Universidade de Aveiro Departamento deElectr´onica, Telecomunica¸c˜oes e Inform´atica, 2019
Ricardo
Enes
Equalizador Iterativo H´ıbrido para sistemas MIMO
CE-OFDM na banda das ondas mil´ımetricas
Hybrid Iterative Equalizer for Massive MIMO
Millimeter Wave CE-OFDM Systems
Universidade de Aveiro Departamento deElectr´onica, Telecomunica¸c˜oes e Inform´atica, 2019
Ricardo
Enes
Equalizador Iterativo H´ıbrido para sistemas MIMO
CE-OFDM na banda das ondas mil´ımetricas
Hybrid Iterative Equalizer for Massive MIMO
Millimeter Wave CE-OFDM Systems
Disserta¸c˜ao apresentada `a Universidade de Aveiro para cumprimento dos requisitos necess´arios `a obten¸c˜ao do grau de Mestre em Engenharia Eletr´onica e Telecomunica¸c˜oes, realizada sob a orienta¸c˜ao cient´ıfica do Pro-fessor Doutor Ad˜ao Silva (orientador), Professor auxiliar do Departamento de Eletr´onica, Telecomunica¸c˜oes e Inform´atica da Universidade de Aveiro e da Doutora Sara Teodoro (co-orientadora), investigadora no Instituto de Telecomunica¸c˜oes de Aveiro.
This work is supported by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Oper-ational Program (COMPETE 2020) of the Portugal 2020 framework, Regional OP Centro (CENTRO 2020), Regional OP Lisboa (LISBOA 14-20) and by FCT/MEC through national funds, under Project MAS-SIVE5G (AAC no 02/SAICT/2017)
o j´uri / the jury
presidente / president Professor Doutor Armando Carlos Domingues da Rocha Professor Auxiliar da Universidade de Aveiro
vogais / examiners committee Professor Doutor Paulo Jorge Coelho Marques
Professor Adjunto do Instituto Polit´ecnico de Castelo Branco (Arguente Principal)
Professor Doutor Ad˜ao Paulo Soares da Silva Professor Auxiliar da Universidade de Aveiro (Orientador)
agradecimentos / acknowledgements
Gostaria de expressar o meu sincero agradecimento a todos aqueles que di-reta ou indidi-retamente contribu´ıram para a realiza¸c˜ao desta disserta¸c˜ao. A minha profunda gratid˜ao dirige-se primeiramente `a minha fam´ılia, que me motivou e apoiou incondicionalmente nesta etapa da minha vida.
Ao meu orientador, Professor Doutor Ad˜ao Silva, e `a minha co-orientadora, Doutora Sara Teodoro, pela sua ajuda, paciˆencia, supervis˜ao e total disponi-bilidade.
Ao Roberto Magueta, um muito obrigado por toda a ajuda e boa disposi¸c˜ao, que tanto contribu´ıram para levar a disserta¸c˜ao a bom porto.
Aos meus amigos que sempre me apoiaram e encorajaram com a sua pre-sen¸ca constante na minha vida, durante os piores e melhores dias deste ´
ultimo ano. `
A Universidade de Aveiro, ao Departamento de Eletr´onica, Telecomu-nica¸c˜oes e Inform´atica e ao Instituto de Telecomunica¸c˜oes por fornecerem as condi¸c˜oes necess´arias de trabalho e aprendizagem.
Keywords 5G, CE-OFDM, full digital architectures, hybrid architectures, massive MIMO, mmWave communications
Resumo
A pr´oxima gera¸c˜ao, a quinta (5G), ir´a oferecer velocidades de transmiss˜ao de dados sem precedentes, baixa latˆencia e a capacidade de conectar tudo e todos. No entanto, de maneira a se garantirem estas funcionalidades, alguns obst´aculos tˆem de ser ultrapassados, destacando-se a escassez de largura de banda nas bandas convencionais sub-6GHz. Esta limita¸c˜ao levou tanto a comunidade cient´ıfica como as operadoras de telecomunica¸c˜oes a desen-volverem sistemas que combinam sistemas MIMO massivo e a banda das ondas milim´etricas. Estas tecnologias apresentam uma rela¸c˜ao simbi´otica que permite contornar a escassez da largura de banda e alcan¸car as ele-vadas taxas de transmiss˜ao previstas para os sistemas 5G do futuro. No entanto, o elevado n´umero de antenas previsto para os futuros sistemas de comunica¸c˜oes sem fios torna impratic´avel a implementa¸c˜ao de arquiteturas puramente digitais, tendo em conta as limita¸c˜oes de hardware. Adicional-mente, tamb´em n˜ao ´e razo´avel desenhar-se um sistema que funcione apenas no dom´ınio anal´ogico, implementando beamforming anal´ogico, devido aos maus desempenhos obtidos com este tipo de t´ecnica. Assim, de maneira a se contornarem estas limita¸c˜oes, tˆem sido propostas na literatura arquite-turas h´ıbridas anal´ogico-digitais, onde parte do processamento de sinal ´e feito ao n´ıvel digital e outra parte ´e feito no dom´ınio anal´ogico. Neste tipo de arquiteturas, o n´umero de cadeias de radiofrequˆencia ´e menor do que o n´umero de antenas, reduzindo assim a complexidade a n´ıvel de hardware. Esta disserta¸c˜ao aborda a implementa¸c˜ao e posterior avalia¸c˜ao de um equal-izador iterativo h´ıbrido para sistemas de multiutilequal-izador massive MIMO (mMIMO) que operam na banda de frequˆencias de ondas milim´etricas (30-300GHz). No lado do transmissor, adotou-se como t´ecnica de modula¸c˜ao o CE-OFDM. Esta t´ecnica ´e dada como promissora para futura utiliza¸c˜ao em comunica¸c˜oes sem fios na banda de ondas milim´etricas. Possibilita n˜ao s´o a amplifica¸c˜ao de potˆencia de baixo custo como tamb´em garante uma alta eficiˆencia utilizando amplificadores altamente n˜ao lineares. Cada terminal de utilizador ´e equipado com uma ´unica cadeia de r´adiofrequˆencia (RF). Na transmiss˜ao consideram-se dois tipos de pr´e-codificadores anal´ogicos. O primeiro consiste numa s´erie de faseadores anal´ogicos, enquanto o segundo se baseia nos ˆangulos m´edios de sa´ıda de cada cluster. No recetor adotou-se a implementa¸c˜ao de um equalizador h´ıbrido n˜ao linear, multiutilizador, baseado no princ´ıpio de equaliza¸c˜ao iterative block decision feedback (IB-DFE), desenhado para remover eficientemente as interferˆencias entre porta-doras e utilizadores. Os resultados apresentados mostram que o equalizador proposto converge ap´os um reduzido n´umero de itera¸c˜oes. Al´em disso, o desempenho alcan¸cado est´a pr´oximo do obtido para um sistema totalmente
Keywords 5G, CE-OFDM, full digital architectures, hybrid architectures, massive MIMO, mmWave communications
Abstract
The next generation, the fifth-generation (5G), is expected to deliver un-precedented data transmission speeds, lower latency, and the capacity to connect everything and everyone. However, accomplishing these features faces some challenges, such as the limited availability of bandwidth in the conventional sub-6GHz band. This constraint has induced telecommuni-cations operators and the research community to develop solutions that combine both the use of the millimeter-wave band (mmWave) and massive MIMO systems. These technologies present a symbiotic relationship that can provide the high transmission rates envisioned for future 5G systems and circumvent the scarcity of bandwidth. However, the large number of antennas envisioned for future wireless systems makes it impossible to use a fully digital architecture due to the hardware constraints. Additionally, it is also not resolvable to have a system that works only in the analog domain by employing full analog beamforming since the performance is poor. To overcome these limitations, hybrid analog-digital architectures, where some signal processing is done at the digital level and some left to the analog domain, have been proposed in the literature. In such architectures the number of radiofrequency chains is lower than the number of antennas, thus reducing the hardware complexity.
This work addresses the implementation of an iterative hybrid two-step space-frequency receiver structure for multiuser uplink millimeter wave (mmWave) massive MIMO based systems. We adopt constant envelope OFDM (CE-OFDM), a promising modulation technique for future mmWave wireless communications, that enables low-cost power amplification and high efficiency, using highly nonlinear amplifiers. The user terminals (UTs) are equipped with a single radiofrequency (RF) chain. In the transmission we consider two different analog precoders. The first one consists in a set of random analog phase shifters, while the second is based on the aver-age angles of departure of the channel. On the receiver side, we adopted a hybrid analog-digital nonlinear multiuser equalizer, based on the iterative block decision feedback equalization (IB-DFE) principle, designed to remove inter-user and inter-carrier interferences efficiently. The results show that the proposed equalizer converges requiring only a few numbers of iterations. Furthermore, the achievable performance is close to the one obtained for the full digital counterpart.
Acronyms
0G Generation 0 1G 1st Generation 2G 2nd Generation 3G 3rd Generation 4G 4th Generation 5G 5th Generation 6G 6th Generation 7G 7th GenerationATM Automated Teller Machine AMPS Advanced Mobile Phone Service AWGN Additive White Gaussian Noise BER Bit Error Rate
BS Base Station
CDMA Code Division Multiple Access CE-OFDM Constant Envelope OFDM
CP Cyclic Prefix
CSI Channel State Information
D-BLAST Diagonal Bell Labs Space-Time Architecture DFE Decision Feedback Equalization
DSL Digital Subscriber Line DoF Degrees of Freedom
EDGE Enhanced Data Rate for Global Evolution EE Energy Efficiency
EGC Equal Gain Combining EHF Extremely High Frequency
EC Error Combining
FDMA Frequency Division Multiple Access FFT Fast Fourier Transform
FM Frequency Modulation FSPL Free Space Propagation Loss
GFDM General Field Division Multiplexing GPS Global Positioning System
GPRS General Packet Radio Service
GSM Global System for Mobile Communications GMSK Gaussian Minimum Shift Keying
HSDPA High Speed Downlink Packet Access HSCSD High Speed Circuit Switched-Data HSPA High Speed Packet Access
HSUPA High Speed Uplink Packet Access
IB-DFE Iterative-Block Decision Feedback Equalizer ICI Inter Carrier Interference
IEEE Institute of Electrical and Electronics Engineers IFFT Inverse Fast Fourier Transform
IoT Internet of Things
ISI Inter Symbol Interference
ITU International Telecommunications Union IMT International Mobile Telecommunications IoT Internet of Things
LSAS Large Scale Antenna Systems LTE Long-Term Evolution
MMB Millimiter Wave Mobile Broadband MMS Multimedia Messaging Service mmWave Millimeter Wave
MIMO Multiple-Input Multiple-Output
MISO Multiple-Input Single-Output
MU-MIMO Multi-User Multiple-Input Multiple-Output MMSEC Minimum Mean Square
MCS-L1 L1 Mobile Communications Sytem MRC Maximum Ratio Combining NMT Nordic Mobile Phone
NTT Nippon Telephone & Telegraph WAP Wireless Application Protocol WLAN Wireless Local Area Network LoS Line-of-Sight
WWW World Wide Web
8-PSK 8-Phase Shift Keying
TACS Total Access Communication System
OFDM Orthogonal Frequency Division Multiplexing OFDMA Orthogonal Frequency Division Multiple Access PAPR Peak-to-Average Ratio
QAM Quadrature Amplitude Modulation QoS Quality of Service
QPSK Quadrature Phase Shift Keying RAT Radio Access technology
RF Radio Frequency
SIC Successive Interference Cancellation SISO Single-Input Single-Output
SIMO Single-Input Multiple-Output
SC-FDMA Single Carrier Frequency Division Multiple Access SFBC Space-Frequency Block Code
SINR Signal-to-Interference plus Noise Ratio SNR Signal-to-Noise Ratio
STTC Space-Time Trellis Code SU-MIMO Single-User MIMO
SVD Single Value Decomposition TDD Time Division Duplex
TDMA Time Division Multiple Access UFMC Universal Filtered Multicarrier UHF Ultra High Frequency
UMTS Universal Mobile Telecommunications System
UT USer Terminal
V-BLAST Vertical Bell Labs Space-Time Architecture VLSI Very Large Scale Integration
WCDMA Wideband Code Division Multiple Access
WiMAX Worldwide Interoperability for Microwave Access
Contents
Acronyms . . . i
Contents v List of Figures vii List of Tables ix 1 Introduction 1 1.1 History and evolution of mobile telecommunications . . . 1
1.2 Into 5G and beyond . . . 6
1.3 Motivation and Objectives . . . 9
1.4 Contributions . . . 11
1.5 Layout of the dissertation . . . 11
1.6 Notations . . . 11
2 Basic Concepts 13 2.1 Radio Propagation Characteristics . . . 13
2.1.1 Radio-Propagation Mechanisms . . . 13
2.1.2 Multipath Propagation . . . 15
2.2 MIMO . . . 19
2.2.1 Diversity . . . 21
2.2.2 Receive and Transmit Diversity . . . 24
Receive Diversity . . . 24
Transmit Diversity . . . 27
2.2.3 Multiplexing . . . 28
2.2.4 Interference Cancellation Techniques . . . 30
Successive Interference Cancellation . . . 33
Decision-Feedback Equalization . . . 34
3 5G Technologies 37 3.1 OFDM . . . 38
3.2 Constant Envelope OFDM . . . 42
3.3 Millimeter Waves . . . 46
3.4 Massive MIMO . . . 50
3.5 Hybrid Architectures . . . 54
4 Hybrid Iterative Equalizer for Massive MIMO Millimeter Wave CE-OFDM
Systems 60
4.1 System Model . . . 61
4.1.1 Channel Model . . . 61
4.1.2 Transmitter . . . 62
4.1.3 Hybrid Iterative Receiver Design . . . 64
Receiver General Overview . . . 64
Full Digital Iterative Space-Frequency Receiver . . . 65
Design of Iterative Digital Part of the Hybrid Equalizer . . . 66
Design of Fixed Analog Part of the Hybrid Equalizer . . . 67
4.2 Results . . . 70
5 Conclusion and Future Work 79 5.1 Conclusions . . . 79
5.2 Future Work . . . 81
List of Figures
1.1 Mobile communications evolution [7]. . . 2
1.2 Mobile telecommunications migration [11]. . . 3
1.3 5G use cases [19]. . . 6
1.4 Key requirements for 5G mobile communications [22]. . . 7
2.1 Most common types of propagation mechanisms. . . 14
2.2 Multipath fading environment. . . 15
2.3 Doppler effect illustration. . . 16
2.4 Radiation pattern for different values of N antennas [45]. . . 19
2.5 Single and Multiple antenna configurations. . . 20
2.6 Diversity illustration. . . 21
2.7 Time diversity illustration. . . 22
2.8 Mutual resistance and reactance of two parallel dipoles as a function of the spacing d in wavelengths [45]. . . 23
2.9 Generalized receive diversity scheme. . . 24
2.10 Performance comparison of different combining techniques at the receiver [28]. 26 2.11 MIMO Mr× Mt system. . . 28
2.12 End-to-end wireless communications system. . . 30
2.13 ZF/MMSE receiver equalization with SIC. . . 33
2.14 Block diagram of a SISO IB-DFE receiver. . . 34
3.1 Basic OFDM model. . . 38
3.2 Illustration of the orthogonality concept inherent to OFDM. . . 39
3.3 OFDM signal structure with Cyclic Prefix. . . 40
3.4 Illustration of OFDM and CE-OFDM mapping unit circle [74]. . . 42
3.5 PAPR in OFDM and CE-OFDM systems. . . 43
3.6 Illustration of CE-OFDM system [33]. . . 43
3.7 Peformance of CE-OFDM as a function of the SNR, to obtain a 10E−4 BER [33]. . . 45
3.8 Available spectrum in mmW band. . . 46
3.9 Atmospheric and molecular absorption at mmWave frequencies [77]. . . 48
3.10 Foliage and rain penetration loss [76]. . . 49
3.11 Multi-User interference cancellation via beamforming. . . 51
3.12 Various antenna configurations [82]. . . 52
3.13 Different AAs applications [88]. . . 53 3.14 Fully-connected hybrid precoding architecture for mmW MIMO systems [31]. 54
3.15 Sub-connected hybrid precoding architecture for mmW MIMO systems [31]. . 55
3.16 Virtual sectorization hybrid precoding architecture for mmW MIMO systems [38]. . . 55
3.17 Relationship between emergent 5G technologies [44]. . . 56
3.18 Heterogeneous Networks [95]. . . 57
4.1 Clustered channel representation (Ncl= 4, Nray = 5). . . 61
4.2 Illustration of CE-OFDM transmitter. . . 63
4.3 Illustration of the receiver structure. . . 65
4.4 Performance comparison with U =8, for different values of the modulation index. 71 4.5 Performance comparison of the proposed equalizer for different numbers of users, using the random precoder, with NrxRF = U . . . 72
4.6 Performance comparison of the proposed hybrid equalizer with the full digital counterpart U = 8 = NrxRF. . . 72
4.7 Performance comparison of the proposed hybrid equalizer with the random precoder (U =4, Mt=16, Mr=32, Ncl=4, Nrays=12) with the full digital coun-terpart. . . 73
4.8 Performance comparison of the proposed hybrid equalizer for different number of users and Nrays=5 and NrxRF = U . . . 74
4.9 Performance comparison of the proposed hybrid equalizer with the full digital counterpart, for U = 8 = NRF rx . . . 74
4.10 Performance of the proposed hybrid equalizer with the AoD precoder (U =4, Mt=16, Mr=32, Ncl=4, Nrays=5). . . 75
4.11 Performance of the proposed hybrid equalizer with the AoD precoder (U =4, Mt=16, Mr=32, Ncl=4, Nrays=12). . . 76
4.12 Gap to Digital as a function of the number of RF chains in the receiver for all of the considered scenarios, considering iteration 4. . . 77
4.13 Performance comparison with U =4 and RF=4 with a fixed modulation index, for different modulation schemes. . . 77
4.14 Performance comparison with U =4 and RF=4, for different modulation schemes and different values of modulation index . . . 78
List of Tables
1.1 The evolution of GSM. . . 3
3.1 Parameters for a well designed OFDM system . . . 40
3.2 Attenuation values for different materials [76]. . . 48
Chapter 1
Introduction
This dissertation starts by mentioning some of the minds behind the birth of radio commu-nications. Then we present the background and evolution of mobile communication systems. Some of the essential features of every generation (G) are described, ending with a general overview of the upcoming 5th Generation (5G) of wireless systems. Then the motivations, objectives, and main contributions of the dissertation are shown. We end this chapter by presenting the layout of the dissertation and the list of notations used.
1.1
History and evolution of mobile telecommunications
The late 19th and early 20th centuries were marked by the indirect synergy between some of the greatest minds to this day, culminating in the birth of radio communications. The experiments carried out by Charles Coloumb, Alessandro Volta, Hans Christian Ørsted, Andre-Marie Amp`ere and Michael Faraday in the field of electricity and magnetism are in-herently linked to the famous Maxwell’s laws [1]. Faraday predicted the existence of what we now call electromagnetic fields, as well as that the light itself was an electromagnetic wave [1]. In 1873, James Clerk Maxwell published his great work, his dissertation on electricity and magnetism, making mathematically reasonable Faraday’s theories and observations [2]. After Maxwell’s death (1879), physicist Oliver Heaviside condensed his work into four vector equations, the famous ”Maxwell’s Four Laws of Electricity and Magnetism” (1885) [2]. Until then, everything was theory, since, there was no experimental evidence to sustain these as-sumptions. In 1887, Heinrich Rudolf Hertz was able to experimentally confirm the existence of electromagnetic waves, validating Maxwell’s theory [3].
For many years, one subject raised much controversy; who is the inventor of radio com-munications? Opinions oscillated between Nikola Tesla, Guglielmo Marconi, and Alexander Popov. Commonly, Marconi is regarded as the ”father” of radio communications, having patented his wireless telegraph in 1987, and in 1901 he made history by transmitting the first intercontinental radio signal between England and Canada [4]. Thus radio communications were born, as this was the catalyzer to its dissemination.
After more than one century, wireless communications evolution has been astronomical, revolutionizing the way we live. The sustained development of all technologies inherent to wireless communications provided higher data rates, faster response times, and increasingly fluid means of accessing more content. This evolution can be translated into generations, each
utilizing new standards, different capacities, new frequency bands, new associated techniques, and new functionalities, differentiating them from one another [5], [6]. These generations and their main innovations are depicted in Figure 1.1.
Figure 1.1: Mobile communications evolution [7].
Generation 0 (0G), corresponds to pre-mobile mobile communications technology, such as the radio handsets in some cars [8]. The first generation (1G) was completely analog and allowed us to make voice calls [8]. The second-generation (2G) introduced the concept of digital communications, allowing for a wide area of coverage and capacity as well as com-munication via text messages, short messaging service (SMS) [5],[6],[8]. The third-generation (3G), made possible the authentic ”mobile broadband experience,” considering that it allowed a substantial increase in data transmission rate and network capacity, opening doors to the world of multimedia and internet on a mobile phone [5],[6],[8]. The fourth-generation (4G) enables access to a wide range of telecommunication services, including advanced mobile ser-vices, supported by mobile services and fixed networks. In addition to these advantages, 4G allowed a substantial increase in spectral efficiency and bandwidth [6]. The fifth-generation (5G) promises to revolutionize the telecommunications market and the way we live, allowing for more significant economic growth and the generalization of the digitalization of a hyper-connected society. 5G technology is the catalyzer of technologies such as autonomous-driving, virtual reality, IoT, to name a few.
1G
Numerous breakthroughs have paved the way for mobile cellular networks, being its major catalyst the invention of the microprocessor in 1971, due to its high processing capacities [9]. All 1G commercialized systems were based on circuit-switching technology and exclusively de-veloped for voice services, using frequency modulation (FM), and more specifically Frequency Division Multiple Access (FDMA), to allocate different frequency bands for the various users [5]-[10]. The channels presented capacities around 30kHz, in the 824-894MHz band with ve-locities up to 2.4kb/s [7]. These systems presented numerous constraints, such as reduced capacity, unstable voice links, the size of mobile phones, insecurity of the communications, very low spectral efficiency and interoperability between the different standards [8]-[10].
2G
By the end of the 1980s, first-generation systems felt short, since they were unable to keep up with the technological demands of the market. The maturation of technologies such as very large scale integration (VLSI) and signal processing has opened the door to the digital age [9]. The digital nature of 2G systems resulted in improved spectral efficiency, innovative data
services, such as SMS and multimedia message service (MMS), and enhanced voice services [6]. It should be noted that 2G systems were the first to contain error detection and correction codes in traffic channels, which allowed for significant improvements in voice transmission and ensured reliable data transmissions [9].
The Global System for Mobile Communications (GSM) standard has become the most desirable and most used standard in 2G systems and is still very important today, in the world of mobile communications (40 % of mobile subscribers around the world still use GSM). In Figure 1.2, it can be seen that 2G is heading towards extinction with the rising of 5G, as many operators are disabling their 2G networks.
Figure 1.2: Mobile telecommunications migration [11].
GSM itself has evolved positively over time, with three data services: HSCSD, GPRS and GSM-EDGE [12]. The evolution of GSM, and its transmission rates, both theoretical and practical are depicted in Table 1.1.
Table 1.1: The evolution of GSM.
System Theoretical maximum Realistic
GSM > 40 kbps 9.6 kbps
HSCSD 115.2 kbps 28.8 kbps
GPRS 171.2 kbps 40 kbps
EDGE 384 kbps 60 − 70 kbps
3G
The pursuit for higher data transmission rates and continuous evolution lead to 3G systems. A significant problem of wireless communications until then resided on the non-interoperability between the distinct standards adopted in different parts of the world [6], making global roaming impossible. Considering the limitations and needs mentioned be-fore, the International Telecommunication Union-Radiocommunications (ITU-R) created the
International Mobile Telecommunications 2000 (IMT-2000) global standard. This standard was intended to unify all existing wireless systems (cellular, Wireless Local Area Networks (WLAN), satellite networks and fixed wireless links) in the same frequency band (2GHz) [9], with a bandwidth of 15-20 MHz [13].
In the first phase of IMT-2000 (2002-2005), the objective was to provide services with transmission rates of 144kb/s in rural zones, 384kb/s in suburban/urban environments and 2Mb/s in indoor/outdoor locations near a base station [14]. To meet the requirements im-posed by the IMT-2000, two organizational groups, the 3GPP and the 3GPP2, were created, responsible for 3G standardization, but based on two different inheritances. The 3GPP is based on Wideband Code Division Multiple Acess (W-CDMA) (5MHz bandwidth), GSM and the associated evolutions, while 3GPP2 is based on CDMAOne (bandwidth of 1.25MHz) [15]. 3GPP is responsible for the development and maintenance of UMTS, the European standard for 3G systems and the most used worldwide. 3GPP2 is accountable for the devel-opment and maintenance of the 3G system, CDMA2000, mostly used in the United States [9]. It should be referred, that the first commercial 3G network was launched by NTT DoCoMo in late 2001, in Japan [6].
3G systems made real-time applications a reality, being those considered a mirage in 2G networks. The latter includes applications such as videos on demand, Global Positioning System (GPS), location-based services (find the nearest Automated Teller Machine (ATM), vehicle tracking/parcels, mobile TV, video conferencing, among others [16]. Another key feature introduced in 3G was the ability to transmit and receive data packets while having a voice conversation. Subsequently, to be able to follow the market, two other technologies were developed and implemented in 3G systems, High-Speed Downlink Packet Access (HS-DPA) and High-Speed Uplink Packet Access (HSUPA). The first is a W-CDMA packet-based protocol, which allows downlink speeds of 8-10Mb/s over a bandwidth of 5MHz [13]. The second complements the first, improving uplink speeds of UMTS/WCDMA systems, initially up to 1.4Mb/s and later allowing uplink speeds in the order of 5.8Mb/s [13].
4G
Similarly to its predecessors, 4G is the answer to a given demand. The increasing need for mobile broadband services with better quality and higher data rates has made imperative the creation of a new standard. The ITU-R in 2008, issued a list of requirements for the 4G standard, called International Mobile Telecommunications-Advanced (IMT-Advanced). Some of those requirements are [17],
• An all-IP based packet switching network; • Interoperability between all existing standards; • Download speeds up to 1Gbps;
• Seamless connectivity and global roaming across multiple networks with smooth han-dovers;
• Ability to offer high quality of service for multimedia support;
• Dynamically share and use network resources to support more simultaneous users per cell.
Long-Term Evolution (LTE) was thought to be the answer to that need and it was designed to overcome 3G networks in terms of capacity, user throughput, latency, more efficient use of the radio frequency spectrum and more flexibility. The main difference between LTE and its forerunners relies on the standard communications protocol to send and receive data in packets. Like 3G, LTE networks are IP-based (Internet Protocol), but with a twist, since LTE uses IP even for voice data, making it an All-IP network.
LTE main features are:
• Data rates an order of magnitude higher than the single carrier spread spectrum radio can provide (300 Mbps downlink and 50 Mbps uplink, compared to 14 Mbps uplink and 5.76 downlink in UMTS HSPA) [14];
• Reduced transit times for user packets (reduced latency) an order of magnitude shorter than can be provided in 3G hierarchical networks (5ms for data and under 100ms for signaling)[14].
• The ability for strict Quality of Service (QoS), control of user data flows with the possibility of these being coupled with multiple charging schemes [14].
• Introduction of Orthogonal Frequency-division Multiple Access (OFDMA) as the down-link multiple-access technique and Single-carrier Frequency Division Multiple Access (SC-FDMA) for uplink, enabling high spectral efficiency [18].
Even though LTE networks outperformed the 3G ones, the first LTE release did not fill all the IMT-Advanced requirements; hence, this release was not considered as 4G. LTE enabled a theoretical peak download velocity of 300Mbps and 75Mbps for downlink, which came very short to the required 1Gbps for downlink [18].
Driven by the unfulfillment of LTE and ITU-R’s requirements for IMT-Advanced, 3GGP started to investigate new technologies or techniques that could enhance the capabilities of LTE. The result was a set of specifications called LTE-Advanced. In October of 2010, ITU announced that two systems met the requirements of ITM-Advanced, LTE-Advanced and WiMAX 2.0 [18]. From those two, LTE-Advanced ended up being the winner, and it is today one of the most dominant mobile communications technologies in the world.
1.2
Into 5G and beyond
Every new generation of wireless networks delivered faster speeds, more functionalities, more reliable communications, and potentially new jobs and industrial opportunities. 1G introduced the first cell phone, 2G introduced text-messaging, 3G made ubiquitous online services a reality and 4G delivered the speeds we have today and interoperability between the different standards. 5G follows this trend, but with a twist, since it is expected to be a key driver for industrial and societal changes [19]. It will be a vital component of the Networked Society and will help realize the vision of virtually unlimited access to information and the sharing of data anywhere and anytime for anyone and anything [20].
Unlike its predecessors, the fifth generation is being designed to not focus solely on mobile connectivity for people [20]. Rather, 5G aims to provide ubiquitous connectivity for any device and any application that may benefit from being connected [20]. Obviously, mobile broadband will continue to have much weight in the design of future systems, meaning that the search for technological developments that provide higher system capacity and higher data rates will remain as a constant in the minds of every person working in the field. However, 5G main goal is to proliferate the connectivity between objects and devices, paving the way to a wide range of new services and associated business models, enabling automation in various industry sectors and vertical markets, more specifically, energy, e-health, smart cities, connected cars, industrial manufacturing, and much more [21]. The upcoming networks will not support only human-to-machine communications, but also machine-to-machine, opening the doors for many applications that will make our life safer and more convenient [21], [22]. Figure 1.3 depicts the most important 5G usage scenarios.
Figure 1.3: 5G use cases [19].
year of 2017. Reaching as much as 11.5 exabytes1 per month at the end of 2017, up from 6.7 exabytes per month at the end of 2016 [23].
Figure 1.4: Key requirements for 5G mobile communications [22].
The 3G Public-Private Partnership (3GPPP) is a collaborative initiative that compounds the biggest players in the scope of the telecommunications industry, the European Com-mission and European Information and Communications Technology (ICT), manufacturers, telecommunications operators, service providers and research institutions [24]. As the 3GPP, the 3GPPP is responsible for the development and standardization of 5G networks.
In Figure 1.4 it is shown the key parameters for 5G. These capabilities include very high achievable data rates, very low latency, and ultra-high reliability. Furthermore, 5G wireless access needs to support a massive increase in traffic in an affordable and sustainable way, implying a need for a dramatic reduction in the cost and energy consumption per delivered bit. 5G wireless access will be realized by the evolution of LTE for the existing spectrum in combination with new radio access technology (RAT) primarily targeting a new spectrum. Key technology components of 5G wireless access includes an extension to higher frequency bands, advanced multi-antenna transmission, lean design, user/control separation, flexible spectrum usage, device-to-device communication, and backhaul/access integration.
The revolutionary scope, demanding requirements and the consequent advantages of the envisioned 5G networks, demand for new architectures, methodologies, and technologies [25], [22], [26], [20], [21], [22]. Namely, energy-efficient heterogeneous frameworks, cloud-based communication (software-defined networks (SDN) and network function virtualization (NFV), full-duplex radio, self-interference cancellation (SIC), device-to-device (D2D) commu-nications, machine-to-machine (M2M) commucommu-nications, dense-deployment, security-privacy
protocols for communication and data transfer, backhaul connections, massive multiple-input and multiple-output (mMIMO), multi-radio access technology (RAT) architectures, and tech-nologies for working on millimeter wave (mmWave) 30–300 GHz [25], [22], [21]. Interestingly, the 5G networks will not be an enhancement of 4G networks in terms of capacity; they will encompass a system architecture visualization, conceptualization, and redesigning at every communication layer.
5G is not yet disseminated worldwide, and the research community is already setting his eyes in defining the sixth-generation (6G). For now, everything is conceptual, but some guidelines have already been envisioned. At the Brooklyn 5G Summit, several speakers stated that 2019 is the year zero for 6G [27]. The terahertz waves band (300GHz - 3THz) is seen as a key component of the next generation of wireless, and some preliminary research has already been done [27]. Terahertz waves have even shorter wavelengths and higher frequencies than millimeter waves, hence terahertz systems should be able to carry even more data and transmit it at a higher rate. The latter indicates that future 6G systems may address some areas that 5G lacks, such as not being able to deliver high enough data throughput or low enough latency [27]. Furthermore, 6G is expected to guarantee a global cover of mobile communications, which could be possible by means of satellite technology [28].
1.3
Motivation and Objectives
Bandwidth is one of the most critical and expensive resources envolving radio telecommu-nications. However, the conventional sub-6GHz bands are getting very crowded, making it mandatory to find solutions to circumvent this limitation. The exploitation of the millimeter wave (mmWave) frequency band has been proposed as a solution to this problem [29]. The use of mmWave bands is typically associated with massive MIMO (mMIMO)technology, to compensate for the high attenuations when using mmWave frequencies. Moreover, the smaller wavelengths enable the use of mMIMO, since it allows packing more antennas in the same space [30].
The implementation of mMIMO systems poses some challenges, mainly if each antenna is connected to one dedicated radio frequency (RF) chain, leading to excessive energy con-sumption and hardware costs, due to the mixed-signal components like analog-to-digital com-ponents not to mention baseband processing complexity [31]. One possible solution to this problem is implementing a number of RF chains lower than the number of antennas, using an analog front-end, and allowing less complex processing for the digital part, i.e., a hybrid analog-digital architecture [32]. On the other hand, orthogonal frequency division multiplex-ing (OFDM) is an efficient technique to mitigate the effects of inter-symbol interference in frequency selective channels, but the high signal fluctuations lead to large peak-to-average power ratio (PAPR) [33], making them prone to strong nonlinear distortions caused by power amplifiers [34]. This impairment can be solved by applying constant envelop modulations, namely the constant envelop OFDM (CE-OFDM), which can present better performance than OFDM for realistic systems [35].
Some hybrid transmit and receive beamforming approaches have been recently proposed in the literature [29]-[36]. In [29], an iterative block space-time equalizer was proposed for multi-user uplink systems. It was designed based on the IB-DFE principle, which efficiently separates the spatial streams and/or removes the multi-user interference [37]. For broadband multi-user downlink mmWave mMIMO-OFDM systems, a unified heuristic design was devel-oped in [38] by maximizing the overall spectral efficiency, and in [36] hybrid precoders aimed to minimize the total transmit power of the base station, considering the coverage of signaling and data rate of users.
Regarding CE-OFDM, some designs are addressed in [35],[39],[40]. In [35], the perfor-mance of CE-OFDM and other modulations were evaluated, and it can be seen that the robustness, coverage, and capacity are higher for CE-OFDM. In [39], a receiver with iterative detection was proposed for a multi-stream CE-OFDM system, based on an amplitude-phase demodulator. Finally, an iterative space-frequency equalizer, based on the IB-DFE principle, was designed in [40], where the equalizer matrices are obtained by minimizing the overall mean square error (MSE) of all data streams at each subcarrier. To the best of our knowledge, hy-brid beamforming techniques for multi-user broadband mmWave massive MIMO CE-OFDM systems were not yet addressed in the literature.
Therefore, the purpose of this dissertation is to implement and evaluate a two-step space-frequency receiver structure for multi-user uplink broadband mmWave mMIMO CE-OFDM systems. In the transmitter side, we design low-complexity user terminals (UTs), each with only one RF chain. We consider two different analog precoders. The first one is designed
considering that the UT has no channel state information (CSI), and it is implemented with a set of random phase shifters. The second analog precoder takes CSI at the UT into consid-eration, specifically, the average angles of departure (AoD) of the clusters belonging to each UT.
At the base station a nonlinear multi-user equalizer, based on the IB-DFE principle, and using a hybrid full-connected analog-digital structure, is designed to efficiently remove the inter-user and inter-carrier interferences. The proposed hybrid multi-user equalizer is designed by minimizing the sum of the mean square error of all sub-carriers, which is demonstrated to be equal to minimizing the weighted error between the hybrid equalizer and the full digital one. The analog part of the equalizer is kept constant over the subcarriers and iterations, while the digital part is computed iteratively on a per sub-carrier basis. The implemented hybrid multi-user equalizer is evaluated under realistic millimeter wave channel models proposed in the literature.
1.4
Contributions
The work developed in the ambit of this dissertation contributed to the proposal of a hybrid nonlinear multi-user equalizer for mmWave massive MIMO CE-OFDM systems, which originated the following article:
• R. Magueta, R. Enes, S. Teodoro, A. Silva, D. Castanheira, R. Dinis and A. Gameiro, ”Hybrid Nonlinear Multiuser Equalizer for mmWave Massive MIMO CE-OFDM Sys-tems”, IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Ra-dio Communications (PIMRC): Special Sessions : Massive MIMO Beyond 5G, Septem-ber 2019, Istanbul, Turkey.
1.5
Layout of the dissertation
The remainder of this document is organized as follows:
Chapter 2 introduces the reader to radio communications basic concepts. We start by presenting an overview of radio propagation characteristics, some of the techniques enabled by MIMO systems, and finally, we present several interference cancellation techniques.
In Chapter 3, we discuss some of the most relevant technologies related to the design of future 5G networks. We tend to emphasize technologies that were exploited in the develop-ment of this dissertation, more specifically, CE-OFDM, Massive MIMO, mmWave band and Hybrid Architectures.
In Chapter 4, the implemented hybrid iterative equalizer for mMIMO mmWave CE-OFDM system is described. First, we contextualize the developed work in future 5G networks. Afterward, we describe the considered system, starting by the transmitter, the utilized broad-band mmWave channel, and we finalize by describing the iterative receiver scheme. Lastly, we assess the proposed hybrid iterative receiver scheme’s performance, and we compare it with a full digital (non-hybrid) counterpart.
Lastly, in Chapter 5, we finalize this dissertation by pointing out the main work conclu-sions, and we present possible future work guidelines.
1.6
Notations
In this dissertation we use the following notations: Boldface capital letters denote matrices and boldface lower-case letters denote column vectors. The operations (.)T, (.)H,(.)∗ and
tr(.) represent the transpose, the Hermitian transpose, the conjugate and the trace of a matrix, respectively. The operators diag(a) and diag(A) correspond to a diagonal matrix with diagonal entries equal to a vector a and a diagonal matrix with entries equal to the diagonal entries of matrix A, respectively. The operator kAkF stands for the Frobenius norm of the matrix A. A(N, M ) represents the element of the nth row and mth column of A. {αk}Sk=1 represents a S-length block. eu ∈ CU is a U -length vector of zeros with the uth
entry equal to one. Im(c) and Re(c) correspond to the real and imaginary part of c. E stands for the expectation operator. Finally, IN is the identity matrix with size N × N .
Chapter 2
Basic Concepts
We live in a digital era, where almost everything is possible if we have an internet connec-tion. Internet is a seemingly limitless space where people have access to a similarly infinite library of knowledge, can perform actions that make our life easier (online banking, online shopping) and have access to ubiquitous social interactions (social networks), to name a few. The Internet is inherently connected to wireless communications, and the latter has been in continuous evolution since G. Marconi transmitted the first radio signal. Since that first radio communication, the demand for higher data rates, bigger coverage distance, higher capacity networks, and better Quality of Service (QoS), remained as a constant in the mind of every person working in the field.
This chapter covers some of the most relevant basic concepts related to wireless systems. We will emphasize some important topics, such as radio-propagation characteristics, MIMO, diversity, multiplexing, and interference cancellation techniques.
2.1
Radio Propagation Characteristics
Radio propagations are highly random and impose some challenges that can jeopardize the performance of wireless communications systems. Small things like, the distance between the transmitter and the receiver, terrain profile, the nature of the surroundings, the frequency used and the speed and direction of the user terminal, are variables that pose a severe threat to wireless communications. In this section, we present some basic concepts about radio propagation.
2.1.1 Radio-Propagation Mechanisms
The ideal link in wireless communications goes by the name of line-of-sight (LOS), i.e., the shortest possible distance between the transmitter and receiver, with a few or none ob-jects in that same direct line [41]. In any mobile-radio communication (LOS included), the electromagnetic waves can meet objects in its path. Many elements, such as walls, buildings, street signs, clouds, and trees, can be prejudicial to wireless communications.
The distinct radio propagation methods are shown in Figure 2.1, where scattering, diffrac-tion, and reflection are generally the most dominant of the bunch.
Figure 2.1: Most common types of propagation mechanisms.
Diffraction effect is when a radio wave is obstructed by surfaces with sharp, irregular edges. The radio wave is ”broken up” and bends around the corners of the object, creating a few replicas of the transmitted signal. It is this property that allows radio waves to operate without a direct line of sight. An example would be radio waves bending around mountains. Absorption is a phenomenon that is manifested when a radio wave passes through an object, and a portion of the total strength of the signal is absorbed as heat; hence the strength of the signal will be weaker if it comes out the other side. An example would be radio waves going through trees.
Reflection occurs when a radio wave interacts with an object that is larger than the wavelength of the radio signal. The radio wave is then reflected off the surface. An example would be radio waves bouncing off walls.
Scattering happens when an electromagnetic wave hits a rough surface area of an object with an irregular shape, which is smaller than the signal’s wavelength. The wave ricochets on the object, creating several replicas of the original. Examples would be radio waves hitting street signs, lamp posts, and foliage.
Refraction arises when a radio wave meets an object with a density different from its current medium, generating a shift in the direction of the signals. An example would be radio waves propagating through clouds.
The before mentioned phenomenons can put severe constraints on the performance of wireless communication systems, but some of them, such as scattering and absorption can also be exploited to enhance it. Even though this affirmation may sound contradictory, some of the ways one can utilize or circumvent these phenomenons will be described throughout this Chapter and in Chapter 3.
2.1.2 Multipath Propagation
In mobile communications, the received signal is a sum of signals coming from different paths, due to an inherent feature of this kind of communication known as multipath prop-agation [42], [41], [43]. Each of these paths is composed of a distinct set of components: random amplitude, random phase, random Doppler shift, and a random delay. For a proper understanding of these concepts, this subsection will cover the most important concepts as-sociated with multipath propagation. In Figure 2.2, it is depicted a multipath downlink communication, between a BS and a mobile UT.
Figure 2.2: Multipath fading environment.
As it was previously mentioned the received signal is a sum of all of the signal’s replicas, the LOS component and the L(t) multipath reflections. One can express the received signal as in (2.1) [42], [43] r(t) = Re L(t) X n=0 αn(t)u(t − τn(t))e−jφn(t)ej2πf0t (2.1)
where αn(t) represents the amplitude of the nth path, u(t) is the complex envelope of r(t),
τn(t) the delay of a nth path, φn(t) is a function dependent of the delay and the Doppler
effect and f0 is the carrier frequency. Please note that, that n=0 corresponds to the LOS
path.
The variable αn(t) is a function that depends on the attenuations (path loss and
shad-owing), reflection coefficient and antenna characteristics, and it can be mathematically rep-resented as
αn(t) = An(t)ρn(t)
p
Gn(t) (2.2)
An(t) represents the shadowing and path loss components, ρn(t) the reflection coefficient
φn(t) may be expressed as
φn(t) = 2π(f0+ fD,n(t))τn(t) − 2πfD,n(t)t (2.3)
being fD,n(t) = vcosθn(t)λ, the Doppler shift, with θn(t) representing the angle of arrival of a
given path.
Doppler Effect
In all mobile communications (except satellite), the location of the base station is fixed, but the user terminal (UT) is mobile. The latter can provoke a phenomenon called, the Doppler effect (i.e., it is observed a shift in frequency of the transmitted signal when received) [41]. The frequency shift varies considerably as the direction and speed of the mobile changes. As the distance between the source and the receiver decreases, the frequency of the received signal increases relative to the source, and when the distance increases, the frequency decreases. The Doppler shift is traduced as
fr= ft− fd= ft−
ft
c(vcosθ) (2.4)
where fr is the frequency of the received signal, ft the frequency of the transmitted signal, fd
is the Doppler shift or Doppler frequency, c is the speed of light, vcosθ represents the speed at which the receiver is moving in the direction of transmitter and θ indicates the angle between the propagation direction and moving direction of the mobile device (receiver).
Figure 2.3: Doppler effect illustration.
One may think that these frequency shifts do not pose a significant threat for wireless communications, since proper frequency tuning can be performed in the receiver by shifting all the received wave components by the same amount. However, different paths have different Doppler shifts, and the superposition of Doppler-shifted waves creates a sequence of dips [41]. Doppler frequency is thus a measure for the rate of change of the mobile channel, and the superposition of many slightly Doppler-shifted signals leads to phase shifts of the total received signal that can impair the reception of angle-modulated signals [41].
Delay Spread and Coherent Bandwidth
As it was mentioned before, multipath propagation is an inherent feature of mobile com-munications channels, which results in a received signal that is dispersed in time [42], [41], [43]. Let us consider a Dirac impulse δ(τ − τn), where the channel impulse response (CIR)
can be expressed as h(τ, t) = L(t) X n=0 αn(t)e−jφn(t)δ(τ − τn(t)) (2.5)
where t is the instant when the impulse is observed, τ − τn as the instant when the impulse
has been transmitted to the channel and τnrepresents the delay for the multipath component
n for the current observation. If the relative delay between different replicas is bigger than the symbol time duration, we will get inter-symbol interference (ISI), since the replicas occur in instants where other symbols are already being transmitted, which may lead to signifi-cant distortion. If the relative delay between replicas is smaller than the symbol duration, distortion may be considered as negligible.
It was mentioned before that each path has its own delay, and the time dispersion may lead to ISI. The delay spread is a measure of the multipath profile of mobile communications channel [41]. Generally, the delay spread is defined as the difference between the time of arrival of the earliest received component and the time of arrival of the latest multipath received component. Standard deviation is a common metric used to measure the delay spread, being this metric widely known as root-mean-square delay spread στ.
Another important metric is called coherence bandwidth Bc. It is a statistical measure of
the range of frequencies over which the channel can be considered as flat, meaning that all spectral components have equal gain and linear phase [41]. Moreover, all symbols transmitted within the coherence bandwidth will fade simultaneously. The Bcis inversely proportional to
the delay spread (στ),
Bc=
1 στ
(2.6) Both the delay spread and coherent bandwidth are metrics that describe the time dis-persing nature of a given mobile channel [43], [41]. If we consider the transmitted signal bandwidth Bs and the symbol duration Ts and we acquire either the Bc or the στ, it is
possible to determine if the channel is experiencing flat fading or frequency-selective fading. Flat fading means that the transmitted pulse bandwidth is less than the Bc and that the
maximum channel delay is much smaller than the symbol duration τmax << Ts, mitigating
the effects of ISI. Frequency selective channels are channels where ISI is observed, due to the Bs>> Bc and that the delay of some paths is larger than the Ts.
Doppler Spread and Coherence Time
The Doppler spread Bdis a measure of spectral broadening caused by the Doppler effect
[42], [43], [41]. It is defined as the frequency range over which the Doppler spectrum is non-zero. When the Bs is much larger than the Doppler spread, the effects caused by the latter
are negligible at the receiver [41]. The Doppler spread can be expressed as σD 6 2 | fd,max |.
The coherence time Tcis a statistical measure of the time duration over which the
propa-gation channel can be considered invariant (highly correlated) [41]. If we guarantee that two different transmitted symbols are spaced in time by more than the Tc, the channel will likely
affect the two signals differently (uncorrelated); otherwise, they suffer from similar fading. The Doppler spread is inversely proportional to the coherence time,
σD =
1 Tc
(2.7) Both of these metrics are used to describe the dispersion nature of the mobile channel, and they can help to determine if the channel is experiencing fast fading or slow fading. The Doppler spread is an important metric to consider when designing a network. If we imagine a scenario where we have a system operating at 2GHz and another one running at 1GHz, the first has double the Doppler spread that of the first, thus the Tc is half as large [41]. The
latter gives rise to faster fading, with shorter fade duration, and channel measurements that become outdated twice as fast.
2.2
MIMO
The study of antenna arrays is widely disseminated in the literature and is a subject of high interest in the research field. When antennas are aggregated, either in the transmitter, the receiver or both, an important improvement of these systems is verified in terms of spectral efficiency, when compared to systems with only one antenna [44].
Single-Input Output (SIMO), Input Single-Output (MISO) or Multiple-Input Multiple-Output (MIMO) systems enable the design of networks with higher capacity and reliability in comparison to Single-Input Single-Output (SISO) systems. These significant improvements are obtained due to the high gains associated with multiple antenna systems in terms of diversity, multiplexing, and antenna gain (directivity), when the antennas are aggre-gated for the same frequency. Antenna array gain or directivity is the maximum obtainable gain in a given angular direction [45]. As the number of antennas increases, the directivity increases, and a narrower radiation diagram is observed. Observing the Figure 2.4, the prop-erty mentioned above is validated. It should be noted that, as the number of antennas in an array increases, the power of the secondary lobes decreases, allowing the attenuation of possible interferences.
Figure 2.4: Radiation pattern for different values of N antennas [45].
Owing to this particularity, one can perform beamforming, a technique widely used in wireless communications and radars. Beamforming has drawn much attention from the scien-tific community, due to its viability in massive MIMO systems to be deployed in the upcoming 5G Base Stations. The latter will be discussed in more detail later in this dissertation. There are three different types of multiple array systems, Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO) and Multiple-Input Multiple-Output (MIMO). All pos-sible antenna systems are depicted in Figure 2.5.
Figure 2.5: Single and Multiple antenna configurations.
Observing Figure 2.5, one can easily conclude that the SISO configuration is the simplest way of creating a radio link, but it presents disadvantages in terms of fading and interference mitigation, when compared with SIMO, MISO and MIMO systems.
SIMO systems enable the implementation of diversity techniques in the receiver, since the signals can reach it from several independent paths, thus, attenuating the effects of fading and increasing the reliability of the transmission. The main disadvantage related to this type of configuration is the need for more complex processing algorithms at the receiver side; thus, it may be limited by cost, power consumption and the BS’s/UT’s dimensions [42], [44].
MISO like SIMO permits the implementation of diversity techniques, but in this case, only at the transmitter side. The main advantage of using this antenna configuration is that the redundancy coding/processing is moved to the transmitter side, which is convenient for downlink where the transmitter is the base station and thus with more computation capacity. Hence, the design of our cellphones is simplified since the number of antennas, and the level of processing can be reduced, saving space, power consumption and software complexity, leading to a decrease in terms of cost and size of our devices [42], [44].
MIMO configurations mean that there are multiple antennas, both at the transmitter and receiver side. The previously mentioned schemes (SISO not included), provide diversity and antenna gains, but in some cases, multiplexing gains are preferred. Diversity is a method that improves the reliability of a system by using two or more independent and uncorrelated com-munication channels, while multiplexing is a method that is used to increase the transmission rate of a system. We give more information about these methods later on this Chapter.
2.2.1 Diversity
The concept of diversity was introduced in 1931, by Peterson, Beverage, and Moore, stat-ing the followstat-ing: ”Diversity is the principle whereby advantages taken of the fact that fadstat-ing does not occur simultaneously on: A) parallel channel of different frequencies; B) antennas of different polarizations on one frequency; or C) on spaced antennas of one polarization on one frequency” [46].
All wireless communications share a common constraint, attenuations, and diversity plays an essential role in mitigating its effects. By providing the receiver with multiple versions of the same signal, one reduces the signal’s degradation and improves link performance. If a radio signal is received through only one channel, and that same channel suffers from pro-found fading events, one cannot guarantee a positive performance of the wireless system. Moreover, if that unique link fails, all the information is lost. Alternatively, by generating multiple independent and uncorrelated paths, we increase the probability of having an excel-lent performance of the wireless system, since if one radio path undergoes a deep fade, another independent path may have a strong signal [42], [47]. Summing up, diversity is enabled by multiple antenna systems, and it consists of sending the same information through different and independent paths, providing the receiver with various looks at the signal to improve reception. Figure 2.6, illustrates a fictitious communication between a BS and a UT.
Figure 2.6: Diversity illustration.
Diversity can be provided across time, frequency, and space. The latter explains why it is depicted (T#, F#, S#) as the links legend in Figure 2.6.
In this section, it will be given more in-depth information about the most important diversity techniques, such as time, frequency, space, and polarization. Furthermore, it will be
discussed, in a superficial manner, the main differences between transmit and receive diversity. Time Diversity
Time diversity is achieved by transmitting the same information in different time-slots if they are separated by much more than the coherence time, exploiting the time-varying nature of wireless channels. The coherence time depends on the Doppler spread of the signal, which is dependent on the mobile speed and the carrier frequency [41]. That being said, one may imagine a scenario where both the transmitter and receiver are not moving, meaning that the coherence time can be quite long, hence, reducing the effectiveness of this kind of diversity [41]. This is the main reason why time diversity is not used in delay-sensitive applications, such as voice communications. Time diversity can be obtained by using channel coding and interleaving across different coherent time periods [42], [47].
The simplest way to obtain time diversity via coding and interleaving is by using repetition codes. The coding provides redundancy, while interleaving guarantees that the bits associated with a codeword have enough time separation between them to undergo different fading. The main drawback associated with this approach is a decrease in the data rate by a factor of L (with repetition coding), where L represents the number of independent paths [42]. Figure 2.7 illustrates the advantages of interleaving in radio communications showing that if a path suffers from deep fading, only 1 bit of each codeword is affected.
Figure 2.7: Time diversity illustration. Frequency Diversity
Frequency diversity is obtained by transmitting the same narrowband signal at different carriers. For the received signals to be statistically independent or at least uncorrelated, the carrier frequencies must have a separation that is greater than the coherence bandwidth Bc
of the radio channel.
The coherence bandwidth depends on the multipath delay spread of the channel, and the latter enlightens one obvious limitation of this technique: guaranteeing that the receiver is able to pick up all these signals, due to the need for having multiple receivers to tune
to these frequencies [41]. Typically, before the transmission, the signal is spread over a broad bandwidth utilizing multicarrier modulation, frequency hopping, to name a few. These bandwidth broadening techniques indicate the main drawback associated with this type of diversity, the need for more bandwidth [42], [41].
Space Diversity
Space diversity is one of the most used diversity techniques, mainly because it is easy to implement, as it takes advantage of the random nature of propagation in different directions [41]. Space diversity can be achieved by employing multiple antennas at the receiver and/or transmitter to create spatial uncorrelated channels, reducing the effects of fading, since it is improbable that uncorrelated and independent channels fade simultaneously. One significant constraint when applying this technique is the physical spacing between antennas. One must guarantee that the antennas are separated by more than the coherence distance, decreasing the effects of a phenomenon called mutual coupling and increasing the probability that the signals arriving at the different antennas of the array are uncorrelated and independent [41], [45]. Mutual coupling, to a certain extent, is inherent to all antenna arrays and derives from the interaction between electromagnetic waves of adjacent antenna elements. It affects the radiation pattern of the antenna array elements, which can cause destructive/constructive addition of the incoming electromagnetic waves, highly decreasing the system’s performance [45].
Another impairment caused by mutual coupling is that it causes alterations in the current distribution in an antenna array, changing the element’s input impedance. In Figure. 2.8, it is depicted the mutual impedance of two parallel side by side dipoles. It can be observed that as the spacing between antennas increases the strength of coupling decreases.
Figure 2.8: Mutual resistance and reactance of two parallel dipoles as a function of the spacing d in wavelengths [45].
Considering the impairments caused by mutual coupling, the study of this phenomenon gained a lot of attention from the research community, due to the expectancy of having hundreds of antenna elements in the upcoming 5G BSs.
2.2.2 Receive and Transmit Diversity
We have already highlighted that diversity can be achieved by exploiting different dimen-sions such as, frequency, time and space. In this section we will shed light on the diversity techniques that are used, both in the receiver and transmitter when using the space dimension. One must refer that unlike the first two mentioned diversity techniques (time and frequency), it is possible to generate independent fading paths without an increase in transmit signal power or bandwidth [43] when using space diversity techniques.
Receive Diversity
Receive diversity can be performed when multiple antennas are employed in the receiving end of a wireless communications system. Since it is easier and more cost effective to em-ploy multiple antennas at the BS than in the UT, this technique is mostly associated with uplink communications. The received signals can be combined to provide improved system performance, in terms of reducing the Signal-to-Noise Ratio (SNR) required to deliver an acceptable level of bit error rate (BER) [41]. Receive diversity can achieve both diversity gain and antenna gain. The first is related to the fact that the channels used in the transmission are independent, whereas the second is achieved considering that the noise terms added at the receiver are independent [42].
In Figure 2.9, it is illustrated the considered generalized received diversity scheme with Mt transmit antennas and Mr receive antennas.
Figure 2.9: Generalized receive diversity scheme.
Observing Figure 2.9 we can see that the received signal model is given by, y1 .. . yMr = h1 .. . hMr s + n1 .. . nMr (2.8)
where, y ∈ CMr is the received signal, the channel matrix between the transmitter and the
receiver is H ∈ CMr, s the transmitted data symbol and n ∈ CMr is the noise matrix.
The estimated symbols ˆs can be represented as,
ˆ s = [ g1, ..., gMr ] y1 .. . yMr + [ g1, ..., gMr ] n1 .. . nMr (2.9)
being the vector g ∈ CMr the equalizer used to obtain ˆs.
The main methods used for combining the multiple replicas of a given signal are maximal-ratio combining, equal-gain combining, and selection combining, which can all employ linear receivers [41].
Maximal ratio combining (MRC) is a technique that outputs the weighted sum of all diversity branches, in other words, a path that presents a higher SNR receives an higher weight and vice versa. All of the received signals are co-phased and summed with optimal weighting, maximizing the combiner’s output SNR.
• MRC equalizer:
gm = h∗m, m = 1, ..., Mr (2.10)
• Signal at the output combiner:
ˆ s = Mr X m=1 |hm|2s + Mr X m=1 h∗mnm (2.11) • Output SNR: SN RP = PMr m=1|hm|2 σ2 (2.12)
where σ2 represents the noise power.
Equal Gain Combining (EGC) is a simpler version of maximal ratio combining. The only difference is that in this technique, the diversity signals are summed up with equal weights, after co-phasing.
• EGC equalizer:
gm=
h∗
|h|, m = 1, ..., Mr (2.13)
• Signal at the output combiner:
ˆ s = Mr X m=1 |hm|s + Mr X m=1 h∗m |hm| nm (2.14)
• Output SNR: SN RP = PMr m=1|hm| 2 σ2 (2.15)
Selection Combining (SC) unlike the two previously mentioned techniques, does not per-form the addition of all the diversity signals. It is based on the premise that the received signals do not have low SNR at the same time; hence, the diversity branch with the highest SNR is selected, and all the other branches are ignored.
• Selection: |hmax| = max[ |h1|, ..., |h|Mr ] (2.16) • Output SNR: SN RP = |h|2 max σ2 (2.17)
In Figure 2.10 [16], it is shown a performance comparison of the previously described combining techniques.
Figure 2.10: Performance comparison of different combining techniques at the receiver [28].
As it can be observed, the MRC presents the best results of the bunch, but that is achieved at a cost. The MRC requires phase information and accurate measurement of the magnitude of the signals, increasing the system’s complexity. Like the MRC, the EGC requires phase information, but it does not require an accurate measurement of the magnitude of the signals since all signals are added up with equal weights. The SC combiner is the easiest to implement, considering that it only requires measurement of SNR at each element.
Transmit Diversity
Analogously to receive diversity, one may achieve transmit diversity by employing multiple antennas on the transmitter. Transmit diversity is applied when more space and power processing capability is available at the transmitter side. Therefore, transmit diversity has high value for downlink communications, considering that it is easier to employ multiple antennas at the base station than on a user terminal, due to power consumption, space, and processing constraints. The system works as in the receiver diversity scheme; thus, transmit diversity provides the same diversity gains as receive diversity, provided that the channel is known at the transmitter side. Transmit diversity can be achieved by using two different techniques: closed loop techniques or open loop techniques.
Closed loop techniques take into consideration the channel state information (CSI), which is related to the known channel properties of the communication link. The transmitter uses channel information to enable simple spatial diversity or beamforming techniques that increase the system’s effective SNR and potentially simplify the receiver architecture.
CSI knowledge can be acquired using two different methods: Time Division Duplex (TDD) or Frequency Division Duplex (FDD). TDD is applied by taking advantage of the channel reci-procity between the transmit and receive links since a single frequency is used. The assigned band is shared by alternating time slots to transmit and receive operations. Consequently, the BS can measure the channel gain and phase on uplink communications, and then use these measurements in communicating back to the UT [43]. In an FDD system, there is no channel reciprocity, being the CSI obtained through feedback in the uplink. Unlike TDD, FDD makes use of two different carrier frequencies for uplink and downlink communications [43]. Both of these techniques present benefits, depending on the application. The choice between one of these techniques is imposed by existing industry standards and spectrum regulation rules. In addition, when the transmitter has CSI knowledge, it can perform beamforming to achieve both diversity and array gains [48].
Unlike closed loop techniques, open loop techniques, do not require CSI availability at the transmitter side. There are a considerable amount of widely used techniques, such as Space-Time/Frequency Coding (STBC or SFBC), Space-Time Trellis Code (STTC). Alam-outi developed a particularly prevalent and straightforward scheme in [49]. In AlamAlam-outi’s space-time code, two transmit antennas use a simple orthogonal repetition code, which is de-coded in the receiver using maximum-likelihood decoding [42], [43]. Due to its simplicity (does not require bandwidth expansion) and performance, this scheme is adopted in LTE standard [42]. A more complex STBC is called the Tarokh Code, which enables the implementation of diversity in a system with more than two transmit antennas. Despite the diversity gains obtained with this technique being higher than with Alamouti coding, this scheme requires bandwidth expansion and the codes are slightly more complex [42]. STBC codes provide maximum diversity order using simple decoding techniques but do not provide coding gain. To achieve both coding and diversity gains, one can implement STTC codes. The latter considers joint design of channel coding, modulation, transmit and receive diversity schemes, which provide higher gain, at the cost of increased complexity.
