . . . . . . 33

4.1.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.2 Cell assignment and initialization . . . . . . . . . . . . . . . . . . . . . . . . 384.3 Frequency Reuse Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384.4 Subcarrier Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.5 Modifications in pre-assigned subcarriers . . . . . . . . . . . . . . . . . . . . 44

4.5.1 Decrease pre-assigned subcarriers . . . . . . . . . . . . . . . . . . . . 444.5.2 Cost benefit analysis of adding subcarriers . . . . . . . . . . . . . . . 444.5.3 Increase pre-assigned subcarriers . . . . . . . . . . . . . . . . . . . . 47

4.6 Release subcarriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5 Performance evaluation 535.1 Simulation Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.1.1 User Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.2 Numerical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6 Conclusion and Future work 616.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

6.1.1 UE Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626.1.2 Load balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626.1.3 Femtocell and Macrocell Deplpyment . . . . . . . . . . . . . . . . . . 62

Bibliography 63

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1 Introduction

1.1 Background

Increasing demand of advanced mobile services such as high-speed internet access, multi-media online gaming, mobile TV, wireless Digital Subscriber Line (DSL), and integrationof voice, video, text in recent years is motivation for technological evolution in mobilecommunication. Indispensably, delivery of wireless broadband services will become po-tential and widespread in today’s wireless communication systems. Therefore, one of thechallenges for designing next-generation wireless systems is to provide wireless broadbandat better cost and performance, while maintaining seamless mobility, service control andQuality of Service (QoS) provisioning [4]. Furthermore, the globalization of markets, in-creasingly serious competence of vendors and especially universality of IEEE 802 wirelesstechnologies in wireless communication domain are also considered as essential motivationsfor technical evolution in wireless communication systems.

As a result, 3GPP, which is a collaboration agreement between a number of telecommunic-ations standards bodies and wireless manufacturers, has already launched the project LongTerm Evolution (LTE) regarding the evolution of the existed Third Generation (3G) sys-tems. The stated targets of the project LTE include support for high peak data rates, lowlatency, improved system capacity and coverage, reduced operating costs, multi-antennasupport, flexible bandwidth operations and easy integration to existing systems. LTE isalso referred to as EUTRA (Evolved Universal Terrestrial Radio Access) or E-UTRAN(Evolved Universal Terrestrial Radio Access Network).

LTE employs multiple access technologies on the air interface: specifically, OrthogonalFrequency Division Multiple Access (OFDMA) in downlink and Single Carrier FrequencyDivision Multiple Access (SC-FDMA) in uplink. OFDM technique that can be combinedwith multiple access using time, frequency or coding separation of the users are referredto as OFDMA technique. The OFDM technology has become a common advanced tech-nology for wide-band digital communication and also been considered one of prime trans-mission technologies of the next generation networks, e.g., LTE Advanced. The basic ideaof OFDM is to use a large number of closely spaced orthogonal subcarriers and thesesubacrriers are used in parallel. The key advantages of employing OFDM over single-carrier schemes are its robustness against multi-path delay spread and frequency selectivefading, elimination of the Inter-Symbol Interference (ISI). All aforementioned advantages

1

1 Introduction

demonstrates that OFDM technique is chosen as a greatly promising candidate for nextgeneration networks.

1.2 Motivations

Interference mitigation in OFDM systems is one of the major challenges, particularly forcell edge users. Although intra-cell interference inside a cell can be eliminated basingon the orthogonal characteristic among the subcarriers in the performance of OFDMtechnology, inter-cell interference exists and hinges seriously on the performance of systemin an OFDM based multi-cell wireless network. More specifically, inter-cell interferencein OFDM based network occurs when the same subcarriers are re-used in neighbor cells.It decreases the Signal to Interference and Noise Ratio (SINR) on these subacarriers.Obviously, interference has the greatest impact on cell-edge users. Hence, the inter-cellinterference is of great interest and one of the essential issues for wireless operators whowant to provide full coverage within their service area and guarantee a certain Quality ofService (QoS) to all users including cell-edge users regardless of their positions inside a cell.In the scope of this thesis, we explore the problem of resource allocation in an OFDMAbased multi-cell network or in other words, how to share the available radio resourcesamong users in terms of bandwidth allocation in order to suppress inter-cell interference,enhance throughput of the cell-edge user and spectral efficiency.

1.3 Organization of Thesis

The rest of the thesis is organized as follows:

Chapter 2 we present an overview of 3GPP LTE networks because the simulation frame-work that we will work on, in the next chapters is well aligned with the current standardsof the LTE. We focus on the description of LTE multiple access technologies such as Ortho-gonal Frequency Division Multiplexing (OFDM), Orthogonal Frequency Division MultipleAccess (OFDMA) for downlink and also address Single Carrier Frequency Division Mul-tiple Access (SC-FMDA) for LTE uplink. Frequency bandwidth and different radio accessmodes such as FDD and TDD that LTE supports will be presented in this chapter. LTEair interface protocol with the Radio Resource Control (RRC) layer which is responsiblefor setting up and management of radio resource blocks will be elaborated. We will ad-dress self organizing networks as new approaches to the network structure of which LTEis taking advantage.

Chapter 3 In this chapter we focus on resource allocation in wireless cellular networksemploying OFDMA as a promising air-interface for wireless systems. We present an over-

2

1.3 Organization of Thesis

view of previous work in the context of radio resource allocation in OFDMA networks.We provide optimization frameworks based on dynamic and adaptive resource allocationschemes studied in the literature for power, bandwidth and rate allocation in the downlink.Resource allocation problems from different aspects such as centralized and distributedscenarios with single- or multi-cell topologies will be considered.

Chapter 4 In this chapter resource allocation problem in the context of downlink multi-cell OFDMA network closely aligned with 3GPP LTE networks will be formulated. Sincethere is no computationally ‘efficient’ (polynomial time) algorithm known for this problemwe propose an iterative heuristic approach for user allocation and specifically subcarrierassignment with low computational cost and acceptable performance loss. In addition wewill make some assumptions to cast the problem, which we want to deal with, from thegeneral problem. The main focus of this chapter will be on providing mechanisms foradding or removing subcarriers and keeping the interference under control.

Chapter 5 In this chapter the algorithms proposed in chapter 4 will be evaluated. Wetake advantage of a simulated OFDMA network which is properly aligned with LTE 3GPPnetwork. Simulation setup, Network layout and user’s profile will be addressed in thischapter. We will assess system performance in terms of number of served users andspectral efficiency while investigating the number of pre-assigned subcarriers. Numericalsimulation results and proposed schemes will be analyzed and compared to each other.

Chapter 6 We summarize the main conclusions of this thesis and make recommendationsfor possible future work.

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2 Long Term Evolution (LTE)

In this chapter we present an overview of 3GPP LTE networks because the simulationframework that we will work on, in the next chapters is well aligned with the currentstandards of the LTE. We focus on the description of LTE multiple access technologiessuch as Orthogonal Frequency Division Multiplexing (OFDM), Orthogonal Frequency Di-vision Multiple Access (OFDMA), and Single Carrier Frequency Division Multiple Access(SC-FMDA). This chapter will provide the analysis of OFDM technique and a compar-ison of this technique to Code Division Multiple Access (CDMA) which Universal MobileTelecommunications System (UMTS) is based on. We will present multiple antenna tech-niques which are used in LTE with aim at improving Signal to Interference plus NoiseRatio (SINR). The frequency bandwidth and different radio access modes such as FDDand TDD that LTE supports are presented in this chapter. LTE air interface protocolwith the Radio Resource Control (RRC) layer which is responsible for setting up and man-agement of radio resource blocks will be elaborated. After that, resource blocks as thesmallest information unit which can be transmitted and different frame structures usedin LTE will be defined. Finally, we will discuss about self organizing networks as newapproaches to the network structure of which LTE is taking advantage.

2.1 Overview

LTE stands for Long Term Evolution and it is a project name of a new, high performanceair interface for mobile communication systems. It started in 2004 by third Third Genera-tion Partnership Project(3GPP). An earlier 3GPP system known as the Universal MobileTelecommunication System (UMTS) and evolved UMTS Terrestrial Radio access Network(E-UTRAN) which evolved from the Global System for Mobile Communications(GSM) to-wards an all-IP broadband network. They are known as the related specifications of LTE.LTE’s E-UTRA technology provides a framework for increasing data rates and overall sys-tem capacity, reducing latency, improving spectral efficiency and cell-edge performance.A rapid increase of mobile data traffic and emergence of applications like MMOG (Multi-media Online Gaming), mobile TV, web 2.0 streaming contents have motivated the 3GPPto work on LTE. LTE’s goals on the way towards Fourth-Generation (4G) are to providehigh data rate, low latency and packet optimized radio access technology supporting flex-ible bandwidth deployments. LTE’s network architecture is also able to support packetswitched traffic with seamless mobility and great quality of service [2].

5


2.2 Multiple access technology in the downlink: OFDM andOFDMA

Transmission in LTE is based on Multiple Access (MA) technologies which are in particularOFDMA which is a variant of OFDM for the downlink and SC-FDMA for the uplink. Toovercome the effect of multi path fading problem available in UMTS, LTE uses OFDMfor the downlink that is, from the base station to the terminal to transmit the dataover many narrow band carriers instead of spreading one signal over the complete carrierbandwidth.

2.2.1 OFDM Technique

OFDM or Orthogonal Frequency Division Multiplexing is a modulation technique whichis used commonly in many radio communication systems like wireless local area network(IEEE 802.11 versions a, g and n) and WiMAX (IEEE 802.16), as well as in digitaltelevision and radio broadcasting. Rather than transmit a high rate stream of data in serialwith a single carrier, OFDM transmitter makes use of a large number of closely spacedorthogonal subcarriers that are transmitted in parallel. The term orthogonal frequency-division is due to the fact that OFDM subcarriers are mutually orthogonal over the specifictime interval. The sub-carriers are combined to produce data rates similar to conventionalsingle-carrier modulation schemes in the same bandwidth.

Each of the subcarriers modulated with one of the modulation schemes like QuadraturePhase-Shift Keying (QPSK), 16 QAM, 64 QAM (Quadrature amplitude modulation) atlower symbol rate and each of them experiences frequency flat fading channel where thecoherence bandwidth of the channel is larger than the bandwidth of the signal. Therefore,all frequency components of the signal will experience the same magnitude fading. In con-trast with flat fading channel, in frequency selective channel as the coherence bandwidthof the channel is smaller than the bandwidth of the signal, different frequency componentsof the signal experience different uncorrelated fading. Thus, an appropriately designedOFDM system converts a frequency selective fading channel into a set of parallel nar-rowband flat fading channels across the subcarriers. For 3GPP LTE the basic subcarrierspacing equals 15 kHz. The number of subcarriers depends on the transmission bandwidth[10].

Figure 2.1 is the representation of key features of OFDM signal in the joint time-frequencydomain. Each of the symbols are modulated with adjacent subcarrier independently. Thesource of Inter Symbol Interference (ISI) is delay spread which can be interpreted as thedifference between the time of arrival of the earliest significant multipath component (typ-ically the Line-Of-Sight (LOS) component) and the time of arrival of the latest multipathcomponent [11]. In order to avoid ISI caused by multi-path delay spread guard intervals

6

2.2 Multiple access technology in the downlink: OFDM and OFDMA

are inserted between the symbols. The guard time must be chosen to be larger than theexpected delay spread, such that multi-path components from one symbol cannot interferewith the next symbol.

Figure 2.1: OFDM signal represented in frequency and time [2]

2.2.2 OFDM Transmitter and Receiver

An illustrative description of a basic OFDM modulator is provided in Figure 2.2. Itconsists of a bank of N complex modulators, where each modulator corresponds to oneOFDM subcarrier. Mapping specifies the modulation scheme (Binary phase-shift keyingor BPSK, QPSK, 16-QAM ...). The resulting information is the amplitude and phaseof each sub-carrier as a function of frequency. By passing it through an Inverse FastFourier Transform (IFFT), we can compute the in-phase and quadrature components ofthe corresponding time-domain waveform.

Figure 2.2: OFDM transmitter block diagram

LTE uses a slightly more complex technique, known as Cyclic Prefix (CP) insertion. LTE

7


transmitter starts by inserting a guard period before each symbol, as before. However, itthen copies data from the end of the symbol following, so as to fill up the guard period.In fact the CP is a copy of the end of a symbol inserted at the beginning. If the guardperiod is longer than the delay spread in the radio channel, and if each OFDM symbol iscyclically extended into the guard period (by copying the end of the symbol to the start tocreate the cyclic prefix), then the inter-symbol interference can be completely eliminated.In the end the information can be mixed up to radio frequency for transmission [10].

In the receiver the reverse operations are done to extract the originally sent message. Butthere should be some extra blocks to make more sufficient reception such as synchronizationand equalization. Synchronization is done to overcome frequency and time offset.

Figure 2.3: OFDM receiver block diagram

2.2.3 Comparison of OFDM and Code Division Multiple Access (CDMA)

As the FFT operations at the heart of OFDM technique was too expensive and demandingin the past, 3GPP couldn’t consider that. In 1998, They chose an alternative technologycalled CDMA. Today the cost of digital signal processing has been greatly reduced andOFDM is now considered as a commercially feasible method of wireless transmission forthe handset. Both OFDM and CDMA have significant benefits. However compared toCDMA upon which UMTS is based , OFDM offers a number of distinct advantages [2]:

• OFDM can be scaled up to wide channels that are more resistant to fading.

• OFDM channel equalizers are much simpler to implement than are CDMA equalizers,

8

2.2 Multiple access technology in the downlink: OFDM and OFDMA

as the OFDM signal is represented in the frequency domain rather than the timedomain.

• OFDM can be made completely resistant to multi-path delay spread. This is possiblebecause the long symbols used for OFDM can be separated by CP.

• OFDM is better suited to MIMO. The frequency domain representation of the signalenables easy precoding to match the signal to the frequency and phase characteristicsof the multi-path radio channel.

OFDM disadvantages:

• In OFDM the subcarriers are closely spaced making OFDM sensitive to frequencyerrors and phase noise. Therefore, OFDM is also sensitive to Doppler shift, whichcauses interference between the subcarriers.

• OFDM has no feature to combat with Inter-Cell Interference(ICI) at the cell edgewhereas CDMA uses scrambling codes to provide protection against that. Therefore,some form of frequency planning at the cell edges will be required.

Figure 2.4 shows a plan for frequency re-use to avoid inter-cell interference at the cell edges.It shows an example that every cell is sharing the same frequency band and every basestation is controlling one cell. Within that band, each cell transmits to nearby mobilesusing the same set of sub-carriers, denoted f0. This idea works well because for the usersclose to base station the received signals are strong enough to overwhelm any interference.Distant users are easily damaged by interference because the received signals are weaker.One approach is neighbouring cells can transmit to those users using different sets ofsub-carriers. Half the frequency band is reserved for nearby users, while the remainder isdivided into three sets, denoted f1, f2 and f3, for use by distant users.

9


Figure 2.4: Example implementation of fractional frequency re-use when using OFDMA.(a) Use of the frequency domain. (b) Resulting network plan [8].

2.2.4 OFDMA Technique

Orthogonal Frequency Division Multiple Access (OFDMA) is a variant of OFDM, which iscommonly used in wireless systems. Since the very narrow band UE-specific transmissionscan suffer from narrowband fading and interference in standard OFDM, 3GPP choseOFDMA, which incorporates some elements of Time Division Multiple Access (TDMA).OFDMA allows subsets of the subcarriers to be allocated dynamically among the differentusers on the channel, as shown in Figure 2.5. The base station can respond to frequencydependent fading, by allocating subcarriers on which the mobile is receiving a strongsignal.

Figure 2.5: OFDM and OFDMA subcarrier allocation [2]

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2.3 Multiple access technology in the uplink: SC-FDMA

By allocating sub-carriers in response to changes in the fading patterns, an OFDMAtransmitter can greatly reduce the impact of time- and frequency-dependent fading.

2.3 Multiple access technology in the uplink: SC-FDMA

For the uplink communication a different concept for the access technique is used. SC-FDMA used in the uplink where lower Peak-to-Average Power Ratio (PAPR) greatlybenefits the mobile terminal in terms of transmit power efficiency and reduced cost ofthe power amplifier [22]. SC-FDMA is not appropriate for the downlink, because lowerPARR is valuable in the User Equipment (UE), but less so in the base stations which iscarrying mutiple users and multiple signals. Another issue is that SC-FDMA need extracomputation at the receiver. If the receiver is base station this is not a big issue, butfor UE as receiver this is costly, because it requires more power and more DSP. There-fore, SC-FDMA has been adopted as the uplink multiple access scheme in 3GPP LongTerm Evolution (LTE), or Evolved UTRA (E-UTRA). Although it is still using a formof OFDMA technology, the implementation is called SC-FDMA. In order to understandthe differences between OFDMA and SC-FDMA, the Figure 2.6 provides the intuitivelygraphical comparison of these modulation schemes. Apparently, in this example, QPSKmodulation scheme is applied for generation of OFDMA and SC-FDMA symbol. EachQPSK data symbol includes four subcarriers with the payload data. The generation of OF-DMA signal is shown on the left side of Figure 2.6, four adjacent subcarriers at spacing of15kHz are already mapped into desired place in overall channel bandwidth. Because onlythe phase of each subcarrier is modulated, the subcarrier power keeps constant betweensymbols in QPSK modulation scheme. Four contiguous symbols are propagated in paralleland then after one OFDMA symbol period has elapsed, CP is inserted between symbols.For visual clarity CP is shown as a gap on the left side of Figure 2.6. However, it is actuallya copy of the end of next symbol. It implies that the transmission power is continuousbut the phase is discontinuous at the symbol boundary. Finally, the transmitted signal iscreated by the performance of IFFT on each subcarrier. IFFT converts the QPSK datasymbols in frequency domain into time domain signals. Sum of the final time-domainwaveform is referred as the transmitted signal.

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Figure 2.6: Comparison of OFDMA and SC-FDMA transmitting a series of QPSK datasymbols [2].

Generation of SC-FDMA signal begins with a special precoding process but then continuesin a manner similar to OFDMA. To create a time-domain waveform of the QPSK datasub-symbols, the four color-coded QPSK data symbols form Figure 2.6 are used and theprocess is to compute the trajectory traced by changing of one QPSK data symbol to thatof the next one to achieve one SC-FDMA symbol in the time domain. With the rate of fourtimes the rate of the SC-FDMA symbol, one SC-FDMA symbol contains four consecutiveQPSK data symbols.

Next step for generating SC-FDMA signal, Discrete Fourier Transform (DFT) process isperformed to those symbols from frequency domain into time domain waveforms. TheDFT sampling frequency is chosen such that the time-domain waveform of one SC-FDMAsymbol is completely presented by four (or M) DFT points (or bins) spacing 15kHz toeach other and each bin presents one subcarrier. The most obvious difference betweenthe two schemes is described visually on right hand side of Figure 2.6. The difference isdistinguishable in terms of that OFDMA transmits the four QPSK data symbols in parallelwith one per subcarrier, whereas SC-FDMA transmits the four QPSK data symbols inseries at four times symbol rate, with each data symbol occupyingM×15kHz bandwidth.

Obviously, OFDMA signal is clearly a multi-carrier with one data symbol per subcarrier,while SC-FDMA signal displays to be similar to a single-carrier (therefore ‘SC’ in theSC-FDMA name) with each data symbol being envisioned one wide bandwidth signal.

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2.4 Multiple antenna techniques

Remarkably, the SC-FDMA symbol consists of sub-symbols (or M = 4 sub-symbols).These multi-symbols have propagated in parallel, which creates the undesirable differenthigh Peak to Average Ratio (PAR) of OFDMA. By transmission of M data symbols inseries atM times the rate, the SC-FDMA occupied bandwidth is the same as multi-carrierOFDMA but essentially, the PAR is the same as that used for the original data symbol. Bytransmitting together many narrow-band QPSK waveforms in OFDMA, the power peakis higher and it would be seen in the wider-bandwidth, single-carrier QPSK waveform ofSC-FDMA [2].

2.4 Multiple antenna techniques

MIMO (Multiple Input Multiple Output) is another LTE major innovation, with the abilityto further improve LTE’s data throughput and spectral efficiency above that obtained bythe use of OFDM. Although MIMO adds complexity to the system in terms of processingand the number of antennas required, it enables far higher data rates to be achievedalong with much improved spectral efficiency. As a result, MIMO has been included asan integral part of LTE. Essentially MIMO employs multiple antennas on the receiverand transmitter to utilize the multi-path effects that always exist to transmit additionaldata, rather than causing interference. The schemes employed in LTE again vary slightlybetween the uplink and downlink. The reason for this is to keep the terminal cost low asthere are far more terminals than base stations [26]. For the downlink, a configuration oftwo transmit antennas at the base station and two receive antennas on the mobile terminalis used as baseline, although configurations with four antennas are also being considered.For the uplink from the mobile terminal to the base station, a scheme called Multi-UserMIMO (MU-MIMO) is to be employed. Using this, even though the base station requiresmultiple antennas, the mobiles only have one transmit antenna and this considerablyreduces the cost of the mobile. In operation, multiple mobile terminals may transmitsimultaneously on the same channel or channels, but they do not cause interference toeach other because mutually orthogonal pilot patterns are used. This technique is alsoreferred to as Spatial Domain Multiple Access (SDMA).

2.5 Radio access modes

The LTE air interface supports the Multimedia Broadcast and Multicast Service (MBMS),a technology for broadcasting content such as digital TV to User Equipment (UE) usingpoint-to-multi-point connections. MBMS was first introduced for UMTS in release 6 itis now an attractive option for operators who finally have enough bandwidth to copewith the demand. LTE provided an evolved MBMS service compared to UMTS Release6 with the target to reach cell-edge spectral efficiency in an urban or sub-urban environ-

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Table 2.1: Transmission bandwidth configuration

Channel bandwidth (MHz) 1.4 3 5 10 15 20Transmission bandwidth configuration (MHz) 1.08 2.7 4.5 9 13.5 18Transmission bandwidth configuration (NUL

RB OR NDLRB ) (RB) 6 15 25 50 75 100

ment of 1 bps/Hz which is equivalent to support of at least 16 Mobile TV channels ataround 300 Kbps per channel in a 5 MHz carrier. Multicast-Broadcast Single-FrequencyNetwork (MBSFN) is the service which operates over a multicast/broadcast over singlefrequency network using a time-synchronized common waveform that can be transmittedfrom multiple cells for a given duration.

2.5.1 Transmission bandwidths

In order to support international and regional spectrum regulations LTE’s specificationsinclude variable channel bandwidths selectable from 1.4 to 20 MHz with subcarrier spacingof 15 kHz. For the new LTE eMBMS, a subcarrier spacing of 7.5 kHz is also possible.Subcarrier spacing is constant regardless of the channel bandwidth. LTE air interface is“bandwidth agnostic” which allows the air interface to adapt to different channel band-widths with minimal impact on system operation. A Resource Block (RB) is the smallestamount of resource that can be allocated in the uplink or downlink. An RB is 180 kHzwide and lasts for one 0.5 ms timeslot. For standard LTE, an RB comprises 12 subcarriersat a 15 kHz spacing with 1 ms each symbol, and for eMBMS with the optional 7.5 kHzsubcarrier spacing an RB comprises 24 subcarriers for 0.5 ms each symbol. The maximumnumber of RBs supported by each transmission bandwidth is given in the Table 2.1.

2.5.2 FDD and TDD LTE frequency bands

The LTE air interface supports both FDD and TDD modes, with different frame struc-ture. Half-duplex FDD allows the sharing of hardware between uplink and downlink sincethe uplink and downlink are never used simultaneously. This technique, while halvingpotential data rates, has uses in some frequency bands and also offers a cost saving. FDDspectrum requires pair bands, one of the uplink and one for the downlink. They are pairedin order to allow simultaneous transmission on two frequencies. The bands also have asufficient separation to enable the transmitted signals not to unduly impair the receiverperformance. TDD requires a single unpaired frequency band. Therefore, the uplink anddownlink share the same frequency, being time multiplexed. As a result, there are dif-ferent LTE band allocations for TDD and FDD. In some cases these bands may overlap

14

2.5 Radio access modes

but this does not necessarily simplify designs since there can be band-specific performancerequirements based on regional needs. UEs that roam may encounter both types on thesame band. Therefore, UEs need to detect what type of transmission is being made onthat particular LTE band in its current location.

Peak single user data rates

In 3GPP standard Release 99, Wideband Code Division Multiple Access (WCDMA) hada peak data rate of 2 Mbps on the downlink and 1 Mbps on the uplink. The introductionof High Speed Downlink Packet Access (HSDPA) in 3GPP Release 5 the peak downlinkdata rate increased to 14.4 Mbps, by the use of a faster coding rate and a new modulationscheme, 16-QAM. There was a similar increase for the uplink in Release 6, through theintroduction of high speed uplink packet access. Later releases have increased the peakdata rate further, through the introduction of 64-QAM and spatial multiplexing, andthe use of multiple carriers. The peak data rate in LTE Release 8 is 300 Mbps in thedownlink and 75 Mbps in the uplink. Figure 2.7 shows the peak data rate of LTE whichhas increased since its introduction in 3GPP Release 8 and compares it with the peakdata rate of WCDMA from Release 99. The data are taken from the most powerful UEcapabilities available in FDD mode at each release.

Figure 2.7: Evolution of the peak data rates of WCDMA and LTE in FDD mode [8]

15


2.6 LTE Air Interface Protocol Aspects

The architecture of E-UTRA radio interface protocol around the physical layer is shownin Figure 2.8 [1]. The physical layer provides data transport services to the higher layers.These services are accessed through transport channels via the Media Access Control(MAC) sub-layer. The physical layer provides transport channels to the Layer 2 MACsub-layer, and the MAC sub-layer provides logical channels to the Layer 2 Radio LinkControl (RLC) sub-layer. Transport channels are characterized by how the information istransferred over the radio interface, whereas logical channels are characterized by the typeof information transferred. In the following diagram, the circles between different layersor sub-layers indicate Service Access Points (SAPs) [1].

Figure 2.8: Radio interface protocol architecture around the physical layer [1].

• Layer 1 - Physical Layer: To provision the data transport service to the higher layers,the physical layer performs the fundamental functions as following: Error detectionon the transport channels, Forward Error Correction (FEC) encoding/decoding ofthe transport channels, Hybrid Automatic Repeat Request (HARQ) soft-combining,rate matching and mapping of coded transport channels to physical channels, mod-ulation and demodulation of physical channels, frequency and time synchronization,radio characteristics measurements, MIMO antenna processing, transmit diversity,beam forming, RF processing, etc. The physical layer specifications are divided intofour major sections: Physical channels and modulation, multiplexing and channelcoding, physical layer procedures and physical layer measurements.

16

2.6 LTE Air Interface Protocol Aspects

• Layer 2 - MAC and RLC sub-layers: The MAC layer performs the mapping betweenthe transport channels and logical channels, scheduling of UEs and their servicesbased on their priorities, selecting the proper transport format. The RLC suppliessequenced delivery of Service Data Units (SDUs) to higher layers and eliminate thecopy of SDUs.

• Layer 3 - Radio Resource Control (RRC) layer: RRC is responsible for setting upand managing radio resource blocks. RRC makes decisions of handover or handoverbased on the neighbouring cell measurement. The requirements for Radio ResourceManagement (RRM) detailed in covers the procedures and performance requirementsfor the efficient utilization of radio resources.

2.6.1 Physical channels and modulation

LTE air interface includes physical channels and physical signals defined in [2]. Systemsynchronization, cell identifications and radio channel estimation are the uses of physicalsignals which are generated in Layer 1. Physical channels carry data from higher layersconsisting of control, scheduling and user payload. There are reference signals in bothuplink and downlink which are known as pilot signals in other standards. The receiveruses these signals to estimate the phase and amplitude flatness of received signal. Inparticular at high modulation depth such as 16 QAM or 64 QAM, where small errors inphase or amplitude cause demodulation errors, using the reference signal is crucial.

2.6.2 Frame structure

As described before, the physical layer supports OFDMA on the downlink and SC-FDMAon the uplink. In addition, both paired and unpaired spectrum are supported usingfrequency division duplexing (FDD) and time division duplexing (TDD), respectively.Although the LTE downlink and uplink use different multiple access schemes, they sharea common frame structure. The frame structure defines the frame, slot, and symbol inthe time domain. Two radio frame structures are defined for LTE and shown in Figures2.9 and 2.10 [2]. Frame structure type 1 is defined for FDD mode. Each radio frame is10 ms long and consists of 10 subframes. Each subframe contains two slots. In FDD,both the uplink and the downlink have the same frame structure though they use differentspectra.

17


Figure 2.9: LTE frame structure type 1 [2]

Frame structure type 2 shown in figure 2.10 is defined for TDD mode. This example is for5 ms switch-point periodicity and consists of two 5 ms half-frames for a total duration of 10ms. Subframes consist of either an uplink or downlink transmission or a special subframecontaining the Downlink and Uplink Pilot Time Slots (DwPTS and UpPTS) separatedby a transmission gap guard period (GP). The allocation of the subframes for the uplink,downlink, and special subframes is determined by one of seven different configurations.Subframes 0 and 5 are always downlink transmissions, subframe 1 is always a specialsubframe, and subframe 2 is always an uplink transmission. The composition of the othersubframes varies depending on the frame configuration.

Figure 2.10: LTE frame structure type 2 for 5 ms switch-point periodicity [2]

2.6.3 Resource element and resource block

A resource element is the smallest unit which occupies one subcarrier in frequency domainand OFDMA or SC-FDMA symbol in time domain. Figure 2.11 shows resource elements

18

2.7 System Architecture Evolution (SAE)

for downlink.

Figure 2.11: Resource grid for downlink [2]

The smallest unit which can be scheduled for transmission is resource block which occu-pies 180 kHz in frequency domain and 1 slot (0.5 ms) in time domain. The number ofsubcarriers per resource block and the number of symbols per RB vary as a function ofthe cyclic prefix length and subcarrier spacing. For example 7.5 kHz subcarrier spacingleads to longer symbols and consequently longer CP which is used to combat the higherdelay spread in the multicast /Broadcast applications.

2.7 System Architecture Evolution (SAE)

Along with 3G LTE that applies more to the radio access technology of the cellular tele-communications system, there is also an evolution of the core network known as SAE. Thisnew architecture has been developed to provide a considerably higher level of performancethat is in line with the requirements of LTE. The new SAE has also been developed sothat it is fully compatible with LTE Advanced, the new 4G technology. Therefore, when

19


LTE Advanced is introduced, the network will be able to handle the further data increaseswith little change.

2.8 Self Organizing Network (SON)

In order to meet the requirements for increased data capacity and reduced latency, alongwith the move to an all-IP network, it is necessary to adopt a new approach to the networkstructure. For 3G UMTS /WCDMA the UTRAN comprising the Node B’s or base stationsand Radio Network Controllers (RNCs) employed low levels of autonomy [26]. The NodeBs were connected in a star formation to the RNCs which carried out the majority of themanagement of the radio resource. In turn the RNCs connected to the core network andconnect in turn to the core network. To provide the required functionality within LTESystem Architecture Evolution (SAE), the basic system architecture sees the removal ofa layer of management. The RNC is removed and the radio resource management isdevolved to the base-stations. The new style base-stations are called eNodeBs or eNBs.The eNBs are connected directly to the core network gateway via a newly defined ‘S1interface’. In addition to this, the new eNBs also connect to adjacent eNBs in a mesh viaan ‘X2 interface’. This provides a much greater level of direct interconnectivity. It alsoenables many calls to be routed very directly as a large number of calls and connectionsare to other mobiles in the same or adjacent cells. The new structure allows many calls tobe routed far more directly and with only minimum interaction with the core network. Inaddition to the new Layer 1 and Layer 2 functionality, eNBs handle several other functions.This includes the radio resource control including admission control, load balancing andradio mobility control including handover decisions for the mobile or UE. The additionallevels of flexibility and functionality given to the new eNBs mean that they are morecomplex than the UMTS and previous generations of base-station. However the new 3GLTE SAE network structure enables far higher levels of performance. In addition to this,their flexibility enables them to be updated to handle new upgrades to the system includingthe transition from 3G LTE to 4G LTE Advanced [26]. With LTE requiring smaller cellsizes to enable the much greater levels of data traffic to be handled, networks have becomeconsiderably more complicated and trying to plan and manage the network centrally isnot as viable. The term SON came into frequent use after the term was adopted by theNext Generation Mobile Networks (NGMN) alliance. The idea came about as result of theneed within LTE to be able to deploy many more cells. Femtocells and other microcellsare an integral part of the LTE deployment strategy and 3GPP created the standardsfor SON. Although LTE SON self-optimizing networks is one of the major drivers for thegeneric SON technology, the basic requirements remain the same whatever the technologyto which it will be applied. The main elements of LTE SON :

• Self configuration: The aim for the self configuration aspects of LTE SON is toenable new base stations to become essentially ‘Plug and Play’ items. They should

20

2.8 Self Organizing Network (SON)

need as little manual intervention in the configuration process as possible.

• Self optimization: Once the system has been set up, LTE SON capabilities willenable the base station to optimize the operational characteristics to best meet theneeds of the overall network.

• Self-healing: Another major feature of LTE SON is to enable the network to self-heal.It will do this by changing the characteristics of the network to mask the problemuntil it is fixed. For example, the boundaries of adjacent cells can be increased bychanging antenna directions and increasing power levels.

21

3 Literature review of radio resourceallocation in OFDMA networks

In this chapter we focus on resource allocation in wireless cellular networks employingOFDMA as a promising air-interface for wireless systems due, primarily, to its inherentresistance to frequency-selective multipath fading and the flexibility it offers in radio re-source allocations. As in OFDMA the total bandwidth is divided into a large number ofsubchannels, throughout the network decisions based on frequency, power allocation canbe made such that the resource utilization be maximized.

In this chapter we present an overview of previous work in the context of radio resourceallocation in OFDMA networks. We provide optimization frameworks based on dynamicand adaptive resource allocation schemes studied in the literature for power, bandwidthand rate allocation in the downlink. Resource allocation problems from different aspectssuch as centralized and distributed scenarios with single- or multi-cell topologies will beconsidered. Throughout this chapter, it is assumed that the network employs an initialchannel estimation phase so that the frequency selective channel gain between any arbit-rary transmitter and receiver through the network is known. It is further assumed thatthe channel estimation is perfect.

3.1 Single cell multi-user system model

A system model for downlink OFDMA-based wireless network, is as follows [27]:

Assume Ku = {1, 2, ...,K} and Nu = {1, 2, ..., N} are the sets of users and subcarriers,respectively. The data rate of the k-th user Rk is given by

Rk = B

N

N∑n=1

ak,n log2 (1 + γk,n)

where B is the total bandwidth of the system and ak,n is the subcarrier assignment indexindicating wether the k-th user occupies the n-th subcarrier or not; i.e., ak,n = 1 only ifsubcarrier n is allocated to user k; otherwise it is zero. The bandwidth of each subchannel

23

3 Literature review of radio resource allocation in OFDMA networks

is B/N = 1/T where T is the OFDM symbol duration. γk,n is the Signal-to-Noise Ratio(SNR) of the n-th subcarrier for the k-th user and is given by

γk,n = pk,nHk,n =pk,nh

2k,n

N0B/N

where pk,n is the power allocated for user k in subchannel n, and hk,n, and Hk,n denotethe channel gain and channel-to-noise ratio for user k in subchannel n, respectively. Thus,The total data rate RT of this system is given by

RT = B

N

K∑k=1

N∑n=1


γk,n is the effective SNR known by the modulation scheme adjusted to meet Bit Error Rate(BER) requirements which is a measure of received bits of a data stream that have beenaltered due to noise, interference, distortion or bit synchronization errors. The generalform of the subcarrier and power allocation problem is given as

maxak,n,pk,n

RT = B

N

K∑k=1

N∑n=1


or

minak,n,pk,n

PT = B

N

K∑k=1

N∑n=1

ak,npk,n,

subject to

C1: ak,n ∈ {0, 1},∀k, n,

C2:K∑k=1

ak,n ≤ 1, ∀n,

C3: pk,n ≥ 0, ∀k, n,

C4:K∑k=1

N∑n=1

ak,npk,n ≤ Ptotal,

C5: user rate requirement.

The first two constraints are on subcarrier allocation to ensure that each subchannel isassigned to only one user. C3 guarantees that the power values are non-negative. C4 is onlyeffective in problems where there is a power constraint Ptotal on the total transmit power ofthe system PT . C5 determines the fixed or variable rate requirements of the users. As theoptimal solution for this problem is computationally complex, they may not be practicalin real time applications [9]. Therefore, suboptimal algorithms have been developed which

24

3.2 Rate Adaptive (RA) Radio Resource Allocation (RRA)

differ mostly in the approach they choose to split the procedure into several (preferablyindependent) steps to make the problem tractable and, in their simplifying assumptionsto reduce the complexity of the allocation process. The performance of each algorithmgreatly depends on the formulation of the problem and the validity of these simplifyingassumptions. In the next sections we consider two main classes of dynamic resourceallocation schemes which have been reported in the literature as Margin Adaptive(MA)[35, 34, 40] and Rate Adaptive(RA) schemes [30, 29, 32].

3.2 Rate Adaptive (RA) Radio Resource Allocation (RRA)

The optimization problem in RA scheme is to maximize the total data rate of the sys-tem with constrains on the total transmit power. Different scenarios of centralized ordistributed algorithms, along with single-cell and multicell network topologies have beenconsidered in the literature for the RA RRA problem.

3.2.1 Schemes for single-cell OFDMA systems

A network with single cell and a single fixed Relay Station (RS) was considered in [14].Such a network with multiple fixed RSs was studied in [23]. The algorithm proposed in[14] performs subcarrier allocation with uniform power distribution (power level of bothBS and RS were equal) which was predetermined. The second algorithm achieves anoptimal joint power and subcarrier allocation. It has been shown in the literature, e.g.,[13] that optimization can be achieved when a subcarrier is assigned to only one user whohas the best channel gain for that subcarrier, and also that equal power allocation amongsubcarriers has almost the same performance as water filling transmit power adaptationbut with less complexity.

Utility function in multi user OFDM systems

The concept of utility functions was used by [31] to formulate the Rate Adaptive (RA)problem in multiuser OFDM systems. Utility is a mapping from network resources usedby a user to a real number and it’s a function of user’s data rate as the most importantfactor to determine satisfaction of users.Therefore, the utility function should be a non-decreasing function of r which is a positive value. The dynamic resource allocation withutility based formulation is the following:

maxak,n,pk,n

K∑k=1

Uk(Rk),

25


subject to:

C1: Si ∩ Sj = ∅,∀i, j ∈ Ku, i 6= j

C2: ∪kSk ⊆ {1, 2, ..., N},

C3: pk,n ≥ 0, ∀k, n,

C4:K∑k=1

∑n∈Sk

pk,n ≤ Ptotal,

where Uk(Rk) is a utility function for the k-th user. Rk is the sum of the data rate of userk over all the subcarriers. Sk is the set of subcarriers assigned to user k for which ak,n = 1.From the objective function and constrains it is obvious that fairness among the users wasnot considered. In [31] the extreme case of an infinite number of orthogonal subcarrierseach with an infinitesimal bandwidth was investigated by introducing two theorems: the-orem I gave the optimal subcarrier allocation assuming a fixed power allocation on allthe subcarriers and theorem II gave the optimal power allocation given a fixed subcarrierallocation. Combining the results of the two theorems, the optimal frequency set and thepower allocation for the extreme case were obtained.

To tackle the mixed integer and continuous variable optimization problem with Uk(Rk) =Rk the authors of [24] proposed a greedy subcarrier and power allocation in relay net-works. Another interesting approach to deal with mixed integer optimization problemwas proposed in [20]. Their approach was to transform the integer optimization probleminto a linear distribution problem in a directed graph to allow the use of the linear optimaldistribution algorithms available in the literature.

Centralized RA RRA schemes in multicell OFDMA networks

A centralized downlink OFDMA scenario in a multicellular network enhanced with sixfixed Relay Stations (RS) per cell was considered in [17, 18]. Relay stations are used toaid the direct communication between the BS and users in order to cope with path-losscharacteristic of wireless channel. The RS forwards the received signal to the BS (uplink)or user (downlink) by employing either the Amplify-and-Forward (AF) or the Decode-and-Forward (DF) strategy on the same subcarrier. The problem was formulated as theminimum number of subcarriers required to satisfy a user’s Quality of Service (QoS).By considering latency, overhead, and computational complexity, it may be seen thatcentralized RRA schemes are not the best option for future wireless networks [9]. Thishas led to the importance of distributed schemes being recognized.

26

3.3 Margin adaptive (MA) radio resource allocation

3.2.2 Distributed RA RRA schemes in OFDMA relay networks

A semi-distributed downlink OFDMA scheme in the form of two algorithms of Separateand Sequential Allocation (SSA) and Separate and Reuse Allocation (SRA), in a singlecell enhanced by M half-duplex fixed relays was considered in [15]. It means that thetransmission and reception at that relay do not occur simultaneously. The starting pointof both the two-step SSA and the SRA algorithm allocation schemes is basically the same.The Subscriber Station (SS) attached to the BS and relays are referred to as the BS–SSand RS–SS clusters, respectively. In the first step, each RS, along with its SS cluster, istreated as a large SS with a required minimum rate equal to the sum of all the minimumrequired rates of the SSs in its cluster. The BS allocates the resources among its own SSsand these virtual large SSs. In the second step, the RS allocates resources to the SSs inits cluster based on one of two allocation schemes:

• Resources assigned to that BS-RS link in the first step are allocated among theconnected SSs (SSA).

• The RS reallocates all N subcarriers to its connected SSs regardless of the BS as-signments (SRA).

Simulation results for a single cell with one relay showed that the semidistributed scheme,SSA in particular, has a comparable capacity and probability of dropping a user to thecentralized scheme [12].

The SSA algorithm showed significant performance stability over the SRA. Since both RSand BS may assign the same subcarriers to their respective SSs in the SRA algorithm,intracell interference may occur, which results in a considerable increase in outage prob-ability.

In general, the proposed semi-distributed schemes reduce the amount of overhead requiredto feedback the CSI and minimum rates to the BS. However, in the case of SRA, thereis no need to communicate such information to the BS. These schemes fail to exploit theinterference avoidance and traffic diversity gains.

3.3 Margin adaptive (MA) radio resource allocation

The MA optimization problem assumes a set of user data rates with fixed QoS require-ments. This can be formulated as [27]:

27


minak,n,pk,n

PT =K∑k=1

N∑n=1

ak,npk,n,

subject to:

C1: ak,n ∈ {0, 1},∀k, n,

C2:K∑k=1

ak,n = 1, ∀n,

C3: pk,n ≥ 0, ∀k, n,

C4:K∑k=1

N∑n=1

ak,npk,n ≤ Ptotal,

C5: Rk ≥ Rk,min, k = 1, 2, ...,K

With help of constraint relaxation and to make the problem tractable, the authors of [34]introduced a new parameter to the cost function, taking values within the interval [0,1],which can be interpreted as the sharing factor for each subcarrier. Then it is shown that theoptimization problem can be reformulated as convex minimization problem over a convexset and be solved by computing the Lagrangian of the problem. Lagrange multiplierswhich satisfy the individual data rate constraints can be found using an iterative searchalgorithm. However, the iterative computation and search for this algorithm make itprohibitively expensive with complexity of O(NK3) where N is the number of subcarriersand K is the number of MA users. One solution to simplify the algorithm is to assumethat the channel is flat for a certain number of subcarriers, as in [36].

3.4 Adaptive resource allocation in cellular networks

A wireless cellular network comprises base stations serving users. The assignment of usersto the base stations depends on the strength of receiving signal. As a mobile devicecan usually receive signals from several base stations, it is typically assigned to the basestation with the strongest received signal. Signals from other base stations are knownas intercell interference which may cause a low Signal to Interference plus Noise Ratio(SINR). Consequently this decreases the transmission data rate of the users.

In order to avoid excessive intercell interference traditional cellular networks employ afixed frequency reuse pattern so that neighboring base stations do not share the samefrequency. As a result the users in cell-edge, which are suffering from interference from

28

3.5 Network Optimization

neighboring cells, do not interfere with each other. The traditional fixed frequency reuseschemes are effective in minimizing intercell interference, but are also resource intensive inthe sense that each cell requires a substantial amount of nonoverlapping bandwidth, so thatonly a fraction of the total bandwidth can be made available for each cell. Consequently,the standardization processes for future wireless systems have increasingly targeted atmaximal frequency reuse, where all cells use the same frequency everywhere [9].

Wireless channels are fundamentally impaired by fading, propagation loss, and interfer-ence. Two types of cooperative network that specifically address the issues of intercellinterference and path-loss:

• Base station cooperation: While in traditional networks base stations were operatingindependently, this type of cooperative network explores the possibility of coordin-ating multiple base stations. In these networks the transmission strategies amongthe multiple BS are designed jointly. In particular, the base stations may cooperatein their power, frequency, and rate allocations in order to jointly mitigate the effectof intercell interference for users at the cell edge.

• Relay cooperation: This type of cooperative network explores the use of relays toaid the direct communication between the base station and the remote subscribersin order to combat against the path-loss characteristic of wireless channel.

In both types of cooperative networks, resource allocation is expected to be a crucial issue.However, in this thesis we will focus on BS cooperation and address the different resourceallocation strategies regarding that.


In this part we consider multiple base station cooperative network which an OFDMAscheme is employed within each cell, and no two links in each cell can use the samesubcarrier at the same time. So, there is no intracell interference.

Given a fixed frequency and power allocation for all transmitters, the network optimizationproblem is that of coordinating the subcarrier assignment.

3.5.1 Single user water-filling

A single link in OFDM transmit power optimization problem has a classic solution knownas water-filling. For a single link problem where the noise and interference are assumed

29


to be fixed, the optimization of achievable rate subject to a total power constraint can beformulated as

maxN∑n=1

log(

1 + |h(n)|2pnΓσ2

)

s.t. :N∑n=1

pn ≤ ptotal,

0 ≤ pn ≤ pmax,

where the optimization is over pn, the transmit power on the frequency tone n. |h(n)|2 isthe channel path-loss and the combined noise and interference σ(n)2 are assumed to befixed. The Lagrangian dual of this optimization problem is

L(pn, λ) =N∑n=1

log(

1 + |h(n)|2pnΓσ(n)2

)− λ

(N∑n=1

pn − ptotal

).

The constrained problem is now reduced to an unconstrained one in which λ can beinterpreted as power price. Setting the derivative of the above Lagrangian yields :

pn =[

1λ− Γσ2(n)|h(n)|2

]pmax

0,

where [.]ba denotes a limiting operation with lower bound a and upper bound b. Thefundamental reasons that an (almost) analytic and exact solution exists for this problemare that the objective function of the optimization problem is a concave function of theoptimization variables and the constraints are linear. Therefore, convex optimizationtechniques such as Lagrangian dual optimization can be applied.

In practice the exact values of power is not important because the water-filling relationoperates on a linear scale on pn, while the rate expression is a logarithmic function, whichis not sensitive to the exact value of pn when it is large.

3.5.2 Multi-user water-filling

In a cellular setting, whenever a particular cell implements power adaptation, it changesits interference pattern on its neighbours. Therefore, when every cell implements water-

30


filling at the same time, the entire network effectively reaches a simultaneous water-fillingsolution, where the optimal power allocation in each cell is the water-filling solution againstthe combined noise and interference from all other cells. Such a simultaneous water-fillingsolution can typically be reached via an iterative water-filling algorithm in a system-levelsimulation where the water-filling operation is performed on a per-cell basis iteratively[38] which has been observed to converge.

3.5.3 Rate region maximization

One of the useful approaches to tackle with the nonlinear utility function of links is toreduce the objective function to weighted sum rate optimization which is essentially lin-earization of the objective function and decouples that on a per-tone basis. This techniquesimplifies the problem significantly.

A key technique for achieving decoupling is to employ the Lagrangian duality theory. Forexample, consider the following weighted rate sum maximization problem subject to thepower constrains

maxK∑k=1

ωkRk

s.t.N∑n=1

pk(n) ≤ pk,total,

where

Rk =N∑n=1

log(

1 + |hkk(n)|2pknΓ(∑l 6=k |hlk|2pln + σ(n)2)

)

pkn denotes the transmit power of user k in subcarrier n, and hkl(n) is the complex channelgain from the transmitter of user k to the receiver of user l. This problem can be solvedby dualizing with respect to the power constraint. This results in a dual function g(λk)defined as

g(λk) = maxpkn

{ωkRk − λk

(N∑n=1

pkn − pk,total

)}

Now, the optimization problem reduces to N per-tone problems:

maxpkn

{ωk log(1 + |hkk(n)|2pkn

Γ(∑l 6=k |hlk(n)|2pln + σ(n)2) − λkpkn

}.

31


Just as in single-user water-filling, where the solution to a convex optimization problemreduces to solving the problem for each λ, then finding the optimal λ, similar algorithmsbased on λ search can be applied here. The reduction to an N per-tone optimizationproblem ensures that the computational complexity for each step of this optimizationproblem with fixed λk is linear in the number of subcarriers. The maximum value ofthe original objective is equal to the minimum of the dual optimization problem whichmeans

maxK∑k=1

ωkRk = minλk≥0

g(λk)

The optimum λk can be found using search methods such as the ellipsoid method (whichis a generalization of bisection search to higher dimensions) or the subgradient method[39].

An interesting fact is that this duality technique remains applicable even when the func-tional form of the rate expression is nonconvex as is the case above, as long as the OFDMsystem has a large number of dimensions in the frequency domain, which allows an ef-fective convexification of the achievable rate region as a function of the power allocation[39, 21].

32

4 Resource allocation in downlink multi-cellOFDMA networks

In this chapter we intend to formulate resource allocation problem in the context of down-link multi-cell OFDMA network closely aligned with 3GPP LTE networks. The formulatedproblem is known to be NP (Non-deterministic Polynomial) hard and there is no computa-tionally ‘efficient’ (polynomial time) algorithm known for that [16]. Therefore, we suggestan iterative heuristic approach for user allocation and specifically subcarrier assignmentwith low computational cost and acceptable performance loss. we primarily intend toformulate a general problem with the following four assumptions.

• Assumption 1: Each subcarrier at each cell can be used by maximum one user servedby that cell, i.e., there is no intra-cell interference.

• Assumption 2: Each user can be served by one cell at maximum.

• Assumption 3: Neighboring cells might use the same sub-carriers.

• Assumption 4: Perfect channel state information at both receiver and transmitter isavailable during transmission.

In addition to these, we will make two assumptions to cast the problem, we want to deal,from the general problem. Throughout this chapter, we concentrate on a subcarrier as-signment procedure with the aim of maximizing data throughput of the system. The mainfocus of this chapter will be on providing mechanisms for adding or removing subcarriersand keeping the interference under control.

4.1 Problem formulation and system model

We investigate a solution for the multi user Rate Adaptive (RA) resource allocation prob-lem in multi cell scenario. Assume that we have an OFDMA cellular network in whichthe set of cells is denoted by I = {1, 2, ..., I}, the set of users K = {1, 2, ...,K} and theset of subcarriers by N = {1, 2, ..., N}. We have K users distributed in I cells with Nnumber of subacarriers available for each cell. The objective is to maximize sum over all

33

4 Resource allocation in downlink multi-cell OFDMA networks

data rates rikn which is the rate of user k in cell i given subcarrier n, if it is assigned, withsome constrains. The general problem formulation is:

maxI∑i=1

K∑k=1

N∑n=1

rikn

subject to:

C1: aikn ∈ {0, 1},∀i, k, n,

C2:I∑i=1

N∑n=1

rikn ≥ Rk,∀k,

C3:N∑n=1

pin ≤ Pi,∀i,

C4: pin > 0, ∀i, n,

C5:I∑i=1

aiknailn = 0,∀k, l = 1, ...,K, k 6= l,∀n

C6:K∑k=1

aiknajkm = 0,∀i 6= j,∀m,n = 1, ..., N,

where rikn = log2

(pingiknaikn∑

j 6=i pjngjkn(∑Kl=1 ajln) + σ2

in

+ 1), ∀i = 1, ..., I,∀k = 1, ...,K, ∀n =

1, ..., N,

The rate expression is obtained by the Additive White Gaussian Noise (AWGN) Shannonchannel capacity. pin is the power allocated to subcarrier n from the cell i. gikn is thechannel gain from cell i on user k and subcarrier n, and σ2

in is the noise power. aikn is anindicator which is one if the user k from cell i is using subcarrier n, and zero otherwise(C1). The received SINR of user k of cell i on subcarrier n can be expressed by:

SINRikn = pingikn∑j 6=i pjngjkn(

∑Kk=1 ajkn) + σ2

in

To maintain fairness, the achieved rate of each user is individually lower bounded in C2.C3 denotes that the power over all the subcarriers in each cell is limited to a certain value.C4 is to guarantee that the transmission power values are non-negative. C5 expresses thateach subcarrier has to be assigned to one user at maximum (Assumption 1). C6 denotesthat each user can be assigned to one cell at maximum (Assumption 2).

34


With no loss of generality we introduce three 2-dimensional variables instead of a 3-dimensional variable aikn to reduce the arithmetic complexity. In this line, aik and akndenote the association of user k to cell i and association of subcarrier n to user k respect-ively. Therefore, by definition aikn = aikakn.

We also define bin ,K∑k=1

aikn.

By substituting the defined variables, the new formulation is given as:

maxI∑i=1

K∑k=1

N∑n=1

log2

(pingiknaikakn∑

j 6=i pjngjknbjn + σ2in

+ 1)

subject to:

C1: aik, akn, bin ∈ {0, 1}, ∀i, k, n,

C2:I∑i=1

N∑n=1

rikn ≥ Rk,∀k,

C3:N∑n=1

pin ≤ Pi,∀i,

C4: pin > 0, ∀i, n,

C5:K∑k=1

aikakn = bin, ∀i, n

C6:I∑i=1

aik ≤ 1, ∀k,

However in the new formulation C5 and C6 are changed, they have the same functionalityas before.

• Assumption 5: In order to simplify the problem we assume that the power is distrib-uted equally among the subcarriers which necessarily means that there is no powercontrol. There is an option for transmit power either to take real positive value, ifit’s allocated to a subcarrier, or zero when no power is allocated to that subcarrier,essentially means the subcarrier is off. Thus, pin = Pi

Nbin ≥ 0, so that we can skip

C4.

35


• Assumption 6: We will also assume that the channel is not frequency selective andwe consider it as frequency flat channel which necessarily means gikn = gikm ∀n,m.Therefore, pingikn = Pi

Ngik.

If we reformulate the problem with new assumptions while substituting cik = PiNgik we

obtain:

maxI∑i=1

K∑k=1

N∑n=1

log2

(cikaikakn∑

j 6=i cjkbjn + σ2in

+ 1)

(4.1.1)

subject to:

aik, akn, bin ∈ {0, 1}, ∀i, k, n,

I∑i=1

N∑n=1

rikn ≥ Rk,∀k,

K∑k=1

aikakn = bin, ∀i, n

I∑i=1

aik ≤ 1, ∀k,

The complexity of the resource allocation problem is very high as the underlying problemof subcarrier allocation to users is of combinatorial nature and it is even more complexfor the case of multi cell scenario, when the objective function is nonlinear due to theinterference term. It is called nonlinear integer optimization problem and referred as aNP-hard (Nondeterministic Polynomial hard) combinatorial optimization problem with nostandard computationlly efficient optimization algorithm to obtain the optimal solution[16]. Some solution strategies such cutting plane and branch-and-bound algorithms havebeen proposed to cope with the certain classes of nonlinear integer programming problemsin [25]. However, it is hard to guarantee that these algorithms achieve good performanceover large scale problems with large number of constraints and variables. In this thesis wepropose a heuristic approach solving the issue of subcarrier assignment and user allocationwith low computational cost and acceptable performance loss.

4.1.1 System model

Looking precisely at problem (4.1.1) aik and akn are independent variables (bin is depend-ent) which are nothing but user allocation to cells and subcarrier assignment to users

36


respectively. Regarding the user allocation we assign users to cells with strongest receiv-ing signal. The main problem in our context is subcarrier assignment to users. Figure4.1 illustrates the main building blocks of subcarrier assignment and peripheral blocks forenhancements.

Cell Assignment

&

Initialization

Alg. 1

Subcarrier Assignment

Alg. 2

Decrease Pre-

Assigned

Subcarriers

Alg. 3

Increase Pre-

Assigned

Subcarriers

Alg. 5 (or 6,7)

Cost-Benefit Analysis

Alg. 4

Compute Current Rates

Compute Potential Rates

Compute Achievable Rates

Compute Gains

Figure 4.1: Block diagram of the whole process

We propose two general strategies in serving users which makes the overall proceduredifferent.

First strategy is aimed at considering the subcarrier assignments in previous iterationsonly as side information to promote assignments in upcoming iterations.

Second strategy is aimed at continuing the subcarrier assignment in each iteration whilekeeping those assigned in previous iterations.

There are two differences in the implementation of these strategies. One lies on initializa-tion of subcarrier assignment block and the other in the necessity of implementing release

37


subcarrier algorithm. Nevertheless, both strategies have the building blocks in Figure 4.1in common. Throughout the next sections we describe the functionality of each block indetail.

4.2 Cell assignment and initialization

In this section we address the user allocation to base station as a primary step for resourceallocation. From now on, we use the BS or cell terms interchangeably. Initially we assigneach user to a cell by using a simple greedy algorithm. In this algorithm we assign celli with best SINR to user k. The best cell refers to the cell which can provide maximumSINR for the user in the situation that all the subcarriers are occupied by all the cells.

SINRik = cik∑j 6=i cjk + σ2

i

As a remark in SINR formulation, the full interference case, where all the subcarriers arebeing used by all the cells, is considered. Consequently bjn = 1 ∀n, j. The procedure ofthe cell assignment is represented in Algorithm 1.

Algorithm 1 Cell Assignment (SINR)Input: cik, σ2

i ∀i, kfor all users k do

ik ← arg maxi

SINRik = cik∑j 6=i cjk + σ2

iaik ← 1

end forreturn (ik)k∈K . later on, we use ik as cell of user k if aik = 1.

4.3 Frequency Reuse Schemes

In OFDMA systems, if two neighbouring cells use the same subcarrier, transmission in onecell interferes with that in the other cell which is known as Inter Cell Interference (ICI). Toovercome ICI problem, the system can employ techniques like spectrum spreading [3, 19]and multiple-receiver based interference suppression [7]. Frequency Reuse Factor (FRF)is a widely accepted approach to design channel allocation such that the two neighboringcells allocate subcarriers exclusively. In other words, the neighboring cells will not sharesame frequencies.

38

4.4 Subcarrier Assignment

For OFDMA systems FRF 1, which essentially means all the cells use the same frequencyband, would be the best choice. However, it has the problem that some users near celledge cannot get service due to the SINR degradation caused by ICI [33, 37]. In [5] severalFRF like conventional integer numbers i.e., 1, 3, 4, or 7 are considered and the possibilityof using some non-integer FRFs such as 7/4 and 7/3 is investigated.

In order to improve the interference situation for the cell edge users we propose FRF 3as the first step of subcarrier assignment. In this line, we divide the available bandwidthinto three subbands, each will be assigned to one cell. Subcarriers in each subband areassumed to be ‘pre-assigned’. The main advantage of considering the so called pre-assignedsubcarriers is that the base station can know the interference situation over the subcarriersahead of the transmission.


In [6] it is suggested that in a single user water-filling solution, the total data rate of azero margin system is close to capacity even with flat transmit Power Spectral Density(PSD) as long as the energy is poured only into subchannels with good channel gains.This important result completely eliminates the major step of power allocation concen-trate mainly on subcarrier allocation. The reason is that in multiuser OFDM systems, aflat transmit power might also perform well is that it is assumed due to multiuser diversity,only subchannels with good channel gains are assigned to each user [27]. Nevertheless,in this thesis we neglect frequency-selective fading of channel and assume all the channelgains equal with respect to the subcarriers. Herein the issue is selecting subcarriers withminimum receiving interference from the neighbouring cells so as to provide maximumdata rate. Along this goal we will propose an algorithm aiming at assigning the subcar-riers which are less affected by the interference of the neighboring cells. Before enteringthat we explain briefly the frequency reuse schemes and it’s advantages in mitigating theinterference.

The subcarrier assignment is based on pre-assigned subcarriers. we define matrix A withsize I ×N as ‘pre-assigned’ subcarriers to the cells with the following characteristic.

αin ={

1 if subcarrier n is pre-assigned to cell i0 otherwise

Initially we set the values in matrix A according to the frequency reuse scheme 3, whichnecessarily means that one third of whole subcarriers may be assigned to one of threeneighboring cells in a way that no two neighboring cells share the same subcarriers. Thedistribution of subcarriers is shown in Figure 4.2. This way, the number of cells usinga special subcarrier is divided by three. Thus, the maximum spectral efficiency will be

39


Figure 4.2: Frequency reuse scheme 3 [5]

divided by three primarily. The idea behind pre-assignment consideration is to keep theinterference situation of system under control, so that the subcarriers can be assignedwithout having concerns about the immediate changes in the interference.

The main characteristic which distinguishes the first strategy from the second is the fol-lowing initialization in subcarrier assignment phase.

{akn = 0 ∀k, nbin = 0 ∀i, n

Setting these variables to zero means that in each iteration we start assigning subcarri-ers from scratch. In fact, the pattern of pre-assigned subcarriers is the thing which reallymatters to us. Now, we concentrate on subcarrier assignment regardless of the strategy wechose cause they don’t differ. In the subcarrier assignment algorithm we start with com-puting the SINR for all users and subcarriers. Notice that the SINR values are computedbased on the pre-assigned subcarriers which is given already. Thus, during the subcarrierassignment the interference situation of the system doesn’t change. By using sinr2rate()function we obtain the achievable rate for each user over each subcarrier.

sinr2rate() function maps SINR values to the rate values using Table 4.1. According toLTE system specification, 16 Channel Quality Indicators (CQI) are distinguished. EachCQI corresponds to a supported modulation scheme and code rate for downlink transmis-

40


sion. The system link budget specification defines what SINR level and what ReceiverReference Sensitivity (RS) level is required to support minimum DL throughput of 95% ofthe maximum possible at different CQIs. Table 4.1 shows the requirements assuming 10MHz transmission bandwidth [28] for RS level specification. RS considers thermal noise(-174 dBm/Hz) multiplied by transmission bandwidth, receiver noise figure (9dB), an im-plementation margin (2.5 dB for QPSK, 3 dB for 16-QAM and 4 dB for 64-QAM), anddiversity gain (-3 dB).

CQI modulation code rate efficiency[bps

Hz

]SINR[dB] RS level [dBm]

0 out of range

1 QPSK 1/8 0.25 -5.1 -101.1

2 QPSK 1/5 0.40 -2.9 -98.9

3 QPSK 1/4 0.50 -1.7 -97.7

4 QPSK 1/3 0.66 -1.0 -97.7

5 QPSK 1/2 1.00 2.0 -97.0

6 QPSK 2/3 1.33 4.3 -94.0

7 QPSK 3/4 1.50 5.5 -91.7

8 QPSK 4/5 1.60 6.2 -90.5

9 16-QAM 1/2 2.00 7.9 -89.8

10 16-QAM 2/3 2.66 11.3 -87.6

11 16-QAM 3/4 3.00 12.2 -84.2

12 16-QAM 4/5 3.20 12.8 -83.3

13 64-QAM 2/3 4.00 15.3 -79.2

14 64-QAM 3/4 4.50 17.5 -77.0

15 64-QAM 4/5 4.80 18.6 -75.9

Table 4.1: SINR and RS requirements for 10 MHz transmission bandwidth.

After finding corresponding rates, we sort the users based on their rate demand so that ineach cell the user with minimum rate demand will be served first. The idea of subcarrierpre-assignment enables us carry out subcarrier assignment in cells in parallel. The ideabehind that is while assigning subcarriers to a user in a cell, the other users in the samecell will not be affected because there is no inter-cell interference. This eliminate the needof recalculating SINR values for a single assignment which is computationally expensive.

The approach is users will either be served completely such that their rate demand be fullysatisfied, or they will not be served. In our fixed interference scenario, the possibility thata user could be served or not, is determinable beforehand. This is because we have the

41


interference over any subcarrier with respect to pre-assigned subcarriers which is alreadyfixed. This essentially means the SINR of any subcarrier is known a prior and it doesn’tchange during the assignments.

SINRkn = cikkαikn∑j 6=ik cjkαjn + σ2

ik

∀k, n

Therefore we simply have the rate each subcarrier can potentially achieve by sinr2rate()function. We ensure the possibility of serving user l if the following inequality holds.

N∑n=1

AchievableRate(l, n) ≥ Rl

Then among users that inequality holds true, a subcarrier which provides maximum ratewill be assigned to the one with minimum rate demand. Once a subcarrier is assigned to auser, the rest of users in that cell can not occupy that subcarrier as stated in assumption1. We repeat this procedure for all users to fulfill their rate demand if possible. Thealgorithm of subcarrier assignment is represented in Algorithm 2 with more details.

42


Algorithm 2 Subcarrier AssignmentInput: cik, αin, aik ∀i, k, n

1: SINRkn ←cikkαikn∑

j 6=ik cjkαjn + σ2ik

∀k, n . Compute the SINR based on pre-assigned

subcarriers (α)2: AchievableRate = sinr2rate(SINR)3: for all cells i do4: Find the users in cell i5: Sort the users based on their rate demands in ascending order.6: for all users l in cell i do7: Rate(l)← 0 . Rate(l) is current rate of user l8: if

∑n

AchievableRate(l, n) ≥ Rl then

9: while Rate(l) < Rl do10: (n∗,maxrate)← max

nAchievableRate(l, n)

11: bin∗ ← 1 . Indicates subcarrier n∗ is assigned to cell i12: Rate(l)← Rate(l) +maxrate13: aln∗ ← 1 . Indicates subcarrier n∗ is assigned to user l14: for all users l in Cell i do15: AchievableRate(l, n∗)← 0 . Subcarrier n∗ should no longer be

available for the users in cell i16: end for17: end while18: else Add i to OverLoadedCells19: end if20: end for21: end for

return akn, bin, OverLoadedCells

43


4.5 Modifications in pre-assigned subcarriers

The outcome of subcarrier assignment shows that the available resources is not fully util-ized and there are some users whose rate demands are not satisfied. Although we havecontrolled the interference issue by employing frequency reuse scheme 3, the ideal FRF forwireless OFDMA systems is 1. In this line we carry out modifications to the pre-assignedsubcarriers. The fact that users with heterogeneous traffics are not uniformly distributedamong the cells makes the cells overloaded with excessive rate demand. Thus the needto change the pre-assigned subcarriers dynamically according to the user’s rate demandseems to be essential. Strategies to cope with lack of resources in overloaded cells will beproposed in the next sections.

4.5.1 Decrease pre-assigned subcarriers

There are some cells in the system which are able to serve all their assigned users an haveextra pre-assigned subcarriers. The idea is to release some of their pre-assigned subcarriersin order to eliminate their interference in the further subcarrier assignment. We remindthat the SINR of a subcarrier is computed based on pre-assigned subcarriers. It can beeasily seen that real SINR obtained by really assigned subcarriers is lower bounded by theSINR computed based on pre-assigned subcarriers. Because,

∀i, n bin ≤ αin ⇒ cikk∑j 6=ik cjkαjn + σ2

ik

≤ cikk∑j 6=ik cjkbjn + σ2

ik

In order to mitigate the interference we release a portion of the pre-assigned subcarrierswhich are not assigned (αin = 1, bin = 0) from cells serving all their users. A decision onreleasing a subcarrier will be made by considering the induced interference of a subcarrierto the users using that subcarrier in the neighboring cells. A subcarrier with maximuminducing interference (in case it’s assigned to a user) is a release candidate. The proposedalgorithm for decreasing pre-assigned subcarrier is shown in Algorithm 3. Here we intro-duced a parameter β ∈ [0, 1] to regulate the number of subcarriers going to be released.

4.5.2 Cost benefit analysis of adding subcarriers

Since there are users which do not have enough assigned subcarrier, we extend pre-assignedsubcarriers in cells that are heavily loaded. In fact, the decision of adding subcarriersshould be taken cleverly. Because adding a subcarrier without considering imposed in-terference on the other cells might cause the system increasingly unstable. To avoid the

44


Algorithm 3 Decrease pre-assigned subcarriersInput: akn, αin, bin, ServingCells, η

1: for all Serving Cells i do2: for all subcarriers n do

3: InducedInterference(n) =K∑k=1

cikakn(αin − bin)

4: end for

5: for t = 1 :⌈η

(N∑n=1

αin − bin

)⌉do

6: n∗ ← argmaxn

InducedInterference(n)7: αin∗ ← 0 . release subcarrier inducing maximum interference8: InducedInterference(n∗)← 09: end for

10: end forreturn αin

consequences of system instability, we perform a cost benefit analysis for each subcarrierin order to ensure a specific subcarrier is beneficial in terms of providing data rate. Sofar, not-served users and overloaded cells are identified. The Algorithm 4 investigates theinfluence of adding a subcarrier on the whole system. The cost benefit analyzer algorithm(Algorithm 4) includes the following steps:

STEP 1 The purpose is to derive the minimum rate each subcarrier can achieve amongall the unserved users. To pursue this aim we assume not-pre-assigned subcarriers (1 −αin, ∀i, n) as pre-assigned subcarriers and compute SINR values based on them. Asa result the achievable rate of subcarriers will be obtained by using sinr2rate() function.Next step is to find the minimum value of achievable rate over each subcarrier by unservedusers in the same cell. In other words, this gives us the minimum benefit we gain in termsof data rate if we assume that subcarrier as a pre-assigned one.

STEP 2 In this step the current rate of users over all subcarriers is computed. This issimply done by computing SINR of any user over any subcarrier based on current pre-assigned subcarriers.

STEP 3 We compute the expected rates of subarriers under the condition that all subcar-riers in an unserved cell become pre-assigned subcarrier (FRF 1). There are some remarksregarding the above two steps.

Remark 1 fixing a specific user from overloaded cell, the rates correspond to newly pre-assigned subcarriers (αin = 0 ∧ αin = 1) in step 2 and 3 are zero cause those subcarriersweren’t occupied by the user.

45


Algorithm 4 Cost benefit analyzerInput: cik, akn, αin, ∀i, k, n

1: for all overloaded cells i do2: for all subcarriers n do3: for all unserved users k do4: SINRkn = cik(1− αin)∑

j 6=i cjkαjn + σ2i

5: end for6: AchievableRates = sinr2rate(SINR)7: MinRate(n)← min

k(AchievableRates(k, n))

8: end for9: for all users l do

10: SINRln = cilalnαiln∑j 6=il cjlαjn + σ2

il11: end for12: CurrentRates = sinr2rate(SINR)13: α← α14: αin ← 1, ∀n15: for all users l do16: SINRln = cilalnαiln∑

j 6=il cjlαjn + σ2il

17: end for18: PotentialRates = sinr2rate(SINR)19: ∆r ← CurrentRates− PotentialRates20: MinGain(n)←MinRate(n)−

∑k

∆rkn

21: end forreturn AchievableRates(k, n),MinGain(n),∆r(n) ∀k, n

46


∀l ∈ i,∀n if (αin = 0 ∧ αin = 1)⇒ CurrentRate(l, n) = PotentialRate(l, n) = 0

Remark 2 fixing a specific user from overloaded cell , the rates correspond to already pre-assigend subcarriers (αin = 1 ∧ αin = 1) in step 2 and 3 are the same cause there is nochange in interference situation for those subcarriers.

∀l ∈ i,∀n if (αin = 1 ∧ αin = 1)⇒ CurrentRate(l, n) = PotentialRate(l, n)

Remark 3 the rates obtained in PotentialRates for users out of aforementioned cell areless or equal to rates in CurrentRate. Because, the induced interference of newly addedsubcarriers affects their rates negatively.

∀l 6∈ i,∀n if ⇒ PotentialRate(l, n) 6 CurrentRate(l, n)

The equality holds for the case user l doesn’t occupy subcarrier n (aln = 0) then bothrates are zero.

STEP 4 the last step is to obtain the difference between CurrentRates and PotentialRateswhich is always non-negative for all subcarriers due to aforementioned remarks. These val-ues (∆r) are losses in user’s data rate that system suffers per subcarrier if a subcarrierbecomes pre-assigend. In step 1 we obtained the minimum rate that system can achieveper subcarrier. Therefore, we subtract the loss in rates from the minimum achievable ratein step 1 so as to obtain the real (net) gain.

Based on this criterion we decide if a subcarrier is beneficial to be added or not. By per-forming experiments we find a threshold for assessing subcarriers and filter the subcarrierswith gain lower than the threshold.

4.5.3 Increase pre-assigned subcarriers

In previous section we established a criterion to eliminate the subcarriers that inflictsignificant loss in data rates of users. In order to increase pre-assigned subcarriers weselect the required number of subcarriers from the remaining ones which are beneficial forthe users altogether. For this phase we propose three approaches which have the numberof subcarriers to be added in common. The first approach is to take advantage of thecriterion obtained in previous section. The procedure of this approach is represented inAlgorithm 5.

As we have not pre-assigned sufficient number of subcarriers in an overloaded cell we tryto add minimum number of subcarriers to fulfill the rate demand of unserved users. Toestimate a lower bound of required number of subcarriers we consider the case that there isno interference on the subcarrier. Consequently, we compute SNR for each subcarrier and

47


obtain its data rate using sinr2rate() function. As we do not have frequency selectivity,the SNR value for the subcarriers are equal.

SNRk = cikkσ2ik

Thus, the minimum number of subcarriers required for each user k is:

LowBoundDemand(k) =⌈

Rksinr2rate(SNRk)

⌉The tuning parameter α is introduced to regulate the required number of subcarriers. Inthe next step, we make use of the criterion enables us to assess the subcarriers. For eachunserved user we select NumofReqSc from subcarriers with maximum Gain and addthem to pre-assigned ones.

Algorithm 5 Increase pre-assigned subcarriers based on cost-benefit analysisInput: µ, cik, αik, AchievableRates(k, n),∆r(n) ∀i, k, n

1: for all overloaded cells i do2: for all unserved users k in cell i do3: SNRk ←

cikσ2i

4: rk = sinr2rate(SNRk) . rk is reference data rate for all subcarriers5: LowBoundDemand(k)←

⌈Rkrk

⌉6: Gain(n)← AchievableRates(k, n)−

K∑l=1

∆rln, ∀n

7: SortedGain = Sort(Gain) in descending order8: NumofReqSc← dµ.LowBoundDemand(k)e . µ is tuning parameter9: m, counter ← 1

10: while (counter ≤ NumofReqSc ∧m ≤ N) do11: if (SortedGain(m) ≥ Threshold ∧ αim = 0) then12: αim ← 1 . pre-assign subcarrier m13: counter ← counter + 114: end if15: m← m+ 116: end while17: end for18: end for

return Gain(n),∆r(n) ∀n

The second approach takes into account the minimum received interference over subcarri-ers. This approach simply selects the subcarriers which are less interfered by neighboringcells and add them to pre-assigned subcarriers. The receiving interference that user ktolerates over subcarrier n is:

ReceivingInterference(k, n) =I∑

j 6=ik

cjkαjn

48


The algorithm which increase pre-assigned subcarriers based on minimum received inter-ference criterion is shown in Algorithm 6.

Algorithm 6 Increase pre-assigned subcarriers based on minimum received interference(2nd approach)Input: cik,MinGain(n) ∀i, k, n

1: for all overloaded cells i do2: for all unserved users k in cell i do3: SNR(k)← cik

σ2i

4: rk = sinr2rate(SNRk) . rk is reference data rate for all subcarriers5: LowBoundDemand(k)←

⌈Rkrk

⌉6: ReceivingInterference(k, n)←

I∑j 6=ik

cjkαjn ∀n

7: [SortedRI, Index] = Sort(ReceivingInterference) in ascending order8: NumofReqSck ← dµ.LowBoundDemand(k)e . µ is tuning parameter9: idx, counter ← 1

10: while (counter ≤ NumofReqSck ∧ idx ≤ N) do11: m← Index(idx)12: if (MinGain(m) ≥ Threshold ∧ αim = 0) then13: αim ← 1 . pre-assign subcarrier m14: counter ← counter + 115: end if16: idx← idx+ 117: end while18: end for19: end for

return αin

We propose the third approach based on minimum inducing interference. This approachsimply selects the subcarriers inducing less interference to neighboring cells (if they becomepre-assigned) and add them to pre-assigned subcarriers. The interference that cell i mayinduce to users in neighboring cells over subcarrier n is:

InducingInterference(n) =K∑l=1

cilaln, ∀i, n

The algorithm which increases pre-assigned subcarriers based on minimum inducing in-terference criterion is shown in Algorithm 7.

So far, we have introduced the fundamental blocks required for subcarrier assignment andthe proposed enhancements.

49


Algorithm 7 Increase pre-assigned subcarriers based on minimum inducing interference(3rd approach)Input: cik,MinGain(n) ∀i, k, n

1: for all overloaded cells i do2: Find unserved users k in cell i3: SNRk ←

cikσ2i

4: rk = sinr2rate(SNRk) . rk is reference data rate for all subcarriers5: NumofReqSc← [µ.

∑k

Rkrk

] . α is tuning parameter

6: InducingInterference(n)←K∑l=1

cilaln ∀n

7: [SortedII, Index] = Sort(InducingInterference) in ascending order8: idx, counter ← 19: while (counter ≤ NumofReqSc ∧ idx ≤ N) do

10: m← Index(idx)11: if (Gain(m) ≥ Threshold ∧ αim = 0) then12: αim ← 1 . pre-assign subcarrier m13: counter ← counter + 114: end if15: idx← idx+ 116: end while17: end for

return αin

50

4.6 Release subcarriers

4.6 Release subcarriers

Regarding the second strategy introduced in the beginning of this chapter, the idea is totake advantage of previously assigned subcarriers to decrease computational complexitiesin the current round of assignment. An important issue raises here is while changing the in-terference situation by modifying pre-assigned subcarriers in each iteration the subcarrierswhich were assigned in previous iterations might become excessive or insufficient for ratedemand fulfillment. This reflects the need for releasing some of already assigned subcarri-ers for further assignments. Thus, we propose release subcarrier algorithm for users withover fulfilled rate demand. The purpose is to release maximum possible number of sub-carriers from over fulfilled users without causing rate reduction below the user’s minimumrate demand. Algorithm 8 shows subcarrier release procedure in details. We finish this

Algorithm 8 Release subcarrierInput: cik, bin, akn, αin, Rk, Rate(k) ∀i, k, n

1: Find users l such that 0 < Rate(l) < Rl2: for all users l do3: Find subcarriers m such that alm = 14: alm ← 05: bill ← 06: Rate(l)← 07: end for8: for all cells i do9: SINRkn ←

cikαinakn∑j 6=i cjkαjn + σ2

i

10: Rate(k)←N∑n=1

sinr2rate(SINRkn), ∀k

11: for all users k do12: (MinRate, n∗)← min

{n|bin 6=1}Rates(k, n)

13: while Rate(k)−MinRate ≥ Rk do14: Rate(k)← Rate(k)−MinRate15: bin∗ ← 016: akn∗ ← 017: (MinRate, n∗)← min

{n|bin 6=1}sinr2rate(SINRkn, Rate, akn)

18: end while19: end for20: end for

return bin, Rate(k), akn ∀i, k, n

chapter with illustration of block diagram of second strategy in Figure 4.3. Notice that,the release subcarrier block is placed before subcarrier assignment to turn off unnecessaryassigned subcarriers either from over-fulfilled or not fully served users and consequently

51


the next block (subcarrier assignment) would be able to function effectively.

Cell

Assignment

&

Initialization

Alg. 1

Subcarrier

Assignment

Alg. 2

Decrease Pre-

Assigned

Subcarriers

Alg. 3

Increase Pre-

Assigned

Subcarriers

Alg. 5 (or 6,7)

Cost-Benefit Analysis

Alg. 4

Compute Current Rates

Compute Potential Rates

Compute Achievable Rates

Compute Gains

Release

Subcarrier

Alg. 8

Figure 4.3: Block diagram of second strategy with subcarrier release phase

52

5 Performance evaluation

5.1 Simulation Framework

In this chapter, the simulation setup is described before presenting and discussing thesimulation results. In order to evaluate the algorithms proposed in Chapter 4, we takeadvantage of a simulated OFDMA network which is properly aligned with LTE 3GPPnetwork. The basic platform for simulation has been developed at the Institute of The-oretical Information Technology within the joint research and development project ‘SelfOrganization for 4G Multi-Tier Networks’ with Huawei Technologies. Our contribution isthe incorporation of radio resource management. This network consists of 21 hexagonalmacro cells (7 sites) and 500 users with heterogeneous traffics distributed randomly. Anexemplary network layout is shown in Figure 5.1.

Figure 5.1: Simulated network layout

In order to improve the interference situation for the cell edge users we will use FRF 3

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as first step of subcarrier assignment. In this line, we divide the available bandwidth intothree subbands, each will be assigned to one cell. As explained in subsection 2.6.3 thebasic unit in downlink transmission of LTE is the Physical Resource Block (PRB), whichcomprises 12 subcarriers with 15 kHz bandwidth each. The effective system bandwidth is9 GHz. Thus, there are 600 subcarriers available in each cell. System characteristics areshown in Table 5.1.

System parameter SettingTotal number of users 500

Number of hexagonal cells 21

Carrier frequency 2 GHz

Effective transmission bandwidth 9 MHz (50 PRBs)

Number of eNBs (macrocells) 12

Min.-Max. Tx power eNB 40 dBm - 46 dBm (optionally: switched off)

Propagation model eNB CORLA (raytracing), omnidirectional

eNB antenna gain 14 dBi

eNB noise figure 5 dB

UE antenna gain 0 dBi

UE Noise figure 9 dB

UE initial distribution random, uniformly distributed

Simulation area 2.5 km × 3.5 km, 5 m resolution

Table 5.1: System parameters for urban evaluation environment.

5.1.1 User Profile

For UE simulation, we consider two components that model user behavior: First, the UEtraffic profile describes the requested traffic type, i.e., type of service, data rate demandto meet QoS requirements, and the priority level. Table 5.2 shows the considered servicesand their proportions on overall traffic. We modified the traffic profile with respect tothe desired evaluation scenario. We consider priority one for all services. Therefore, thenumber of active UEs in the system equals the sum of their priorities.

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5.2 Numerical results

Service Data rate [kbps] Proportions [%]VoIP 128 40Web 128− 512 40Data 128− 1024 20

Table 5.2: UE traffic profile considered for evaluation.


Two main strategies proceeded in chapter 4 will be evaluated from different point ofviews. First strategy was aimed at considering the subcarrier assignments in previousiterations only as side information to promote assignments in upcoming iterations. Secondstrategy was aimed at continuing the subcarrier assignment in each iteration while keepingthose assigned in previous iterations. Simulations are made in both strategies to assessperformance of proposed algorithms in terms of indexes such as percentage of served users,spectral efficiency and number of assigned subcarriers.

• percentage of served users= | κ |K∗ 100 where | κ | is size of set κ = {k | Rate(k) >

Rk, k ∈ K} and K is the size of K (set of all users).

• spectral efficiency=I∑i=1

K∑k=1

N∑n=1

rikn/BW

• number of pre-assigned subcarriers=I∑i=1

N∑n=1

αin In chapter 4 we defined parameters

µ, η, Threshold which enables us to investigate dependency of system indexes topre-assigned subcarrier changes and improve system performance.

• η (in Algorithm 3) is tuning parameter which regulates the number of pre-assignedsubcarriers not being used anymore.

• µ (in Algorithm 5) is tuning parameter which regulates the number of subcarriersgoing to be pre-assigned.

• Threshold (Algorithm 5) subcarriers with gain above this Threshold will be thecandidates for pre-assigned subcarriers.

Experiments are performed to investigate percentage of served users with respect to vari-ations of the parameters. We have observed that there are fluctuates in our indexesdue to consecutive or nonconsecutive increase and decrease in pre-assigned subcarriers

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of particular cells. In order to keep the system stable, we adopted a policy to limit thetimes that a particular cell could decrease its pre-assigned subcarriers. Thus, we stopdecreasing pre-assigned subcarriers of cells where we had both decrease and increase inprevious iterations. Based on several realizations we observed, after some iterations thenumber of pre-assigned subcarriers will not change anymore which leaves system indexesunchanged. One can keep doing iterations for further assurance. Performance of firststrategy According to the first strategy in each iteration we start assigning subcarriersfrom scratch. The key aim of iterating the procedure is refining pre-assigned subcarriers.We run the algorithms for different values of µ and η to find intervals where the system in-dexes remain almost steady. In this experiment we suppose 30 iterations would be enoughfor system to become reasonably stable. Parameters Threshold, µ and η vary between{−1.5,−1,−0.5, 0, 0.5, 1}, [0.1, 1.5] and [0.05, 1] respectively. Figure 5.1 represents systemperformance in terms of percentage of served users (1st index) after 30 iterations. It showsthat more than 99% of users were served for certain µ, η intervals. Secondly, the resultsshow that for small negative and close to zero threshold the probability of serving moreusers is higher.

Figure 5.2: Percentage of served users with respect to Threshold, µ and η.

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Performance of second strategy According to the second strategy we keep the sub-carriers assigned in the previous iterations while extending pre-assigned subcarriers inoverloaded cells and contracting pre-assigned subcarriers in the serving cells. Further-more, subcarriers assigned to users where rate demands are either not fully satisfied orover fulfilled, will be released. The idea behind this strategy is to take advantage of sub-carriers previously assigned, so that in each iteration there exists some users with satisfiedrate demand and consequently less time is required for serving the remaining ones. In or-der to evaluate performance of this strategy we will perform similar experiment with thesame parameters. Parameters Threshold, µ and η vary between {−1.5,−1,−0.5, 0, 0.5, 1},[0.1, 1.5] and [0.05, 1] respectively. Figure 5.2 represents system performance in terms ofpercentage of served users (1st index) after 30 iterations. It shows that more than 99%of users were served for certain µ, η intervals. Secondly, the results show that for smallnegative and close to zero threshold the probability of serving more users is higher.

Figure 5.3: percentage of served users with respect to Threshold, µ and η.

Comparing Figures 5.1 and 5.2 we can observe that first strategy outperforms the secondone for Threshold = 0 and negative values close to zero. In order to evaluate performanceof first and second strategy in terms of spectral efficiency and number of pre-assignedsubcarriers we perform certain number iterations for specific µ and η. The result is shown

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in Figures 5.3 and 5.4 respectively. In this evaluation we set the parameters µ and ηto values which the system shows the best performance in average. i.e. on average, fordifferent Threshold values, performance of first strategy is maximum at µ = 0.6 andη = 0.75. According to the upper plot, spectral efficiency is higher for zero and smallnegative values of Threshold. Looking at Figures 5.4 (a) and (b) low Threshold valueresults in more pre-assigned subcarriers, but spectral efficiency is reduced significantly dueto excessive induced interference.

Figure 5.4: (a) Changes of average cell spectral efficiency in 30 iterations . (b) Changes ofpre-assigned subcarriers in 30 iterations.

On average, for different Threshold values, performance of second strategy is maximum atµ = 0.7 and η = 0.8. It can be seen from Figure 5.5 (a) and 5.5 (b) that for high Thresholdvalues base stations are not able to serve many users due to lack of subcarrier.

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Figure 5.5: (a) Changes of average cell spectral efficiency in 30 . (b) Changes of pre-assigned subcarriers in 30 iterations.

If we compare Figures 5.4 and 5.5 we conclude that second strategy brings more stability tospectral efficiency index and furthermore, it demands less iterations to achieve stability.

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6 Conclusion and Future work

Resource allocation problem for the OFDMA based networks, specially for the practicalsystems regarding heterogeneous traffic is one of the difficult and complex problems. Inthis thesis, the effective and affordable mechanisms to share the available resources in a3GPP LTE downlink multi-cell network are provided and investigated. Details for eachchapter is summarized as follows. Chapter 1 has presented the motivation, main researchworks, and outline of this thesis. Chapter 2 has provided the brief overview of LTE, es-pecially emphasized on the multiple access technology for downlink. The key parametersused for execution of resource allocation in LTE downlink are described as well. Chapter3 has addressed different scenarios in the context of OFDMA cellular networks such assingle- and multi-cell topology with single- or multi-user scenarios. It is also discussedabout network optimization problem which is nothing but coordinating the subcarrier as-signment given a fixed frequency and power allocation for all transmitters. Chapter 4 wasthe contribution of the thesis in the area of subcarrier assignment in multi-user multicellOFDMA network. We made positive contribution to the problem of subcarrier assignmentby proposing algorithms with reasonable running time and acceptable performance loss.The main algorithm starts assigning subcarriers based on FRF 3 in the first iteration thenit dynamically modifies assignments neglecting fixed frequency plan. We have establishedeffective mechanism of pre-assigned subcarriers to maintain interference of the systemstable during subcarrier assignment. In this regard, we proposed two main strategies.First strategy suggests performing subcarrier assignment in each iteration without consid-ering the previous assignments. Despite that, second strategy utilizes the assignments inprevious iterations. In Chapter 5 two strategies have been implemented and their advant-ages investigated. First strategy is relatively robust to changes of Threshold and giveshigh system performance for different µ and η values. Although, second strategy demandsextra time to perform release subcarrier, since there are less users to be served in eachiteration, system indexes will converge faster. Therefore, second strategy shows potentialfor being used in practical situation when the UEs are mobile and network should adaptquickly to the changes by performing minor updates in the assigned subcarriers.

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6 Conclusion and Future work

6.1 Future work

6.1.1 UE Mobility

Through the work, we have assumed that various parameters such as static user’s popu-lation, the channel gains between one user and one eNB equal on all subcarriers. In otherwords, in our model the users are static over time in the sense that no user can enroll intosystem or leave it. In practical systems, number of active users can however be highlydynamic because of user leaving and being admitted to access network. Moreover, channelquality may vary dramatically in environments involved a high degree of mobility. Underthose circumstances, the approaches coped with fixed scenarios might be no longer valid.The development of a new algorithm for resource allocation considering the realistic trafficand mobility models which user arrive in and leave out system, and the multipath fadingmodel utilized to generate fading profiles for our multi-cell resource allocation procedure,is referred to as our first challenging future work.

6.1.2 Load balancing

Like GSM and WCDMA, LTE has still been influenced seriously by the problem of loadimbalance defined when the load of cells are not uniformly distributed. When the loadsamong cells are not balanced, in heavily loaded cells there is not enough resources toserve users, while their neighboring cells may have resources not fully utilized. Therefore,load imbalance deteriorates the performance of system and is a severe problem existingin 3GPP LTE networks. This provokes us to propose the framework of load balancing tofind triple consisting of a source cell, a neighbor cell, and a user for handover.

6.1.3 Femtocell and Macrocell Deplpyment

In our model, the considered architecture of network is the employment of macrocellsonly. Nevertheless, the architecture of 4th generation multi-tier networks, for instance,the network with femtocell and marcocell deployment, has deployed widely. Future workcan extend in multi-wireless networks by the means of taking interference between layoutsinto account instead of only inter-cell interference.

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