L’évolution des communications sans fil doit répondre à la croissance exponentielle de la consommation de données. On prévoit une augmentation du débit allant jusqu’à 1000 d’ici 2020. Cependant, pour atteindre ce but, plusieurs ingrédients sont essentiels. La limitation majeure des systèmes sans fil est l’interférence à cause de la réutilisation des fréquences. C'est un problème qui existait depuis toujours et notamment à partir de la 3G. On croit que ce problème sera notamment plus grave dans la 5G, et cela à cause de la densification prévue des réseaux. L’utilisation de l’OFDM en 4G a mené à la gestion de l’interférence par coordination dynamique des blocs de ressources. Or, cela n’a permis qu’une augmentation modeste du débit. Une nouvelle technique de gestion de l’interférence fut née il y a 5 années. Cette technique s’appelle l’alignement d’interférence (IA). L’IA permet d’avoir une capacité égale à la moitié de la capacité d’un système sans interférences. Cette technique suppose que chaque transmetteur (TX) connait les canaux non seulement envers les récepteurs (RX)s mais les canaux de tous les TXs vers tous les RXs. Une technique d’interférence plus récente qui améliore l’IA, c’est le massive MIMO, ou les TXs sont équipés d’antennes à grande échelle. l’idée est motivée par plusieurs simplifications qui apparaissent en régime asymptotique ou les stations de base ont un trés grand nombre d’antennes. Le but de cette thèse est d’introduire des solutions complètes et réalistes pour la gestion d’interférence en utilisant le massive MIMO dans un scénario multi-cellules multi-utilisateurs. Notre travail traite surtout le problème de la connaissance imparfaite des canaux. / The evolution of wireless communication must meet the increasingly high demand in mobile data. It is expected to increase the maximum rates of wireless by a factor of 1000 by 2020. Meanwhile, it is clear that to reach this goal, a combination of different ingredients is necessary. The major limitation of wireless systems is the interference due to frequency reuse. This has been a long-standing impairment in cellular networks of all generations that will be further exacerbated in 5G networks, due to the expected dense cell deployment. The use of orthogonal frequency-division multiplexing (OFDM) in 4G leaded to an interference management by dynamic coordination of resource blocks. However, this allowed only modest gains in rates. A new technique of interference management was born 5 years ago, the interference alignment (IA). the IA permits to have a capacity with equals the half of the capacity of an interference-free system. This technique supposes that each transmitter (TX) knows the channels not only towards its receivers (RX)s, but the channels from all TXs to all receivers RXs. A more recent interference technique that boosts IA is massive multiple input multiple output (MIMO), where TXs use antennas at a very large scale. The idea is motivated by many simplifications, which appear in an asymptotic regime where base stations are endowed with large numbers of antennas. This thesis treats the problem of interference cancellation and capacity maximization in massive MIMO. In this context, the thesis proposes new interference management alternatives for the massive MIMO antenna regime, taking into account also the practical challenges of massive antenna arrays.

In this thesis, we analyze the performance of three promising technologies being considered for future fifth generation (5G) and beyond wireless communication systems, with primary goals to: i) render 10-100 times higher user data rate, ii) serve 10-100 times more users simultaneously, iii) 1000 times more data volume per unit area, iv) improve energy efficiency on the order of 100 times, and iv) provide higher bandwidths. Accordingly, we focus on massive multiple-input multiple-output (MIMO) systems and other future wireless technologies, namely millimeter wave (mmWave) and full-duplex (FD) systems that are being considered to fulfill the above requirements. We begin by focusing on fundamental performance limits of massive MIMO systems under practical constraints such as low complexity processing, array size and limited physical space. First, we analyze the performance of a massive MIMO base station (BS) serving spatially distributed multi-antenna users within a fixed coverage area. Stochastic geometry is used to characterize the spatially distributed users while large dimensional random matrix theory is used to achieve deterministic approximations of the sum rate of the system. We then examine the deployment of a massive MIMO BS and the resulting energy efficiency (EE) by considering a more realistic set-up of a rectangular array with increasing antenna elements within a fixed physical space. The effects of mutual coupling and correlation among the BS antennas are incorporated by deriving a practical mutual coupling matrix which considers coupling among all antenna elements within the BS. Accordingly, the optimum number of antennas that can be deployed for a particular antenna spacing when EE is considered as a design criteria is derived. Also, it is found that mutual coupling effect reduces the EE of the massive system by around 40-45% depending on the precoder/receiver used and the physical space available for antenna deployment. After establishing the constraints of antenna spacing on massive MIMO systems for the current microwave spectrum, we shift our focus to mmWave frequencies (more than 100GHz available bandwidth), where the wavelength is very small and as a result more antennas can be rigged within a constrained space. Accordingly, we integrate the massive MIMO technology with mmWave networks. In particular, we analyze the performance of a mmWave network consisting of spatially distributed BS equipped with very large uniform circular arrays (UCA) serving spatially distributed users within a fixed coverage area. The use of UCA is due to its capability of scanning through both the azimuth as well as elevation dimensions. We show that using such 3D massive MIMO techniques in mmWave systems yield significant performance gains. Further, we show the effect of blockages and path loss on mmWave networks. Since blockages are found to be quite detrimental to mmWave networks, we create alternative propagation paths with the aid of relays. In particular, we consider the deployment of relays in outdoor mmWave networks and then derive expressions for the coverage probability and transmission capacity from sources to a destination for such relay aided mmWave networks using stochastic geometric tools. Overall, relay aided mmWave transmission is seen to improve the signal to noise ratio at the destination by around 5-10dB with respect to specific coverage probabilities. Finally, due to the fact that the current half duplex (HD) mode transmission only utilizes half the spectrum at the same time in the same frequency, we consider a multiuser MIMO cellular system, where a FD BS serves multiple HD users simultaneously. However, since FD systems are plagued by severe self-interference (SI), we focus on the design of robust transceivers, which can cancel the residual SI left after antenna and analog cancellations. In particular, we address the sum mean-squared-errors (MSE) minimization problem by transforming it into an equivalent semidefinite programming (SDP) problem. We propose iterative alternating algorithms to design the transceiver matrices jointly and accordingly show the gains of FD over HD systems. We show that with proper SI cancellation, it is possible to achieve gains on sum rate of up to 70-80% over HD systems.

Massive multiple-input-multiple-output (MM) is becoming a promising candidate for wireless communications. The idea behind MM is to use a very large number of antennas to increase throughput and energy efficiency by one or more orders of magnitude. In order to make MM feasible, many challenges remain. In the uplink a fundamental question is whether to deploy single massive arrays or to build a virtual array using cooperative base stations. Also, in such large arrays the signal processing involved in receiver combining is non-trivial. Therefore, low complexity receiver designs and deployment scenarios are essential aspects of MM and the thesis mainly focuses on these two areas. In the first part, we investigate three deployment scenarios: (i) a massive co-located array at the cell center; (ii) a massive array clustered at B discrete locations; and (iii) a massive distributed array with a uniform distribution of individual antennae. We also study the effect of propagation parameters, system size, correlation and channel estimation error. We demonstrate by analysis and simulation that in the absence of any system imperfections, a massive distributed array is preferable. However, an intermediate deployment such as a massive array clustered at a few discrete locations can be more practical to implement and more robust to imperfect channel state information. We then focus on the performance of the co-located scenario with different types of antenna array, uniform square and linear arrays. With MM, it may be the case that large numbers of antennas are closely packed to fit in some available space. Hence, channel correlations become important and therefore we investigate the space requirements of different array shapes. In particular, we evaluate the system performance of uniform square and linear arrays by using ergodic capacity and capacity outage. For a range of correlation models, we demonstrate that the uniform square array can yield similar performance to a uniform linear array while providing considerable space saving. In the second part of the thesis we focus on low complexity receiver designs. Due to the high dimension of MM systems there is a considerable interest in detection schemes with a better complexity-performance trade-off. We focus on linear receivers (zero forcing (ZF) and maximum ratio combining (MRC)) used in conjuction with a Vertical Bell Laboratories Layered Space Time (V-BLAST) structure. Our first results show that the performance of MRC V-BLAST approaches that of ZF V-BLAST under a range of imperfect CSI levels, different channel powers and different types of arrays as long as the channel correlations are not too high. Subsequently, we propose novel low complexity receiver designs which maintain the same performance as ZF or ZF V-BLAST. We show that the performance loss of MRC relative to ZF can be removed in certain situations through the use of V-BLAST. The low complexity ordering scheme based on the channel norm (C-V-BLAST) results in a V-BLAST scheme with MRC that has much less complexity than a single ZF linear combiner. An analysis of the SINR at each stage of the V-BLAST approach is also given to support the findings of the proposed technique. We also show that C-V-BLAST remains similar to ZF for more complex adaptive modulation systems and in the presence of channel estimation error, C-V-BLAST can be superior. These results are analytically justified and we derive an exhaustive search algorithm for power control (PC) to bound the potential gains of PC. Using this bound, we demonstrate that C-V-BLAST performs well without the need for additional PC. The final simplification is based on the idea of ordering users based on large scale fading information rather than instantaneous channel knowledge for a V-BLAST scheme with MRC (P-V-BLAST). An explicit closed form analysis for error probability for both co-located and distributed BSs is provided along with a number of novel performance metrics which are useful in designing MM systems. It is shown that the error performance of the distributed scenario can be well approximated by a modified version of a co-located scenario. Another potential advantage of P-V-BLAST is that the ordering can be obtained as soon as the link gains are available. Hence, it is possible that mean SINR values could be used for scheduling and other link control functions. These mean values are solely functions of the link gains and hence, scheduling, power adaptation, rate adaptation, etc. can all be performed more rapidly with P-V-BLAST. Hence, the P-V-BLAST structure may have further advantages beyond a lower complexity compared to C-V-BLAST.

Half-Duplex Multiuser Multiple-Input Multiple-Output (HD MU-MIMO) systemscurrently employed in communication systems are not experiencing the selfinterference(SI) problem but they are not optimal in terms of efficiency and interms of resources used (time and frequency resources). Ignoring the effect of largescalefading, we start by explaining the uplink (UL) and downlink (DL) parts ofthe MU-MIMO system and how the sum-rate is calculated. We also introduce thethree linear receivers/precoders, Maximum-Ratio Combining (MRC)/Maximum-Ratio Transmission (MRT), Zero-Forcing (ZF), and Minimum Mean-Square Error(MMSE) and which of the three types is going to be used in the study of Full-Duplex Multiuser Multiple-input Multiple-output (FD MU-MIMO) system. Thenwe introduce FD MU-MIMO system, and how the equation used to calculate thesum-rate of the UL part changes when the SI occurs, and why SI problem is notpresent in the DL part. Next, we introduce the spectral efficiency (SE), and howto calculate it and why it is taken as a parameter to compare HD and FD systems.Also the effect of SI on FD MU-MIMO system is presented through simulationgraphs, then we move to show how to reduce SI effect by increasing the number ofantennas in the base-station (BS). Lastly, we take the effect of large scale fading inorder to reach a simple statistical model in the form cumulative distribution function(CDF) graph for different values of SI and compare those of FD MU-MIMOsystem to HD MU-MIMO. The results show that FD MU-MIMO together withmassive MIMO technology is very promising and would save time and frequencyresources which means an increase in the SE but SI must be below a certain level.

The recent developments in Internet-of-Things (IoT) and the next-generation wireless communication systems (5G and beyond) are posing unprecedented demands for massive connectivity, enhanced spectrum efficiency, and strengthened reliability. Moreover, the conventional orthogonal multiple access (OMA) techniques have approached their fundamental limits or the improvements in performance are marginal. To this end, a paradigm-shift from OMA to massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) technology is proposed. The proposed techniques are capable of serving multiple spatially-distributed user nodes/IoTs in the same frequency-time resource block by reaping out the benefits of power-domain NOMA, and favorable propagation and channel hardening brought by very large antenna arrays.First, a comprehensively literature survey has been conducted. Next, system, channel and signal models were developed by considering practical transmission impairments of the proposed massive MIMO NOMA. Then, novel NOMA relaying strategies via massive MIMO with pilot designs, per-hop and cascaded channel estimation, statistical-parameter based power allocation policy, and reliable precoding scheme are designed. Then, a complete analytical framework to derive the fundamental performance metrics is developed. A MATLAB-based simulation framework is developed to verify the proposed system designs.Then, the detrimental effects of residual interference caused by intra-cluster pilot sharing and error propagation caused by imperfect successive interference cancellation are quantified. The results acquired can provide insights for refining the proposed techniques in terms of signal model and pilot design.Trade-offs among massive connectivity and spectral efficiency will be established and refined for the proposed relay aided/cell-free massive MIMO NOMA via carefully designing per-hop and cascaded channel estimation, low-complexity statistical-parameter-based power allocation, and conjugate precoding schemes. The proposed technique is expected to significantly outperform the conventional OMA scheme in all overloaded system scenarios by virtue of the proposed aggressive spatial multiplexing and power-domain NOMA techniques. Hence, the proposed technique can simultaneously serve many users with fast data rates than that of the existing OMA techniques. The proposed NOMA techniques are expected to provide higher spectral and energy efficiencies with ultra-low end-to-end latency than those of existing OMA. Thus, the proposed relay-aided/cell-free massive MIMO NOMA can significantly contribute as a novel candidate technology for the next-generation wireless standards.

This research addresses green communication issues, including energy efficiency, peak-to-average power ratio (PAPR) reduction and power amplifier (PA) linearization. Green communication is expected to be a primary goal in next generation cellular systems because it promises to reduce operating costs. The first key issue is energy efficiency of distributed antenna systems (DASs). The power consumption of high power amplifiers (HPAs) used in wireless communication systems is determined by the transmit power and drain efficiency. For unequal power allocation of orthogonal frequency division multiplexing (OFDM), the drain efficiency of the PA is determined by the PAPR and hence by the power distribution. This research proposes a PAPR-aware energy-efficient resource allocation scheme for joint orthogonal frequency division multiple access (OFDMA)/space division multiple access (SDMA) downlink transmission from DASs. Grouping-based SDMA is applied to exploit the spatial diversity while avoiding performance degradation from correlated channels. The developed scheme considers the impact of both system data rate and effective power consumption on the PAPR during resource allocation. We also present a suboptimal joint subcarrier and power allocation algorithm to facilitate implementation of power-efficient multi-channel wireless communications. By solving Karush-Kuhn-Tucker conditions, a closed-form solution for the power allocation of each remote radio head is obtained. The second key issue is related with PAPR reduction in the massive multiple-input multiple-output (MIMO) systems. The large number of PAs in next generation massive MIMO cellular communication system requires using inexpensive PAs at the base station to keep array cost reasonable. Large-scale multiuser (MU) MIMO systems can provide extra spatial degrees-of-freedom (DoFs) for PAPR reduction. This work applies both recurrent neural network (RNN)- and semidefinite relaxation (SDR)-based schemes for different purposes to reduce PAPR. The highly parallel structure of RNN is proposed in this work to address the issues of scalability and stringent requirements on computational times in PAPR-aware precoding problem. An SDR-based framework is proposed to reduce PAPR that accommodates channel uncertainties and intercell coordination. Both of the proposed structures reduce linearity requirements and enable the use of lower cost RF components for large-scale MU-MIMO-OFDM downlink. The third key issue is digital predistortion (DPD) in the massive MIMO systems. The primary source of nonlinear distortion in wireless transmitters is the PA, which is commonly modeled using polynomials. Conventional DPD schemes use high-order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is impractical for scaling to tens or hundreds of PAs in massive MIMO systems. This work therefore proposes a scalable DPD method, achieved by exploiting massive DoFs of next generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero-forcing precoding and DPD. Experimental results show that over 70% of computational complexity is saved for the proposed solution, it is shown that a 3rd order polynomial with the new solution achieves the same performance as the conventional DPD using 11th order polynomial for a 100x10 massive MIMO configuration. / Ph. D.

Two of the most prominent techniques to meet the next generation wireless communication system's demands are cognitive radio and massive MIMO systems. Cognitive radio systems improve radio spectrum utilization either by spectrum sharing or by opportunistically utilizing the spectrum of the licensed users. Employing multiple antennas at the transmitter and/or the receiver of the radio can further improve the overall performance of the wireless systems. Massive MIMO systems, on the other hand, improve the spectral and energy efficiencies of currently deployed systems by reaping all the benefits of the multi-antenna systems at a very large scale. The price paid for employing a large number of antennas either at the transmitter or receiver is the high hardware cost. Judicious transmit or receive antenna selection can reduce this cost, while retaining most of the benefits offered by multiple antennas. In my doctoral research, we have presented both upper and lower bounds on the capacity of a general selection diversity system. These novel bounds are simple to compute and can be used in a variety of different fading environments. We have also proposed and analyzed the performance of different antenna selection schemes for both an underlay cognitive radio and a massive MIMO system. Specifically, we have considered both receive and transmit antenna selection in an underlay cognitive radio based on the maximization of secondary link signal-to-interference plus noise ratio. Exact and asymptotic performance analyses of the secondary system with such selections are carried out, and numerical examples are presented to verify the correctness of the analytical results. Several sub-optimal antenna subset selection schemes for both a single-cell and a multi-cell multi-user massive MIMO system are also proposed. Numerical results on the sum rate of the system in different scenarios are presented to verify the superior performance of the proposed schemes over the existing sub-optimal antenna subset selection schemes. Lastly, we have also presented three novel hybrid analog/digital precoding schemes to reduce the hardware and software complexities of a sub-connected massive MIMO system. / Graduate / 0544

The past decade has seen an enormous increase in the number of connected wireless devices, and currently there are billions of devices that are connected and managed by wireless networks. At the same time, the applications that are running on these devices have also developed significantly and became more data rate insatiable. As the number of wireless devices and the demand for a high data rate will always increase, in addition to the growing concern about the energy consumption of wireless communication systems, the future wireless communication systems will have to meet three main requirements. These three requirements are: i) being able to achieve high throughput; ii) serving a large number of users simultaneously; and iii) being energy efficient (less energy consumption). Massive multiple-input multiple-output (MIMO) technology can satisfy the aforementioned requirements; and thus, it is a promising candidate technology for the next generations of wireless communication systems. Massive MIMO technology simply refers to the idea of utilizing a large number of antennas at the base station (BS) to serve a large number of users simultaneously using the same time-frequency resources. The hypothesis behind using a massive number of antennas at the BS is that as the number of antennas increases, the channels become favourable. In other words, the channel vectors between the users and their serving BS become (nearly) pairwisely orthogonal as the number of BS antennas increases. This in turn enables the use of linear processing at the BS to achieve near optimal performance. Moreover, a huge throughput and energy efficiency can be attained due to users multiplexing and array gain. In this thesis, we investigate the performance of massive MIMO systems under different scenarios. Firstly, we investigate the performance of a single-cell multi-user massive MIMO system, in which the channel vectors for the different users are assumed to be correlated. In this aspect, we propose two algorithms for users grouping that aim to improve the system performance. Afterwards, the problem of pilot contamination in multi-cell massive MIMO systems is discussed. Based on this discussion, we propose a pilot allocation algorithm that maximizes the minimum achievable rate in a target cell. Following that, we consider two different scenarios for pilot sequences allocation in multi-cell massive MIMO systems. Lower bounds on the achievable rates are derived for two linear detectors, and the performance under different system settings is analysed and discussed for both scenarios. Finally, two algorithms for pilot sequences allocation are proposed. The first algorithm takes advantage of the multiplicity of pilot sequences over the number of users to improve the achievable rate of edge cell users. While the second algorithm aims to mitigate the negative impact of pilot contamination by utilizing more system resources for the channel estimation process to reduce the inter-cell interference.

The architecture of cellular networks has been undergoing an extraordinarily fast evolution in the last years to keep up with the ever increasing user demands for wireless data and services. Motivated by a search for a breakthrough in network capacity, the paradigm of heterogeneous networks (HetNets) has become prominent in modern cellular systems, where carefully deployed macrocells coexist with layers of irregularly deployed cells of reduced coverage sizes. Users can thus be offloaded from the macrocell and the capacity of the network increases. However, universal frequency reuse is usually employed to maximize capacity gains, thereby introducing the fundamental problem of inter-cell interference (ICI) in the network caused by the sharing of the spectrum among the different tiers of the HetNet. The objective of this PhD thesis is to provide analysis and mitigation techniques for the fundamental problem of interference in heterogeneous cellular networks. First, the interference of a two-tier network is modeled and analyzed by making use of spatial statistics tools that allow the reconstruction of complete coverage maps. A correlation analysis is then performed by deriving a spatial coverage cross-tier correlation function. Second, a novel architecture design is proposed to minimize interference in HetNets whose base stations may be equipped with very large antenna arrays, another key technology of future wireless systems. Then, we present interference mitigation techniques for different types of small cells, namely picocells and femtocells. In the third contribution of this thesis, we analyze the case of clustered deployments by proposing and comparing techniques suitable for this scenario. Fourth, we tackle the case of femtocell deployments by analyzing the degrading effect of interference and proposing new mitigation methods. Fifth, we introduce femtorelays, a novel small cell access technology that combats interference in femtocell networks and provides higher backhaul capacity.

Next generation wireless systems will support higher data rates, improved spectral efficiency, and less latency. Massive multiple-input multiple-output (MIMO) is proposed to satisfy these demands. In massive MIMO, many benefits come from employing hundreds of antennas at the base station (BS) and serving dozens of user terminals (UTs) per cell. As the number of antennas increases at the BS, the channel becomes sparse. By exploiting sparse channel in massive MIMO, compressive sensing (CS) methods can be implemented to estimate the channel. In CS methods, the length of pilot sequences can be shortened compared to pilot-based methods. In this paper, a novel channel estimation algorithm based on a CS method called elastic net is proposed. Channel estimation accuracy of pilot-based, lasso, and elastic-net based methods in massive MIMO are compared. It is shown that the elastic-net based method gives the best performance in terms of error for the less pilot symbols and SNR values.