Physical-layer cooperative interference mitigation techniques for wireless heterogeneous systems

Universidade de Aveiro Departamento deElectr´onica, Telecomunica¸c˜oes e Inform´atica 2018

Syed Saqlain Ali

T´

ecnicas Cooperativas de Elimina¸

c˜

ao de

interferˆ

encia ao N´ıvel da Camada F´ısica para

Sistemas Heterog´

eneos sem Fios

Physical-Layer Cooperative Interference Mitigation

Techniques for Wireless Heterogeneous Systems

Universidade de Aveiro Departamento deElectr´onica, Telecomunica¸c˜oes e Inform´atica 2018

Syed Saqlain Ali

T´

ecnicas Cooperativas de Elimina¸

c˜

ao de

interferˆ

encia ao N´ıvel da Camada F´ısica para

Sistemas Heterog´

eneos sem Fios

Physical-Layer Cooperative Interference Mitigation

Techniques for Wireless Heterogeneous Systems

Disserta¸c˜ao apresentada `a Universidade de Aveiro para cumprimento dos requesitos necess´arios `a obten¸c˜ao do grau de Doutor em Telecomunica¸c˜oes, realizada sob a orienta¸c˜ao cient´ıfica do Doutor Ad˜ao Silva, Professor Aux-iliar do Departamento de Electr´onica, Telecomunica¸c˜oes e Inform´atica da Universidade de Aveiro e co-orienta¸c˜ao do Doutor At´ılio Gameiro, Professor Associado do Departamento de Electr´onica, Telecomunica¸c˜oes e Inform´atica da Universidade de Aveiro.

O j´uri / The jury

presidente / president Professor Doutor Jo˜ao Carlos de Oliveira Matias

Professor Catedr´atico da Universidade de Aveiro

vogais / examiners committee Doutor Ant´onio Jos´e Nunes Navarro Rodrigues

Professor Auxiliar da Universidade de Aveiro

Doutor An´ıbal Jo˜ao de Sousa Ferreira

Professor Associado da Universidade do Porto

Doutor Doutor Rui Miguel Henriques Dias Morgado Dinis

Professor Associado com Agrega¸c˜ao, Facultade de Ciˆencias e Technologia, Univer-sidade Nova de Lisboa

Doutor Ant´onio Jos´e Castelo Branco Rodrigues

Professor Auxiliar da Universidade T´ecnica de Lisboa

Doutor Ad˜ao Paulo Soares Silva

Agradecimentos / Acknowledgements

Foremost, I would like to express my sincere thanks to my supervisor Prof. Ad˜ao Paulo Soares da Silva and co-supervisor Professor At´ılio Gameiro for giving me the opportunity to be part of his research group and supervising my Ph.D. thesis. I have learned and benefited from their great intuition and deep technical insight. They always supported my work and shared their knowledge which yielded to quite interesting research directions. I am also thankful to my supervisory committee for reviewing my PhD thesis, and providing their valuable feedbacks.

I wish to thank Dr. Daniel Castanheira for guiding me towards my initial stage as a Ph.D student. He always supported my work and shared his knowledge, his work quality and passion for research have been essential to motivate and encourage me since I joined the group. I learned from him many technical and analytical skills required to conduct scientific research. He always clarified my endless doubts and encouraged me during these challenging years. Without his inspiring ideas, guidance and valuable feedbacks, it was not possible to accomplish this work.

I would like to extend my thanks to Professor Elvino Sousa and Dr. Ahmed Alsohaily for giving me the opportunity to join the Wireless Lab as a visiting researcher at University of Toronto, Canada. I would also like to thank Eduardo Casta˜neda for his motivational words and technical discussions. I acknowledge Instituto de Telecomunica¸c˜oes, University of Aveiro and MAP-TELE program committee for providing excellent working conditions, and Funda¸c˜ao para a Ciˆencia e a Tecnologia (FCT) for its support and funding. My thanks also extends to Ms. Suzana and Sandra for their administrative support. I also thank my friends Bilal Hussain, Rizwan Asghar, Jose Quevedo, Ali Awan, Arsalan, Smriti for their help, support and motivation throughout my PhD. I would also like to extend my thanks to other colleagues at the MOBNET research group and other IT members for the valuable talks and fruitful discussions.

Last, but not least, I would like to dedicate this work and to thank my parents, Syed Sadiq Hussain and Farhat Hussain; without their resolute patronage I would not have reached this standing, and to my wife Kehkashan, for her invaluable love and support during both good and not so good days and all my brothers and sisters, specially my elder brother Dr. Sajjad Hussain for his kind support and motivation throughout my educational career.

Syed Saqlain Ali Aveiro, May 2018

Palavras-chave Redes Heterog´eneas, c´elulas macro, c´elulas pequenas, inter-ferˆencia/alinhemento do sinal, codifica¸c˜ao de rede f´ısica, codifica¸c˜ao por blocos no espa¸co-frequˆencia, OFDM, MIMO

Resumo O tr´afego m´ovel com origem em redes celulares est´a a aumentar exponen-cialmente, principalmente devido ao uso de servi¸cos de dados como o v´ıdeo. Uma froma efetiva de lidar com essas exigˆencias ´e reduzir o tamanho da c´elula, implementando c´elulas pequenas (SCs), ao longo da ´area de cober-tura do atual sistema macro-c´elular. A implementa¸c˜ao de SCs melhora a cobertura de forma significativa. No entanto, como as licen¸cas de espectro adicionais s˜ao dif´ıceis e caras de adquirir, espera-se que a macro e as pe-quenas c´elulas possam coexistir sob o mesmo espectro. A coexistˆencia dos dois sistemas resulta em interferˆencias entre eles. Neste contexto, esta tese foca-se no projeto de v´arias t´ecnicas de mitiga¸c˜ao de interferˆencia em redes heterog´eneas (HetNets) sob requisitos de coordena¸c˜ao limitados.

A primeira parte da tese foca-se no projeto de v´arias t´ecnicas baseadas no alinhamento de interferˆencia (IA) para o sentido descendente do sistema heterog´eneo. Mais especificamente, s˜ao propostos esquemas baseados no alinhamento de interferˆencia com diferentes n´ıveis de coordena¸c˜ao inter-sistema e a restri¸c˜ao de que o desempenho do sistema macro-c´elula ´e man-tido pr´oximo do caso em que o sistema SCs ´e desligado.

A segunda parte da tese centra-se no projeto conjunto de t´ecnicas baseadas no IA e c´odigos por bloco no espa¸co-frequˆencia (SFBCs) para o sentido descendente. Mais especificamente, ´e apresentado o projeto do esquema de IA com SFBCs orientado para se obter diversidade. A principal motiva¸c˜ao para o projeto conjunto do IA com SFBCs, ´e permitir a coexistˆencia dos dois sistemas, considerendo uma pequena troca de informa¸c˜ao entre sis-temas. As c´elulas pequenas apenas precisam de saber que o SFBC ´e usado pelo sistema macro-celular, n˜ao sendo necess´aria a troca de nenhum canal inter-sistema, contrariamente aos outros esquemas propostos na primeira parte da tese.

A parte final da tese apresenta a aplica¸c˜ao do alinhamento de sinal (SA) e codifica¸c˜ao de rede f´ısica (PNC) para a liga¸c˜ao ascendente do sistema het-erog´eneo. A principal motiva¸c˜ao por detr´as do projeto conjunto SA-PNC ´e aproveitar o alinhamento do sinal e codifica¸c˜ao de rede f´ısica, para utilizar a interferˆencia como um sinal ´util, permitindo que mais utilizadores possam estar ativos simultaneamente.

Os resultados num´ericos mostram claramente que os m´etodos propostos fornecem um desempenho pr´oximo do ´otimo, com o m´ınimo de troca de

Keywords Heterogeneous Networks, Macro-cells, Small-cells, Interference/Signal Alignment, Physical Network Coding, Space Frequency Block Codes, OFDM, MIMO

Abstract Mobile traffic in cellular based networks is increasing exponentially, mainly due to the use of data intensive services like video. One effective way to cope with these demands is to reduce the cell-size by deploying small-cells (SCs) along the coverage area of the current macro-cell system. The de-ployment of SCs significantly improves the coverage. Nevertheless, as addi-tional spectrum licenses are difficult and expensive to acquire, it is expected that the macro and small-cells will coexist under the same spectrum. The coexistence of the two systems results in co-tier/intra-system and cross-tier/inter-system interference. In this context, this thesis focuses on the design of several interference mitigation techniques in order to cancel the interference in heterogeneous networks (HetNets) under limited coordina-tion requirements.

The first part of the thesis focuses on the design of several interference alignment (IA) based techniques for the downlink of HetNets. More specif-ically, we design IA based schemes under different levels of inter-system coordination and the constraint that the performance of macro-cell system is kept close to the case where SC system is switched-off.

The second part of the thesis focuses on the joint design of IA and space-frequency block codes (SFBCs) for the downlink of HetNet. More specif-ically, the design of diversity-oriented IA scheme with SFBCs is presented. The main motivation for joint IA with SFBCs is to allow the coexistence of two systems under minor inter-system information exchange. The SCs just need to know what SFBC is used by the macro-cell system and no inter-system channels need to be exchanged, contrarily to other schemes proposed in the first part of the thesis.

The final part of the thesis presents the application of joint signal alignment (SA) and physical network coding (PNC) for the uplink of HetNets. The main motivation behind the joint design of SA-PNC is to take advantage of SA and PNC to utilize the interference as a useful signal that allows the network to achieve high degree of freedom (DoF) by serving more users. The numerical results clearly show that the proposed methods provide close to optimal performance with minor overheads.

Contents

Contents i

List of Figures vii

List of Tables ix

List of Acronyms xi

List of Acronyms xi

List of Symbols xvii

1 Introduction 1

1.1 Cellular Systems Overview . . . 1

1.2 Future Networks-Issues and Challenges . . . 3

1.2.1 Massive MIMO . . . 5

1.2.2 Millimeter Wave (mmW) . . . 5

1.2.3 Small-cells and Heterogeneous Networks . . . 6

1.3 Motivation . . . 6

1.4 Main Contributions and Dissemination . . . 7

1.5 Outline . . . 9

2 Literature Overview on Related Topics 11 2.1 Multiple Antenna Systems . . . 11

2.1.1 MIMO Channel Model . . . 12

2.1.2 MIMO-OFDM . . . 14

2.2 Conventional Cellular Architecture . . . 14

2.3 Cooperative Based Systems . . . 16

2.4 Heterogeneous Networks (HetNets) . . . 18

2.4.1.1 Micro Base Stations . . . 20

2.4.1.2 Pico Base Stations . . . 20

2.4.1.3 Relay Nodes . . . 21

2.4.1.4 Femto/Small-cell Access Points . . . 21

2.4.2 SC Network Paradigms: Underlay, Overlay and Interweave . . . 23

2.5 Deployment Scenarios for HetNets . . . 23

2.5.1 Multi-Carrier Deployment . . . 24

2.5.2 Carrier Aggregation . . . 24

2.5.3 Co-Channel Deployment . . . 25

2.6 Technical Challenges of SCs and HetNets . . . 25

2.6.1 Self Organization . . . 25

2.6.2 Mobility management and handovers . . . 26

2.6.3 Security . . . 26

2.6.4 Timing and Synchronisation . . . 27

2.6.5 Backhauling . . . 28

2.6.6 Interference Management . . . 28

2.7 Interference Mitigation Techniques for HetNets . . . 30

2.7.1 Interference Cancellation . . . 30

2.7.2 Interference Avoidance . . . 31

2.7.3 The Interference Alignment Concept . . . 31

2.7.3.1 A Classical 3-user IA structure . . . 32

2.7.3.2 Fundamental Aspects of IA . . . 33

2.7.3.3 Dimensions in IA-based Schemes . . . 36

2.7.3.4 Some Network Topologies for IA Schemes . . . 37

2.7.3.5 Interference Alignment for K User Interference Channel . . . 39

2.7.4 Applications of Interference Alignment . . . 40

2.7.4.1 Cognitive Radio (CR) . . . 41

2.7.4.2 Cellular Networks . . . 41

2.7.4.3 Heterogeneous Networks and LTE-A . . . 42

2.7.4.4 Device-to-Device Systems (D2D) . . . 42

2.7.4.5 Massive MIMO and 5G Networks . . . 42

2.8 Physical Network Coding (PNC) . . . 43

2.8.1 Illustrating Example: A Three Node Wireless Network . . . 43

2.8.1.1 Traditional Transmission Scheduling Scheme . . . 43

2.8.1.2 Straightforward Network Coding Scheme . . . 44

2.8.2 PNC for HetNets . . . 46

2.9 Signal Alignment . . . 47

2.9.1 The 2-User 2-AP MIMO Uplink . . . 48

2.10 Space-Time Coding . . . 48

2.10.1 Alamouti Scheme . . . 49

2.10.2 Quasi-Orthogonal Codes . . . 51

2.11 Conclusion . . . 53

3 Interference Alignment for MIMO HetNets 55 3.1 Introduction . . . 55

3.2 Related Work . . . 57

3.2.1 Contributions . . . 57

3.3 System Model . . . 58

3.3.1 Macrocell Signal Model . . . 58

3.3.2 Small-Cell Signal Model . . . 60

3.4 Proposed Approach For Precoder and Equalizer Design . . . 60

3.4.1 Interference from MBS towards SUTs . . . 60

3.4.2 Design of the Alignment Direction . . . 61

3.4.2.1 Static Method . . . 61

3.4.2.2 Coordinated Method . . . 62

3.4.2.3 Proposed Two-Bit Method . . . 62

3.4.3 Interference Between SUTs and Interference from SAPs Towards MUT 63 3.4.3.1 Performance and Feedback Requirements . . . 63

3.5 Numerical Results and Discussion . . . 64

3.6 Conclusion . . . 67

4 Joint IA and SFBC for MIMO-OFDM HetNets 69 4.1 Introduction . . . 69

4.2 Related Works . . . 70

4.2.1 Contributions . . . 71

4.3 System Model . . . 72

4.3.1 Signal Model Without SFBC . . . 73

4.3.1.1 Macro-cell System . . . 73

4.3.1.2 Small-cell System . . . 74

4.3.2 Signal Model with SFBC . . . 75

4.3.2.1 Macro-cell System . . . 75

4.4 Proposed Approaches for Precoder and Filter Matrix Design . . . 76

4.4.1 Extended and Proposed Methods without SFBC . . . 77

4.4.1.1 Full-Coordianted Scheme . . . 77

4.4.1.2 Uncoordinated Static Scheme . . . 78

4.4.1.3 Coordinated 2n-Bit Scheme . . . 79

4.4.2 Methods with Joint IA and SFBC Schemes . . . 79

4.4.2.1 IA-Filter Matrix Design for Methods with SFBC . . . 79

4.4.3 Examples for specific SFBCcodes . . . 80

4.4.3.1 Alamouti Codes . . . 81

4.4.3.2 Quasi-Orthogonal Codes . . . 82

4.4.3.3 Tarokh Codes . . . 82

4.5 Performance versus Information Exchange Comparison . . . 83

4.6 Numerical Results and Discussion . . . 85

4.7 Conclusions . . . 92

5 Signal Alignment Enabled Physical Network Coding for HetNets 93 5.1 Introduction . . . 93 5.2 Related Works . . . 95 5.2.1 Contributions . . . 96 5.3 System Model . . . 97 5.3.1 Macro-cell System . . . 97 5.3.2 Small-cell System . . . 99

5.4 Description of Joint SA and PNC Method . . . 99

5.4.1 SA Based Precoding at SUTs . . . 100

5.4.1.1 Case-1: All Equal 1 Group . . . 100

5.4.1.2 Case-2: All Different K Group . . . 101

5.4.1.3 Case-3: Generic S Group . . . 101

5.4.2 Decoding at the MBS . . . 101

5.4.3 PNC and Decoding Process at the CU . . . 103

5.5 Design of the Target Subspace Matrix A . . . 103

5.5.1 Joint SA-PNC Coordinated Scheme . . . 103

5.5.2 Joint SA-PNC Static Scheme . . . 104

5.5.3 Joint SA-PNC 2n-Bit Scheme . . . 104

5.6 Performance Vs Feedback Requirements . . . 104

5.7 Numerical Results and Performance Comparison . . . 106

5.7.2 BER Performance for Small-cell System . . . 109

5.7.2.1 Case-1 . . . 109

5.7.2.2 Case-2 . . . 110

5.7.2.3 Case-3 . . . 110

5.7.3 BER Vs Number of SUTs . . . 111

5.7.4 Trade-off Between Systems Performance . . . 111

5.8 Conclusions . . . 115

6 Conclusions 117 6.1 Thesis in a Nutshell . . . 117

6.2 Future Research Directions . . . 120

6.2.1 Massive MIMO Aided HetNets . . . 121

6.2.2 Generalized Frequency-Division Multiplexing for 5G Networks . . . . 122

6.2.3 Physical Network Coding for Distributed Massive MIMO Systems . . 122

6.2.4 IA for 5G HetNets Based on mmW and Massive MIMO . . . 123

List of Figures

1.1 Cisco forecasts 49 Exabytes per month of mobile data traffic by 2021 [1]. . . . 4

1.2 Expected number of devices by 2020. Ericsson Mobility Report June 2016 [2]. 7 2.1 Multiple Antennas System. . . 13

2.2 Conventional Cellular Architecture. . . 15

2.3 Multi-Cell Cooperation Overview . . . 16

2.4 Relay Assisted Cooperation . . . 17

2.5 Cells with different sizes within the coverage area of Macro-cell creating a Heterogeneous Networks. . . 18

2.6 Small-cells overlaid over macro cellular system. . . 22

2.7 Co-Tier and Cross-Tier Interferences in HetNets. . . 29

2.8 Different interference management techniques for HetNets. . . 29

2.9 The Interference Alignment Concept. . . 32

2.10 Illustration of different network topologies using IA . . . 38

2.11 A K-user IA-Based MIMO Interference Network. . . 40

2.12 Applications of Interference Alignment. . . 40

2.13 (a) A three node network, (b) Traditional scheme, (c) Straightforward network coding scheme, (d) Physical network coding. . . 44

2.14 The 2-user, 2-APs MIMO Uplink Scenario using PNC-SA. . . 49

2.15 Alamouti Scheme . . . 50

2.16 Quasi-Orthogonal Scheme. . . 52

3.1 SCs within the coverage area of Macro-cell creating a HetNets. . . 59

3.2 Block diagram of the considered HetNet. . . 59

3.3 BER comparison of macro-cell without interference and proposed methods (Scenario-1). . . 65

3.5 BER comparison of macro-cell without interference and proposed methods

(Scenario-2). . . 66

3.6 BER performance for the Small-cell system (Scenario-2). . . 67

4.1 SCs within the coverage area of Macro-cell creating a HetNets. . . 73

4.2 Block Diagram of the Considered HetHet. . . 74

4.3 SFBC Schemes at MBS . . . 81

4.4 BER performance for the Macro-cell system (Scenario-1) . . . 86

4.5 BER performance for the Small-cell system (Scenario-1) . . . 87

4.6 BER performance for the Macro-cell system (Scenario-2) . . . 88

4.7 BER performance for the Small-cell system (Scenario-2) . . . 88

4.8 BER performance for the Macro-cell system (Scenario-2) . . . 89

4.9 BER performance for the Small-cell system (Scenario-2) . . . 90

4.10 BER performance at macro-cell system for Joint IA and Alamouti code, Joint IA and Quasi-orthogonal code, Joint IA and Tarokh code . . . 91

4.11 BER performance at small-cell system for Joint IA and Alamouti code,Joint IA and Quasi-orthogonal code,Joint IA and Tarokh code . . . 91

5.1 System Model: Small-cells within coverage area of macro-cell. . . 98

5.2 Block diagram of the considered system . . . 98

5.3 BER performance for the macro-cell system using QPSK modulation. . . 108

5.4 BER performance for the Macro-cell system using 16-QAM modulation. . . . 108

5.5 BER performance for the SC system using QPSK modulation (Case-1). . . . 109

5.6 BER performance for the SC system using 16-QAM modulation (Case-1). . . 110

5.7 BER performance for the SC system using QPSK modulation (Case-2). . . . 112

5.8 BER performance for the SC system using 16-QAM modulation (Case-2). . . 113

5.9 BER performance for the SC system using QPSK modulation (Case-3). . . . 113

5.10 BER performance for the SC system using 16-QAM modulation (Case-3). . . 114

5.11 BER Versus Number of SUTs using QPSK modulation (All Cases). . . 114

List of Tables

2.1 PNC Mapping: Modulation Mapping at N1, N3, Demodulation and

Modula-tion Mapping at N2 . . . 46

2.2 Macro-cell: Encoding scheme used for Alamouti coding . . . 50

3.1 Macro-cell: Performance with feedback requirements . . . 63

4.1 Macro-cell System: Comparison of inter-system information exchange and per-formance . . . 84

4.2 Small-cell: Comparison of inter-system information exchange and performance 85 5.1 Macro-cell: Performance Vs feedback requirements . . . 105

5.2 Small-cell (Case-1): Performance Vs feedback requirements . . . 106

5.3 Small-cell (Case-2): Performance Vs feedback requirements . . . 106

5.4 Small-cell (Case-3): Performance Vs feedback requirements . . . 106

List of Acronyms

1G First Generation.

2G Second Generation.

3G Third Generation.

3GPP 3rd Generation Partnership Project.

4G Forth Generation.

5G Fifth Generation.

ADSL Asymmetric Digital Subscriber Line.

AF Amplify-and-Forward.

AMPS Advanced mobile Phone Systems.

AP Access Points.

AWGN Additive White Gaussian Noise.

BC Broadcast Channel.

BER Bit Error Rate.

BPSK Binary Phase-Shift Keying.

BS Base Station.

CDMA Code Division Multiple Access. CoMP Cooperative Multipoint.

CSG Closed Subscriber Group. CSI Channel State Information.

CSIR Channel State Information at the Receiver. CSIT Channel State Information at the Transmitter.

CU Central Unit.

D2D Device to Device.

DAS Distributed Antennas System.

DBWS Distributed Broadband Wireless System.

DF Decode-and-Forward.

DoF Degree of Freedom.

EDGE Enhanced Data Rates for GSM Evolution.

EGC Equal Gain Combining.

EM Electromagnetic Waves.

FDMA Frequency-Division Multiple Access.

FR Frequency Reuse.

GFDM Generalized Frequency-Division Multiplexing. GPRS General Packet Radio Service.

GPS Global Positioning System.

GSM Global System for Mobile Communication. HetNet Heterogeneous Network.

HSPA High-Speed Packet Access. IA Interference Alignment. IC Interference Channels. ICI Inter-cell Interference.

IEEE Institute of Electrical and Electronics Engineers. IFBC Interfering Broadcast Channel.

IMAC Interfering Multi-Access Channels. IMCC Interference Multicast Channel. IoT Internet of Things.

IoV Internet of Vehicles. ISI Inter-Symbol-Interference.

ITU International Telecommunication Union.

LTE Long Term Evolution.

M2M Machine-to-Machine.

MAC Multiple Access Mode.

MBS Macro Base Station.

MIMO Multiple Input Multiple Output. MISO Multiple Input-Single Output.

ML Maximum Likelihood.

MMS Multimedia Messaging Service. MMSE Minimum Mean Square Error.

mmW Millimeter Wave.

MRC Maximum Ratio Combining.

MRT Maximal Ratio Transmission.

MS Mobile Station.

MUT Macro-cell User Terminal.

NC Network Coding.

NTT Nippon Telephone and Telegraph.

OFDM Orthogonal Frequenc-Division Multiplexing. OFDMA Orthogonal Frequenc-Division Multiple Access. OIA Opportunistic Interference Alignmentl.

OSTBC Orthogonal Space-Time Block Code. PDC Pacific Digital Cellular.

PIC Parallel Interference Cancellation. PNC Physical Network Coding.

QAM Quadrature Amplitude Modulation. QoS Quality of Service.

QPSK Quadrature Phase-Shift Keying.

SA Signal Alignment.

SAP Small-cell Access Point.

SC Small Cell.

SDs Spatial Dimenssions.

SF Space-Frequency.

SFBC Space Frequency Block Codes. SIC Successive Interference Cancellation. SMS Short Message Service.

SNR Signal to Noise Ratio. STBC Space Time Block Codes.

STF Space-Time-Frequency.

SUT Small-cell User terminal. SVD Singular Value Decomposition.

TACS Total Access Communication System.

TC Threshold Combining.

TDD Time Division Duplex.

TDMA Time Division Multiple Access. THP Tomlinson-Harashima Precoding.

TIA Telecommunication Industry Association. TTI Transmission Time Interval.

TWRC Two-way Relay channel.

UMTS Universal Mobile Telecommunication System.

UT User terminal.

UTRA Universal Terrestrial Radio Access.

WCDMA Wideband Code-Division Multiple Access. WiFi Wireless Fidelity.

WiMAX Worldwide Interoperability for Microwave Access.

XC X Channels.

ZC Z Channels.

List of Symbols

(·)H Hermitian transpose operator (·)T Transpose operator

C The set of complex numbers

A ⊗ B Kronecker product between matrices A and B A Upper case bold symbols represent matrices A_(n) Column n of matrix A

I The identity Matrix

x Lower case bold symbols represent vectors x = vec(X) The vectorization of matrix X

σ2 _{Noise variance}

I(a) For a complex number a, denotes its imaginary part R(a) For a complex number a, denotes its real part BER(.) The average bit-error rate

det(·) Determinant operator

diag(A1, ., AN) A diagonal matrix with entries A1, ..., AN fn Indicating the subcarrier index

K Number of Small-cell users in the system

Mm Number of antennas at the transmitter for Macro-cell system Ms Number of antennas at the transmitter for Small-cell system

Nm Number of antennas at the receiver for Macro-cell system Ns Number of antennas at the receiver for Small-cell system null(A) A matrix whose columns span the null-space of matrix A P Available power for transmission

RL The maximum capacity of the Ethernet link Span(A) Subspace spanned by matrix A

Chapter 1

Introduction

1.1

Cellular Systems Overview

A brief history of the evolution of mobile communication is useful to appreciate the enor-mous impact that future cellular systems will have on all of us over the next decades. As wireless communication is enjoying its fastest growth than ever before in history due to many enabling technologies which allows wide spread deployment. Previous generations of cellular systems set the foundation for their successors. In Japan the first operational cellular system was deployed by the Nippon Telephone and Telegraph (NTT) in 1979, followed by Scandi-navian Nordic Mobile Telephony (NMT) in 1981. At the same year, the Advanced mobile Phone Systems (AMPS) was introduced in North America by AT&T. In 1982, Total Access Communication System (TACS) was introduced in United Kingdom and afterwards in 1985 the Radicom 2000 was introduced in France and the C-450 cellular system was introduced in Germany and Portugal [3–5]. The main characteristics of first generation (1G) systems in-cludes analog frequency modulation for voice transmission and the use of Frequency Division Multiple Access (FDMA) for channel allocation.

During the mid 80s with the advances in integrated circuits and digital communication, the digital telephone brought the opportunity to develop the second generation of cellular systems. This digital generation results in increased system capacity, spectral efficiency better quality of service (QoS) and smaller user terminals. The second generation (2G) of cellular systems were widely deployed in the 90’s with the Global System for Mobile Communication (GSM) standard based on Time Division Multiple Access (TDMA) [6]. In parallel the development of digital cellular standards was done by Telecommunication Industry Association (TIA) in the USA yielding in another TDMA based standard called IS-54, also referred to as North American TDMA Digital Cellular. Afterwards, with revised standards such a system has been renamed by IS-136 often called D-AMPS. In 1993, with the development of Code Division

Multiple Access (CDMA) standard the IS-95 was introduced by TIA. In Japan, a 2G based TDMA standard was also developed referred to as Pacific Digital Cellular (PDC). Just after that, the IS-95 CDMA system named as CDMAOne also introduced in Japan. The 2G systems provided voice, e-mail and primary data services, Short Message Service (SMS), that was considered the most successful mobile service to date, after voice 1G. Higher data rates were later introduced in 2G systems with data enhanced technologies such as General Packet Radio Service (GPRS) that could provide rates from 56 kb/s to 115 kb/s. Such rates could provide other services other than SMS as Multimedia Messaging Service (MMS) and Enhanced Data Rates for GSM Evolution (EDGE).

The third generation (3G) family of cellular systems was a worldwide effort to develop and deploy more enhanced cellular systems. 3G offers much higher range of data rates mul-timedia communication, coverage and capacity as compared with predecessors and enables such services as video streaming and makes data services more common. The Universal Mo-bile Telecommunication Services (UMTS) was developed as the 3G replacement for GSM and UMTS was considered as a synonymous with Wideband Code-Division Multiple Access (WCDMA) standard that was also referred to as IMT-2000 standard. WCDMA radio inter-face of Universal Terrestrial Radio Access (UTRA) was developed by 3GPP and CDMA2000 1 x Evolution-Data Optimized (EV-DO) Advanced standard was developed by the 3GPP2. These standards were the collective effort of standardization bodies in Europe, Japan, South Korea, USA and China with relevant International Telecommunication Union (ITU) recom-mendations [7,8].

Subsequent developments yielded to enhance 3G networks. 3GPP specifications yielded into a radio access standard referred to as Long Term Evolution (LTE) and major extensions of WCDMA radio interface evolved High-Speed Packet Access (HSPA) also called HSPA+ [7,9]. There are other deployments with Institute of Electrical and Electronics Engineers (IEEE) 802 family that are the standards related to the broadband wireless metropolitan area networks within the Worldwide Interoperability for Microwave Access (WiMAX) forum have led to the fixed WiMAX (802.16d-2004) and mobile WiMAX (802.16e-2005) standards. The fourth generation (4G) of cellular networks was envisaged offering a seamless connectivity, i.e., the capability to roam across cellular systems, wireless LANs and WANs and IP interoperability according to the need of the user. 4G introduces the concept of ubiquitous connectivity, any time, anywhere from any kind of device and offers high data rates (peak throughput of 100Mbps and 1Gbps in high and low mobility scenarios, respectively). Currently 4G networks supports Multiple-Inputs and Multiple-Outputs (MIMO) and there are currently different types of 4G networks: LTE with Orthogonal Frequency Division Multiple Access (OFDMA); HSPA+ ; W-CDMA; WiMAX using OFDMA [10, 11]. 4G applications include

mobile telemedicine and monitoring, high bandwidth applications, mobile entertainment and multi-party games etc. According to ITU the 4G term is undefined, however it applies to technologies beyond IMT-2000 or evolved 3G technologies providing a substantial level of improvement in performance and capabilities with respect to the initial 3G systems. ITU refers to technologies beyond IMT-2000 as IMT-Advanced which is the response from 3GPP evolved the LTE-Advanced (LTE-A), Release 10.

1.2

Future Networks-Issues and Challenges

Having a quick look at the recent wireless network statistics reveals that the global mobile traffic grew around 70 % in 2016 [1]. Global mobile data traffic reached 7.2 exabytes per month at the end of 2016, from 4.4 exabytes per month at the end of 2015 and expected to reach 49 exabytes per month by 2021 as shown in Figure 1.1. It is interesting to observe that only 26 % smart devices (of the total global mobile devices) are responsible for 89% of the total mobile data traffic. This percentage relates to the Pareto principle [12] (also known as the 80/20 rule, the law of the vital few, or the principle of factor sparsity) states that, for many events, roughly 80 % of the effects come from 20 % of the causes. Smart devices refers to mobile connections that have advanced multimedia/computing capabilities with a minimum of 3G connectivity. In 2016, on an average, a smart device generated 13 times more traffic than a non-smart device and Cisco visual networking index forecasts that mobile networks will have more than half of connected devices as smart phones by 2019. This increasing usage of smart-phones results in an exponential growth in mobile multimedia traffic and since 2012, video traffic is more than half of the total global mobile traffic [1] .

Due to the use of bandwidth-intensive applications, supporting this enormous and rapid increase in data rates and connectivity, represnets an extremely difficult challenge to current 4G LTE cellular networks [13–16]. In order to support 1000 times mobile data traffic by 2020, the future 5G network technologies are under development to achieve the targets in terms of high data dates and increased network capacity. A number of standardization bodies and organizations have been established to define and develop standards for 5G such as; ITU-R, 3GPP, EU’s METIS and 5G-PPP, NGMN, Small-Cell Forum, etc [17–20]. The major requirements for 5G systems are summarized as follows:

• Higher data rates: 10 to 100x higher typical user data rate.

• Higher system capacity: 1000x bandwidth per area. To enable large number of con-nected devices with higher bandwidths for longer durations in a specific area.

• Reduced latency: 1 ms, end-to-end round trip latency. Almost 10 times reduction from 4G’s 10ms round trip time.

• Huge number of connected devices: In order to realize the vision for IoT, emerging 5G networks need to provide connectivity to 10 to 100x number of connected devices. • Coverage: almost 100 % coverage for ”anytime anywhere” connectivity.

• Perceived availability of 99.999%. 5G networks envisions that the network should be always available.

• Battery life: 10x better battery life for low power devices. • Energy efficient: 90 % reduction in network energy usage.

Figure 1.1: Cisco forecasts 49 Exabytes per month of mobile data traffic by 2021 [1].

To fulfil the mentioned requirements, certainly the one that receives the most attention is the requirement of higher data rates. There is a group of emerging technologies, that promise to solve the technical challenges of current and future wireless networks [15,16] divided into three categories [15,16,21].

1. Massive MIMO: Increased spectral efficiency through advances in MIMO to support more bits/s/Hz per node.

2. Millimeter Wave (mmW): Increased bandwidth by moving towards mmW spectrum and by better utilization of unlicensed spectrum in the GHz band.

3. Small-cells: Extreme densification and offloading through the use of small-cell to im-prove the spectral efficiency.

There are also other technologies that may contribute to the efficiency of future 5G net-works, e.g., device to device (D2D), internet of things (IoT), internet of vehicles (IoV) and machine to machine (M2M) communications. However, the main share of the surge in ca-pacity should come from the ideas mentioned in above categories and are discussed in the following:

1.2.1 Massive MIMO

Massive MIMO has been considered as a breakthrough for future 5G networks. Massive MIMO consists of equipping the terminals with a very large number of antennas in order to serve many active devices in the same time-frequency block [15,21,22]. The main objective of massive MIMO is to make available all the advantages of MIMO but on a large scale. The main benefits of massive MIMO includes energy and spectrum efficiency. Massive MIMO will play the key role to make future networks more energy and spectral efficient, but there are a few technical issues regarding the practical implementation of massive MIMO. To mention a few of them: the development of low cost antennas, channel characterization and the requirement of channel state information (CSI) are the most technical issues related to massive MIMO [21,22].

1.2.2 Millimeter Wave (mmW)

The capacity of wireless network depends on spectral efficiency and bandwidth. The current communication systems use a relatively very slim range of microwave frequencies (from several hundred MHz to a few GHz) that corresponds to wavelengths ranging from a few centimetres upto about a meter. Alternatively, there is an enormous amount of spectrum available at mmW frequencies ranges from 3 to 300 GHz [15,16,21]. The key essence of future 5G networks lies in exploring this unused mmW frequency band. Collision free radars were the first to utilize this mmW spectrum [23] and radio astronomy, radars, military applications and airport communications have already been using the mmW spectrum over the last few decades. From the huge 3 ∼ 300 GHz mmW spectrum, only 57 ∼ 64 GHz and 164 ∼ 200 GHz is unsuitable for communications due to legalization reasons. As compared to the current cellular spectrum, a small fraction of mmW spectrum can support 100 of times of more data rate and capacity [24]. Therefore, the mmW spectrum is opening a new horizon for spectrum constrained future mmW networks [23,24].

1.2.3 Small-cells and Heterogeneous Networks

Another straightforward and extremely effective way to increase the network capacity and to handle the wireless traffic explosion for 5G networks, is the massive development of SCs that is related to the concept of HetNets [21, 25–27]. HetNets typically consists of SCs having low transmission power coexisting with the legacy macro-cells. Cell splitting has numerous advantages, where the overlap of small-cells with the existing macro-cells results in improved network capacity, extended coverage and may also leads to improved and efficient spectrum reuse. On the other hand, when the cell densification becomes extreme, a number of technical challenges arise mainly related to self-organization, backhauling and interference management issues [28–30]. Interference problem is one of the most limiting factor for the performance of multi-tier systems. HetNets call for a coordinated operation between multi tiers for mutual interference mitigation. Another important aspect of these networks is the amount of information that needed to be exchanged between the macro and small cells in order to get rid of interference. The technical issues related to interference management are discussed in Section 2.6.6 in more detail.

1.3

Motivation

Due to a new generation of wireless user equipment and the proliferation of bandwidth-intensive applications (such as video, mobile broadband modems, tablets, mobile data ap-plications), the corresponding network load are increasing in an exponential manner. This massive mobile internet access is expected to exceed wired devices access by 2018 [1]. Fur-thermore, the number of networked devices are expected to reach 25 billion by 2020 as shown in Figure 1.2 [2,31]. This increasing demand will bring huge challenges in terms of network operational capabilities and global standardizations. Despite the fact that the 4G of cellular networks is already operational and has achieved maturity both from industry and academia, the future 5G of communications will bring service demands that the current infrastructures are far from being capable to handle [15]. A straightforward and extremely effective approach to increase the network capacity is to bring the transmitter and receiver closer to each other. This approach has been considered over several generations of cellular networks [32,33]. The first generation had the cell sizes on hundreds of square kms, since then the cell size have been progressively shrinking to fractions of square km. Now networks are rapidly evolving towards the nested SCs such as picocells (within 100 meters) and femtocells (similar to WiFi range) [34, 35]. Cell reduction has numerous benefits that includes the off-loading of traffic from macro cellular system and they can be operated inside the coverage of macro-cell system over the same frequency band and offer great advantages for operators and for users, who get

better coverage and higher data rate, and can access new services [36].

Inspired by the features and potential advantages of SCs, their development and deploy-ment have gained considerable interest in the wireless industry and research communities. On the other hand, these networks suffer from the problem of self-organization, interference management and rising costs of maintenance and feedback requirements for HetNet. There-fore, the work presented in this thesis is motivated by the technical challenges in HetNets, that still need to be addressed for their successful rollout and operation. Furthermore, the research in this thesis is motivated by the design challenges in IA, mainly related with global channel state information (CSI) feedback requirements. In this context, the development of interference mitigation techniques in order to cancel interference in multi-tier HetNets is therefore of paramount importance. More specifically, we design several interference align-ment (IA) and signal alignalign-ment (SA) based schemes under minor inter-system information exchange requirements.

Figure 1.2: Expected number of devices by 2020. Ericsson Mobility Report June 2016 [2].

1.4

Main Contributions and Dissemination

The main contributions of this thesis are summarized as follows

• Proposal of several IA-based schemes under different levels of inter-system coordination requirement in order to tackle the downlink interference in HetNets.

• Proposal of a coordinated 2n-bit scheme with limited feedback requirement to overcome the limitations of previously high-feedback proposed methods.

• Design of a new diversity oriented low-complex IA scheme with SFBC without any inter-system information exchange requirements.

• Proposal of several joint SA and PNC scheme under minor information exchange to tackle the uplink interference for HetNets and increase the overall network capacity. • Design of a new diversity-oriented scheme based on joint Dual-SFBC and SA-PNC

to facilitate inter-system coexistence without information exchange for the uplink of HetNets.

The work presented in this thesis has produced a number of scientific publications: 1 book chapter, 3 journal papers, and 5 conference papers listed below.

Ch1 S. S. Ali, D. Castanheira, A. Silva, and A. Gameiro, ”Physical-Layer Transmission Cooperative Strategies for Heterogeneous Networks”, Towards 5G Wireless Networks - A Physical Layer Perspective, ed. Dr. Bizaki, InTech, 2016.

J1 S. S. Ali, D. Castanheira, A. Silva, and A. Gameiro, ”Transmission Cooperative Strategies for MIMO-OFDM Heterogeneous Networks”, Radioengineering Journal, vol. 25, no. 2, p. 431-441, July 2015.

J2 S. S. Ali, D. Castanheira, A. Silva, and A. Gameiro, ”Joint IA and SFBC Macrocells and Small-Cells Coexistence under Minor Information Exchange”, Mobile Information Systems, vol. 2016, Article ID 3047859, 10 pages, 2016.

J3 S. S. Ali , D. Castanheira , A. Silva , A. Gameiro, ”Joint Signal Alignment Precoding and Physical Network Coding for Heterogeneous Networks”, Physical communication Journal, vol. 23, p. 125-133, March, 2017.

C1 S. S. Ali, D. Castanheira, A. Silva and A. Gameiro, ”Downlink cognitive interference alignment for heterogeneous networks”, IEEE 21st International conference on Telecommunications (ICT), pp. 236-240, Lisbon, Portugal, 2014.

C2 S. S. Ali, D. Castanheira, A. Silva and A. Gameiro, ”Joint Signal Alignment and Physical Network Coding for heterogeneous networks”, IEEE 23rd International conference on Telecommunica-tions (ICT), Thesalonoki, Greece, 2016.

C3 S. S. Ali, D. Castanheira, A. Silva and A. Gameiro, ”A New SA-PNC Scheme for Uplink HetNets”, IEEE 25th European Signal Processing Conference (EUSIPCO), KOS Island, Greece, 2017.

C4 S. S. Ali, D. Castanheira, A. Silva and A. Gameiro, ”A Novel SA-PNC Method for Macro and Small cells Coexistence Under the Same Spectrum”, IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, Canada, 2017.

C5 S. S. Ali, D. Castanheira, A. Alsohaily, E. Sousa, A. Silva, and A. Gameiro, ”Two-Tier Cellular System Up-link Based on Space-Frequency Block Codes and Signal Alignment”, IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 2018.

1.5

Outline

The remaining of the thesis is organized as follows:

 Chapter 2 presents a brief overview of the main theoretical concepts that are stud-ied along the thesis. First of all, an overview on multiple antenna system and orthogonal frequency-division multiplexing (OFDM) systems are presented. Then the overview and def-initions of conventional cellular networks, SCs and HetNets along with their main technical challenges are presented mainly related to interference management. Related work for inter-ference mitigation techniques for HetNet is also provided in this chapter. Then an overview on IA is presented. Moreover, fundamental aspects of IA, including feasibility condition, per-formance metrics and feedback requirements are presented along with the example of three user interference channel and applications of IA. At the end, an overview on physical network coding (PNC), signal alignment (SA) and space-frequency block codes (SFBC) are also pro-vided, which are of central importance to understand the scope of the research and results in this thesis.

 Chapter 3 presents the first phase of this thesis. The work presented in this chapter is focused on designing several IA-based methods under different levels of inter-system coordi-nation requirements in order to mitigate the interference in the downlink of HetNets. Mainly an IA method based on limited feedback requirements is proposed to overcome the drawbacks of fully-coordinated and uncoordinated IA-based schemes. The proposed scheme provides a commitment between performance and feedback requirements.

 Chapter 4 presents the second part of this thesis. This chapter addresses the problem of limited information exchange in designing IA based schemes for HetNets. The two-tier network (macro and small cells) shares the same frequency bands and results in considerable interference between them. In this context, the work in this chapter is focused on designing interference mitigation techniques based on joint IA and SFBC with minor feedback require-ments. Namely, several diversity-oriented IA schemes with SFBC are proposed without any inter-system information exchange.

 Chapter 5 belongs the third part of this thesis. This chapter presents the joint ap-plication of SA and PNC for the uplink of HetNets in order to mitigate the interference generated from SCs at the MUT. The proposed methods are compared with the existing IA based methods, where the joint design of SA and PNC allows the network to achieve the higher DoF by serving more SUTs as compared to the case where IA or PNC is employed individually. Three different joint SA-PNC schemes are proposed under different levels of inter-system coordination requirements.

 Chapter 6 concludes the thesis summarizing the main achieved results and presents some future lines of work.

Chapter 2

Literature Overview on Related

Topics

This chapter presents a brief overview of the main theoretical concepts of the related topics that are studied along this thesis. First of all, a brief introduction on multiple antenna system is provided along with orthogonal frequency-division multiplexing (OFDM) sys-tems. Then the conventional cellular architecture and background on cooperative systems is presented along with the definitions and major advantages of HetNets. The concept of HetNets has proven to provide improved coverage and capacity for cellular networks but these networks come up with their own design challenges mainly related to the interfer-ence management. Since the HetNet architecture consists of two tiers (i.e., macro-cell and small-cell systems) where these two systems have to coexists over the same spectrum that results in considerable interference, hence degrading the performance of both the systems. In this context, HetNets require more dynamic and careful planning in order to avoid the interference. The chapter provides a brief introduction on interference management tech-niques for HetNets. Since the work developed in the next chapters are mainly focused on interference alignment (IA), signal alignment (SA), and physical network coding (PNC), a brief introduction of all these topics are also presented in this chapter.

2.1

Multiple Antenna Systems

The amount of data rate per unit bandwidth that can be transferred for a wireless channel is referred as spectral efficiency and is limited by the available transmit power for a single communication link. The spectral efficiency can be enhanced by utilizing multiple antennas at both the transmitter and receiver sides which provides spatial diversity since several in-dependent data streams can be transmitted simultaneously, achieving the multiplexing gain. Multiplexing can be obtained by exploiting the structure of the channel gain matrix to

ob-tain independent signalling paths that can be used to send independent data streams. The initial work on MIMO was sparked by the pioneer works of Winters [37], Foschini [38], Gans [39] and Telatar [40] obtaining remarkable spectral efficiency gains for wireless systems using multiple antennas at the transmitter and receiver sides. In addition to spectral efficiency, inter-symbol-interference (ISI) and interference from other users can be eliminated by using smart antenna techniques. The signal processing techniques in MIMO systems are classified into three categories: beamforming, spatial multiplexing techniques and antenna diversity techniques [41]. Beamforming is a transmission scheme where data streams of different users are encoded independently and multiplied by weight vectors in order to mitigate mutual inter-ference. Beamforming can be defined as the steering of data signals towards intended/selected users by means of array processing so that the received signal power is increased and the inter-user interference is mitigated. On the other hand, spatial multiplexing techniques exploit the DoF gains provided by MIMO by transmitting different data streams simultaneously, that in-creases the transmission rate by reusing the spatial dimensions provided by MIMO [42]. The antennas diversity techniques can be applied at both the transmitter and receiver sides. In receiver diversity, the independent fading paths associated with multiple receive antennas are combined to obtain a resultant signal that is then passed through a demodulator. The com-plexity and overall performance can vary depending on the combining technique used. Most of the combining techniques are linear, i.e., the output of the combiner is just a weighted sum of the different paths. Examples of receiver based diversity scheme includes; selection combining (SC), threshold combining (TC), maximum ratio combining (MRC) and equal gain combining (EGC) [43]. For the transmit diversity, there are multiple antennas at the transmitter with transmit power divided among these antennas. The transmit diversity is desirable in systems such as cellular networks where more space, power and processing capability is available at the transmitter side as compared to the receiver side. Transmit diversity scheme includes; space diversity, polarization diversity and time/frequency diversity [43].

2.1.1 MIMO Channel Model

A multiple-input-multiple-output (MIMO) system employs multiple antennas at both the transmitter (Mt) and receiver (Nr) sides to improve the performance by means of signal processing techniques. In [43–45], the authors provide the mathematical motivation behind the use of multiple antenna systems. By using multi antenna system, the spectral efficiency increases by a factor of min(Mt, Nr) assuming uncorrelated channels without extending the bandwidth requirements or power budget. A narrowband point-to-point communication sys-tem of Mt transmit and Nr receive antennas is shown in Figure 2.1. This system can be represented in matrix form as follows:

    y₁ .. . y_Nr     =     h11 . . . h1Mt .. . . .. ... hMr1 . . . hNrMt         x1 .. . xMt     +     n1 .. . nNr     (2.1)

or simply, the received signal y can be expressed as,

y = Hx + n. (2.2)

where x denotes the Mtdimensional transmitted symbol, n denotes the Nr dimensional noise vector and H is the Nr× Mt matrix of channel gains hij representing the gain from transmit antenna j to receive antenna i. Different assumptions can be made for the knowledge of the channel gain matrix H at the transmitter and receiver side referred to as channel state information at the transmitter (CSIT) and channel state information at the receiver (CSIR), respectively. CSIR is typically assumed to be known at receiver side, since the channel gains can be obtained easily by sending a pilot sequence for the channel estimation. If the feedback link is available then CSIR from the receiver can be sent back to the transmitter to provide CSIT. CSIT may also be obtained in time-division duplexing systems without a feedback link by exploiting reciprocal properties of propagation between the uplink and downlink slots.

2.1.2 MIMO-OFDM

Orthogonal frequency-division multiplexing (OFDM) is a special kind of multi-carrier modulation [41, 46–49], which was originally used in military high frequency radio. In [50], Weinstein proposed an effective way to implement OFDM by means of the discrete Fourier transform. The first application of OFDM for mobile communication can be found in [48]. During the past decade OFDM has gained considerable importance and has been adopted in many wireless communication standards, including European digital audio broadcasting terrestrial, digital video broadcasting and asymmetric digital subscriber line [46]. In ad-dition OFDM has been considered in many IEEE standards, such as IEEE802.11a/g/n, IEEE802.15.3a and IEEE802.16d/e. The applications of OFDM include wireless personal area networks, wireless local area networks and wireless metropolitan networks. Further-more, OFDM is being investigated as one of the most effective radio transmission technique for 3GPP-LTE.

OFDM is a digital modulation scheme in which wideband signal is split into a number of narrowband signals. Since the symbol duration of a narrowband signal is larger than that of a wideband signal, with OFDM the amount of time dispersion caused by multipath delay spread is reduced. OFDM is a special case of multi-carrier modulation, in which multiple symbols are transmitted in parallel using different sub-carrier with overlapping frequency bands that are mutually orthogonal. Over the years, the combination of MIMO with OFDM has gained significant interest from industry and academia and it has been considered as one of the most promising technology for the present and future wireless communications systems [46, 51]. This combination (MIMO-OFDM) turns out to be a very effective approach for future high-data rate broadband wireless systems. The first application of MIMO-OFDM can be found in [52,53]. Since then, the joint MIMO-OFDM design has become a very popular approach in wireless communications [41, 46, 54–59]. Most of MIMO techniques are designed for flat fading channels. However, multipath will cause frequency selectivity of broadband wireless channels, where the frequency selective channel offers an additional degree of diversity known as frequency diversity. In a MIMO-OFDM based system it is desired to achieve multipath and spatial diversity gains. Space-frequency (SF) and space-time-frequency (STF) codes have been designed to achieve levels of space and multipath diversity and are discussed at the end of this chapter.

2.2

Conventional Cellular Architecture

Conventional cellular architecture was basically designed under the consideration of fixed frequency planning, where there is no coordination between the BSs [3–5]. The first cellular

system uses the concept of Frequency Reuse (FR) [5] in order to avoid inter-cell interference (ICI) at the expense of reduced spectral efficiency. Each base station is dedicated a group of channels to be used within a small area called cell, where BSs in adjacent cells are assigned different channels. The same group of channels can be used to cover different cells that are separated by distances enough to avoid interferences. As shown in Figure 2.2, where three adjacent cell of each is assigned different set of sub channels but this scheme faces the problem of lower data rates and in a conventional single antenna cellular networks, ICI could be a severe problem. Besides that conventional cellular architectures has several limitations, mainly the lack of information shared among entities of the network and fixed radio resource in the deployment. Therefore, the systems are not flexible and they cannot adapt to changes in the amount of users or services, and as the deployment has no adaptation to the changing network metrics, the radio resources are wasted in some parts of the network and resource starvation may arise. As compared to the conventional architecture, the cooperative based architecture allows the use of distributed antennas, which increase the coverage area, reduce the power consumption and reduce interference due to their capacity of exploiting the space domain. The multiple antennas technology has been studied broadly in literature and their implementation combined with Orthogonal Frequency-Division Multiple Access (FDMA) has been adopted in many high data rate wireless communications standards worldwide interoperability for microwave access and Long Term Evolution (LTE)-A. Furthermore, in order to overcome the limitations of the conventional cellular networks, the concept of SCs and HetNets have been proposed and are discussed in detail in the next sections.

2.3

Cooperative Based Systems

The increasing demand of wireless traffic and number of devices, results in increasing the interference level, which significantly degrades the capacity gains as promised by single cell MIMO based techniques [60]. In conventional cellular networks, each BS transmits sig-nal intended for particular users within the cell coverage area, where interference caused by neighbouring cells can degrade the received signal quality and the downlink capacity of the cellular systems is limited by inter-cell interference. An attractive way to improve the system capacity is the cell reduction approach. However, the deployment of large number of SCs is not possible without new technical challenges [61]. As most of the interference mitigation challenges originate due to the cell edge users, which increases as the number of cell increases. Since BS can be connected via a high speed backbone, there is an opportunity to have coordi-nation in cellular systems in order to minimize the inter-cell interference and to increase the system capacity [60]. In this context, multi-cell cooperation is a promising solution for cellular systems to mitigate inter-cell interference, improving system fairness and capacity. Multi-cell cooperation can be achieved in the uplink or downlink of the communication between BS and a user terminal as shown in Figure 2.3. The cooperating entities have the information about source data and CSI and are already under study in LTE-A [62]. There are several multi-cell cooperation approaches depending on the amount of information that needs to be shared between the transmitters through the back-haul network and where the processing take place, that can be centralized if the processing takes place at the central unit (CU) or distributed if the processing take place at different transmitters. In centralized approach, transmitters exchange both data and CSI for joint signal processing at the CU and promise larger spectral efficiency gains as compared to distributed coordination techniques but at the expense of complex backhaul and more severe synchronization requirements.

Another approach to mitigate inter-cell interference and to increase the capacity of cellular systems is known as relay assisted cooperation, where a dedicated relay can save on equipment costs, backhaul link, and deployment. Relay based cooperation is possible whenever there is at least one additional node willing to aid in the communication and this approach is known as relaying. The use of relay assisted cooperation is considered as an important technology for future wireless systems, because it has the potential to increase the capacity, extend coverage and improve access fairness, as well as to provide additional flexibility in order to do efficient upgrading of the network. Relay based cooperation can be achieved through cooperation of terminals, either dedicated or user terminals acting as relays as shown in Figure 2.4. The relay channel is a three-terminal communication channel, where the source terminal is labelled as (S), the relay terminal as (R) and the destination terminal as (D), where the signal transmitted from the source is X, the signal received at the relay is V , the signal transmitted from relay is W and the received at the destination is Y . Information is relayed in two phases: first, when Source transmits and Relay, Destination receives which is known as Broadcast Channel (BC) mode and second, when Source and Relay transmits and Destination receives known as Multiple Access Mode (MAC).

Figure 2.4: Relay Assisted Cooperation

Different transmit strategies can be considered, depending on the capacity of the backhaul channel that connects the coordinated BSs. An enhanced cellular architecture with a high-speed backhaul channel has been proposed and implemented under the European FUTON project [63]. This project aims at the design of a Distributed Broadband Wireless System (DBWS) by carrying out the development of a Radio over Fiber infrastructure transparently connecting the BSs to a CU where centralized joint processing can be performed. Coordi-nated transmissions are effective when BSs and mobile terminals are equipped with multiple

antennas and referred to as Multi-cell MIMO cooperative networks. In recent years, relevant works on multi-cell precoding techniques have been proposed in [64–66]. The multi cell down-link channel is closely related to the MIMO broadcast channel, where the optimal precoding is achieved by the dirty paper coding principle. [67] presents currently known techniques for Multi-cell MIMO cooperation in wireless networks, where interference emerges as a key capacity limiting factor and multi cell cooperation can improve the system performance.

Core Network Backhaul Macrocell Microcell Picocell Femtocell Picocell Relay Node

Figure 2.5: Cells with different sizes within the coverage area of Macro-cell creating a Heterogeneous Networks.

2.4

Heterogeneous Networks (HetNets)

As future mobile wireless network experiences an unrelenting demand for higher data rates in wireless networks and there is a continuous advancement in new standards for both home and cellular networks [1,31]. Recent studies have suggested that this rapid increase in demand for high data rates is chiefly generated from indoor environments. There are two main wireless technologies used for providing coverage to indoor users, the first one is the traditional outdoor cellular infrastructure which deals with real time voice, short messages and Mobile Broadband applications and the second one is Wireless Fidelity (WiFi). Due to the increasing demands for indoor higher data-rate wireless applications, existing cellular systems will be insufficient to meet the expected QoS requirement for indoor users from both service coverage and capacity perspectives. In the past there has been an uncoordinated development for home and cellular networks and considering the current demand for new services in wireless networks non-cooperation leads to waste of radio resources and a poor network performance. As a solution

to these problems, 4G standards have proposed new cooperative network architectures in which network elements share information about radio resources, environment, interference, load and traffic, in order to maximize the overall network efficiency [68, 69]. Coordination between these networks will be able to form the so called HetNets that reduces the access cost, improve energy and spectrum efficiency. HetNet deployment has received great attention due to its high potential to improve the capacity of cellular networks. In particular the overlay deployment of femto and pico cells along with macro cell and indoor-outdoor coexistence are most challenging scenarios. However, it has been observed that the deployment of femto and pico cells improves the spectral efficiency but also increases outage probability due to sever interference between indoor systems and indoor-outdoor systems known as Co-tier (intra-tier) and Cross-tier (inter-tier) interferences respectively [28,70,71].

Due to this massive overlaid deployment, traditional interference coordination technolo-gies such as soft frequency reuse are not sufficient. In order to meet this challenge of new scenarios and deployments, enhanced inter-cell interference coordination (eICIC) technologies are required [26, 29, 72]. Mainly, there are two categories of ICIC techniques, namely the multi-carrier inter-cell interference management and single-carrier (co-channel) inter-cell in-terference management [26,72]. In multi-carrier inter-cell interference management approach, frequency can be assigned to macrocells and SCs have to achieve interference coordination, where carrier management and power control procedures among macrocells and SCs must be performed to achieve the high system throughput. Another method known as cross-carrier scheduling was introduced for systems deployed with carrier aggregation, in which the macro-cell schedules its users by using the control channel region in one carrier while the overlaid SCs can schedule their users via the control channel in another carrier [26]. For the single carrier, also known as co-channel inter-cell interference management, in which macrocells and the SCs have to coexist under the same carrier, where time-domain resource partition among macro and small cells can be used on the subframe level. In some subframes, a cell does not transmit data signals but only transmits the control signals needed to maintain system operation so that the interference to other cells can be reduced. Other techniques including range expansion can be used to offload the traffic from marcocell to SCs and are discussed later in this chapter.

HetNet is one of the most widely term used in today’s wireless communication system, where some people consider the overlay of SCs with macro-cell under the same air interface technology as HetNets. Others consider the integration of cellular network plus WiFi networks as a main used case. There are also other definitions of HetNets that includes new network topologies as part of HetNets, such as personal hotspot, relays, peer-to-peer, device-to-device, machine-to-machine and near filed communications. Since flexible sharing and dynamic

ac-cess of radio resources become part of the network infrastructure, we can expect HetNets to include cognitive radios (CR). In order to meet the demands on both capacity and coverage of cellular networks and to utilize the limited spectrum efficiently, the concept of HetNet [26, 73] have been proposed to make the network more agile and flexible by allowing unli-censed users/systems to coexist with the liunli-censed users/systems [74] as shown in Figure 2.5 [74]. Network densification through use of SCs has been considered in 3GPP which made SCs an integral part of LTE-Advanced by developing the concept of HetNet [27,74]. In general a HetNet consists of multiple tiers or layers of networks with different cell sizes. LTE macro-cell overlaying LTE SCs is a good example of two-tier HetNets.

2.4.1 Heterogeneous Network Nodes

Heterogeneous cellular network nodes are different according to their power, coverage area, physical size, radio propagation characteristics and backhaul connections. Macrocells are operator-deployed with the largest coverage areas, having transmit power that typically varies between 5W and 40W. The transmit power of low power nodes ranges from 250mW to approximately 5W if they are deployed outdoors and falls below 100mW for indoor deployment [27,75]. The low power nodes may include, micro and pico base stations and femtocell/small-cell access points and relay nodes as shown in Figure 2.5 and are discussed as follows.

2.4.1.1 Micro Base Stations

Micro BS/nodes serving microcell users, are regular BSs that provide standardized inter-faces over the backhaul connection, but with lower transmit power than traditional macro cellular systems. Typically, the transmit power of micro cell is of the order of 5-10W for outdoor scenario. These base stations are deployed outdoors in an operator-planned fashion and particularly famous for proving outdoor hotspot coverage. They can be equipped with omnidirectional antennas but may have antenna directivity as well [26].

2.4.1.2 Pico Base Stations

The pico BSs or nodes serving picocell users are similar to micro nodes but with even lower transmit per picocell served and possibly even more smaller size. For the outdoor deployment the transmit power ranges from 250mW to approximately 2W, while it is typically 100mW or less for indoor scenarios. Also pico BS are deployed indoors and outdoors often in an operator-planned fashion and particularly popular for providing hotspot coverage. They can be equipped with omnidirectional antennas or with some antenna directivity [26].

2.4.1.3 Relay Nodes

In scenarios where a wired backhaul is not available, relay nodes can be deployed with the air interface used for both backhaul connection and access to user equipments and they can be either deployed for indoor or outdoor scenarios. The transmit power for relay nodes ranges from 250mW to approximately 2W for outdoor deployments and for indoor scenarios it is typically 100mW or less. A relay node is considered to be a full-fledged BS but without a wired backhaul and it appears as a user equipment toits donor node and as regular BS to the user equipments that it serves. Relay nodes are typically equipped with directional antennas in the backhaul link and have omnidirectional or directional antennas for the access link [26].

2.4.1.4 Femto/Small-cell Access Points

Small-cells (SCs) are low powered base stations that are increasingly recognized by the operators [76, 77], as a way to cope with the projected demand for higher data rates for the next generation wireless cellular networks [13]. Due to the scarcity of radio spectrum, these SCs have to coexists within the coverage area of macro cellular system as shown in Figure 2.6. Other than the capability of providing higher data rates, off-loading traffic from macro cellular system and capacity improvements, SCs provide other advantages, such as: they are easy to deploy (i.e. they can be installed in an indoor area by the end user just like a Wi-Fi router and provides almost the functionalities of a cellular network to the end user), have low deployment cost, improved coverage (especially in indoor environment) and are more energy efficient [32,75]. Inspired by the features and potential advantages of the SC networks, their development and deployment have gained considerable interest in the wireless industry and research communities. The potential growth of SCs have also been considered by standardization bodies such as 3GPP LTE-Advanced [74]. In this context, there has been an increasing interest to deploy SCs in residential homes, subways and offices. On the other hand, these networks also come up with their own challenges. There are significant technical issues related to self-organization, backhauling and interference management that still need to be addressed for their successful rollout and operation [25,28,78].

In recent years, various types of SCs have been developed depending on the air interface technologies, services, standards and access control strategies, where each type of SC provide different kinds of services depending on user needs. For example, the 2G SCs are based on the the global system for mobile communication system (GSM) air interfaces. The 3G SC uses the wideband code-division multiple access (WCDMA) based air interface of universal mobile telecommunication system (UMTS). For worldwide interoperability for microwave ac-cess (WiMAX) and LTE, SCs uses orthogonal frequency-division multiple acac-cess (OFDMA)