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.

This thesis focuses a noncoherent two-user uplink system with each user having a single antenna and a base station equipped with a large number of antennas. It is assumed that small scale channel fading is Rayleigh fading and varies in every one time slot. For such massive MIMO uplink system, we consider an optimal finite-alphabet non-orthogonal multiple access (NOMA) design with each user utilizing nonnegative binary modulation. A fast noncoherent maximum likelihood (ML) detection algorithm for the sum constellation of the two users and a corresponding closed form symbol error probability (SEP) formula are derived. In addition, the lower and upper bounds on SEP are established to quantitatively characterize how quickly SEP decays when the number of base station antennas goes to infinity. Two important concepts: full receiver diversity and geometrical coding gain, are introduced. Particularly for two users and three users systems, with each user transmitting nonnegative binary constellation, we obtain an optimal closed form sum constellation that maximizes both the receiver diversity gain and geometrical coding gain. Computer simulations validateour theoretical analysis and demonstrate that our proposed optimal constellation attains significant performance gains over the currently available constellation design for the same massive MIMO upink system.\ Our future work is to develop an algorithm for devising an optimal AUDCG for the considered system in a more general case. / Thesis / Master of Applied Science (MASc)

The 5th generation mobile telecommunication system (5G) is currently being specified and developed, with large expectations on throughput and efficiency. While 4G and more specifically LTE might constitute a basis of the design of the network, there are some parts that should be improved. One thing to improve is the static signalling that occurs very frequently in a 4G network, of which system information such as synchronization signals, detection of network frequencies, operators, configurations etc. is a part. It has been shown that the static signalling requires both much energy and time-frequency resources. Since the system information is not intended for a single user it is always broadcast so that any user, and any amount of users can read it when needed. 5G will use a technique called massive MIMO, where the base station is equipped with a large number of antennas which can be used to direct signals in space, called beamforming. This thesis presents a new method for distribution of system information that can utilize the beamforming capabilities of massive MIMO. A simple model together with simulated user channel statistics from urban 4G scenarios are used to show that the new method outperforms the classical method of only broadcasting the information, with respect to time-frequency resources. Especially if there are high requirements on the latency of the system information, the new method results in a large gain.

Mobile data traffic is growing exponentially due to the popularization of smart phones, tablets and other data traffic appliances. One way of handling the increased data traffic is to deploy large antenna arrays at the base station, also known as Massive MIMO. In Massive MIMO, the base station having excessive number of transmit antennas, can achieve increased data rate by spatial-multiplexing terminals into the same time-frequency resource. This thesis investigates Massive MIMO in LTE in a single-cell deployment with up to 100 base station antennas. The benefits of more antennas are investigated with single-antenna terminals in a typical urban environment. The terminal transmitted sounding reference signals (SRS) are used at the base station to calculate channel state information (CSI) in order to generate an MRT precoder. With perfect CSI, the results showed that the expected terminal SINR depends on the antenna-terminal ratio. It was also showed that with spatial-multiplexed terminals and 100 base station antennas, the maximum cell throughput increased 13 times compared with no spatial-multiplexed terminals. Channel ageing causes inaccuracy in the CSI, the thesis showed that the variation in terminal SINR increased rapidly with less frequent SRS transmissions. When having moving terminals at 3 km/h, the difference between the 10th and 90th SINR percentile is 1 dB with an SRS transmission periodicity of 20 ms, and 17 dB with an SRS transmission periodicity of 80 ms. With 100 base station antennas and moving terminals at 3 km/h with an SRS periodicity of 20 ms, the maximum cell throughput decreased with 13% compared to when the base station has perfect CSI. The result showed that the maximum cell throughput scaled linearly with the number of base station antennas. It also showed that having the number of spatial-multiplexed terminals equal to the number of antennas is a reasonable assumption when maximizing the cell throughput.

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.

Dans le domaine des télécommunications sans fil, les domaines applicatifs sont de plus en plus larges, s’étendant par exemple du grand public, à la voiture connectée, à l’internet des objets (IoT Internet of Things) et à l’industrie 4.0. Dans ce dernier cas, l’objectif est d’aboutir à une flexibilité et à une versatilité accrues des chaînes de production et à une maintenance prédictive des machines, pour ne citer que quelques exemples. Cependant, les réseaux sans fil actuels ne sont pas encore en mesure de répondre aux nombreuses lacunes de la quatrième génération des réseaux mobiles (4G) et aux exigences de la 5G quant à une connectivité massive, une ultra fiabilité et des temps de latence extrêmement faibles. L’optimisation des ressources spectrales est également un point très important. La 5G était initialement considérée comme une évolution, rendue possible grâce aux améliorations apportées à la LTE (Long Term Evolution), mais elle ne tardera pas à devenir une révolution et une avancée majeure par rapport aux générations précédentes.Dans ce cadre, la technologie des réseaux massifs ou Massive MIMO (Multiple-Input Multiple-Output) s’est imposée comme l’une des technologies de couche physique les plus prometteuses. L'idée principale est d'équiper les stations de base de grands réseaux d’antennes (100 ou plus) pour communiquer simultanément avec de nombreux terminaux ou équipements d’utilisateurs. Grâce à un prétraitement intelligent au niveau des signaux d’émission, les systèmes Massive MIMO promettent d’apporter une grande amélioration des performances, tout en assurant une excellente efficacité spectrale et énergétique. Cependant certains défis doivent encore être relevés avant le déploiement complet des communications basées sur le massive MIMO. Par exemple, l’élaboration de modèles de canaux représentatifs de l’environnement réel, l'impact de la diversité de polarisation, les stratégies de sélection optimale d’antennes et l'acquisition d'informations d'état du canal, sont des sujets importants à explorer. En outre, une bonne compréhension des canaux de propagation en milieu industriel est nécessaire pour optimiser les liens de communication de l'industrie intelligente du futur.Dans cette thèse, nous essayons de répondre à certaines de ces questions en nous concentrant sur trois axes principaux :1) La caractérisation polarimétrique des canaux massive MIMO en environnement industriel. Pour cela, on étudie des scénarios correspondant à des canaux ayant ou non une visibilité directe entre émetteur et récepteur (Line of Sight – LOS) ou Non LOS, et en présence de divers types d’obstacles. Les métriques associées sont soit celles utilisées en propagation telles que le facteur de Rice et la corrélation spatiale, soit orientées système comme la capacité totale du canal incluant des stratégies de précodage linéaire. De plus, les schémas de diversité de polarisation proposés montrent des résultats très prometteurs.2) En massive MIMO, un objectif important est de réduire le nombre de chaînes de fréquences radio et donc la complexité du système, en sélectionnant un ensemble d'antennes distribuées. Cette stratégie de sélection utilisant la corrélation spatiale du récepteur et une métrique de propagation comme facteur de mérite, permet d'obtenir une capacité totale quasi-optimale.3) Une technique efficace de réduction des ressources lors de l’acquisition d’informations du canal de propagation dans les systèmes FDD (frequency-division-duplex) est enfin proposée. Elle repose sur la corrélation spatiale au niveau de l'émetteur et consiste à résoudre un ensemble d'équations auto-régressives simples. Les résultats montrent que cette technique permet d’atteindre des performances qui ne sont pas trop éloignées de celles des systèmes TDD (time-division-duplex) initialement proposés pour le massive MIMO. / Continuous efforts have been made to boost wireless systems performance, however, current wireless networks are not yet able to fulfill the many gaps from 4G and requirements for 5G. Thus, significant technological breakthroughs are still required to strengthen wireless networks. For instance, in order to provide higher data rates and accommodate many types of equipment, more spectrum resources are needed and the currently used spectrum requires to be efficiently utilized. 5G, or the fifth generation of mobile networks, is initially being labeled as an evolution, made available through improvements in LTE, but it will not be long before it becomes a revolution and a major step-up from previous generations. Massive MIMO has emerged as one of the most promising physical-layer technologies for future 5G wireless systems. The main idea is to equip base stations with large arrays (100 antennas or more) to simultaneously communicate with many terminals or user equipments. Using smart pre-processing at the array, massive MIMO promises to deliver superior system improvement with improved spectral efficiency, achieved by spatial multiplexing and better energy efficiency, exploiting array gain and reducing the radiated power. Massive MIMO can fill the gap for many requirements in 5G use-cases notably industrial IOT (internet of things) in terms of data rates, spectral and energy efficiency, reliable communication, optimal beamforming, linear processing schemes and so on. However, the hardware and software complexity arising from the sheer number of radio frequency chains is a bottleneck and some challenges are still to be tackled before the full operational deployment of massive MIMO. For instance, reliable channel models, impact of polarization diversity, optimal antenna selection strategies, mutual coupling and channel state information acquisition amongst other aspects, are all important questions worth exploring. Also, a good understanding of industrial channels is needed to bring the smart industry of the future ever closer.In this thesis, we try to address some of these questions based on radio channel data from a measurement campaign in an industrial scenario using a massive MIMO setup. The thesis' main objectives are threefold: 1) Characterization of massive MIMO channels in Industry 4.0 (industrial IoT) with a focus on spatial correlation, classification and impact of cross-polarization at transmission side. The setup consists in multiple distributed user-equipments in many propagation conditions. This study is based on propagation-based metrics such as Ricean factor, correlation, etc. and system-oriented metrics such as sum-rate capacity with linear precoding and power allocation strategies. Moreover, polarization diversity schemes are proposed and were shown to achieve very promising results with simple allocation strategies. This work provides comprehensive insights on radio channels in Industry 4.0 capable of filling the gap in channel models and efficient strategies to optimize massive MIMO setups. 2) Proposition of antenna selection strategies using the receiver spatial correlation, a propagation metric, as a figure of merit. The goal is to reduce the number of radio frequency chain and thus the system complexity by selecting a set of distributed antennas. The proposed strategy achieves near-optimal sum-rate capacity with less radio frequency chains. This is critical for massive MIMO systems if complexity and cost are to be reduced. 3) Proposition of an efficient strategy for overhead reduction in channel state information acquisition of FDD (frequency-division-duplex) systems. The strategy relies on spatial correlation at the transmitter and consists in solving a set of simple autoregressive equations (Yule-Walker equations). The results show that the proposed strategy achieves a large fraction of the performance of TDD (time-division-duplex) systems initially proposed for massive MIMO.

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. / The global climate change has emerged as a critical issue over the last decades. The increasing popularity of wireless communication networks, has resulted in information and communication technology becoming a non-negligible contributor to the overall carbon footprint. The increasing number of base stations and remote radio heads leads to higher operating expenditure mainly because of the higher energy consumption. This growth can be attributed not only to the increase in the number of smart devices in emerging economies, but also to the growth of shared multimedia data and online games. The wireless industry needs significant improvements in the energy efficiency of base stations and other network infrastructure to compensate for the increased energy demands from the network growth. Therefore, designing energy-efficient communication systems has become a critical issue for 5G, which promises massive deployment of smart devices served new infrastructure elements. In this dissertation, we primarily investigate the theoretical foundations and practical algorithms for the next generation wireless technologies, and discuss the impact of ongoing trends in cellular communications, such as shrinking cell sizes and multi-antenna system deployments, on energy-efficient 5G networks. The theoretical development and wireless algorithms are valuable for the deployment of next generation wireless network systems