Resource allocation and optimization techniques in wireless relay networks

Relay techniques have the potential to enhance capacity and coverage of a wireless network. Due to rapidly increasing number of smart phone subscribers and high demand for data intensive multimedia applications, the useful radio spectrum is becoming a scarce resource. For this reason, two way relay network and cognitive radio technologies are required for better utilization of radio spectrum. Compared to the conventional one way relay network, both the uplink and the downlink can be served simultaneously using a two way relay network. Hence the effective bandwidth efficiency is considered to be one time slot per transmission. Cognitive networks are wireless networks that consist of different types of users, a primary user (PU, the primary license holder of a spectrum band) and secondary users (SU, cognitive radios that opportunistically access the PU spectrum). The secondary users can access the spectrum of the licensed user provided they do not harmfully affect to the primary user. In this thesis, various resource allocation and optimization techniques have been investigated for wireless relay and cognitive radio networks

Joint precoding and antenna selection in massive mimo systems

This thesis presents an overview of massive multiple-input multiple-output (MIMO) systems and proposes new algorithms to jointly precode and select the antennas. Massive MIMO is a new technology, which is candidate for comprising the fifth-generation (5G) of mobile cellular systems. This technology employs a huge amount of antennas at the base station and can reach high data rates under favorable, or asymptotically favorable, propagation conditions, while using simple linear processing. However, massive MIMO systems have some drawbacks, such as the high cost related to the base stations. A way to deal with this issue is to employ antenna selection algorithms at the base stations. These algorithms reduce the number of active antennas, decreasing the deployment and maintenance costs related to the base stations. Moreover, this thesis also describes a class of nonlinear precoders that are rarely addressed in the literature; these techniques are able to generate precoded sparse signals in order to achieve joint precoding and antenna selection. This thesis proposes two precoders belonging to this class, where the number of selected antennas is controlled by a design parameter. Simulation results show that the proposed precoders reach a lower bit-error rate than the classical antenna selection algorithms. Furthermore, simulation results show that the proposed precoders present a linear relation between the aforementioned design parameter that controls the signals’ sparsity and the number of selected antennas. Such relation is invariant to the number of base station’s antennas and the number of terminals served by this base station.Esta dissertação apresenta uma visão geral sobre MIMO (do termo em inglês, multiple-input multiple-output) massivo e propõe novos algoritmos que permitem a pré-codificacão de sinais e a seleção de antenas de forma simultânea. MIMO massivo é uma nova tecnologia candidata para compor a quinta geração (5G) dos sistemas celulares. Essa tecnologia utiliza uma quantidade muito grande de antenas na estação-base e, sob condições de propagação favorável ou assintoticamente favorável, pode alcançar taxas de transmissão elevadas, ainda que utilizando um simples processamento linear. Entretanto, os sistemas MIMO massivo apresentam algumas desvantagens, como por exemplo, o alto custo de implementação das estações-bases. Uma maneira de lidar com esse problema é utilizar algoritmos de seleção de antenas na estação-base. Com esses algoritmos é possível reduzir o número de antenas ativas e consequentemente reduzir o custo nas estações-bases. Essa dissertação também apresenta uma classe pouco estudada de pré-codificadores não-lineares que buscam sinais pré-codificados esparsos para realizar a seleção de antenas conjuntamente com a pré-codificação. Além disso, este trabalho propõem dois novos pré-codificadores pertencentes a essa classe, para os quais o número de antenas selecionadas é controlado por um parâmetro de projeto. Resultados de simulações mostram que os pré-codificadores propostos conseguem uma BER (do termo em inglês, bit-error rate) menor que os algoritmos clássicos usados para selecionar antenas. Além disso, resultados de simulações mostram que os pré-codificadores propostos apresentam uma relação linear com o parâmetro de projeto que controla a quantidade de antenas selecionadas; tal relação independe do número de antenas na estação-base e do número de terminais servidos por essa estação

Energy-Efficient Pilot-Data Power Control in MU-MIMO Communication Systems

Multiple-input multiple-output (MIMO) antenna system is considered as a core technology for wireless communication. To reap the benefits of MIMO at a greater scale, massive MIMO with very large antenna arrays deployed at base station (BS) has recently become the forefront in wireless communication research. Till present, the design and analysis of large-scale MIMO systems is a fairly new subject. On the other hand, excessive power usage in MIMO networks is a crucial issue for mobile operators and the explosive growth of wireless services contributes largely to the worldwide carbon footprint. As such, significant efforts have been devoted to improve the spectral efficiency (SE) as well as energy efficiency (EE) of MIMO communication systems over the past decade, resulting in many energy efficient techniques such as power allocation. This thesis investigates novel energy-efficient pilot-data power control strategies which can be used in both conventional MIMO and massive MIMO communication systems. The new pilot-data power control algorithms are developed based ontwo optimization frameworks: one aims to minimize the total transmit power while satisfying per-user signal-interference-plus-noise ratio (SINR) and power constraints; the other aims to maximize the total EE, which is defined as the ratio of the total SE to the transmit power, under individual user power constraints. The proposed novel pilot-data power allocation schemes also take into account the maximum-ratio combining (MRC) and zero-forcing (ZF) detectors in the uplink together with maximum-ratio transmission (MRT) and ZF precoder in the downlink. Considering that a direct use of such SINR expressions in the power control schemeswould lead to a very difficult optimization problem which is not mathematically tractable, we first investigatethe statistical SINR lower bounds for multi-cell multi-user MIMO (MU-MIMO)communication systemsunder minimum mean square error (MMSE) channel estimation. These lower bounds of the per-user average SINRs are used to replace the true SINRs to simplify the power allocation optimization problems. Such relaxation of the original average SINR yields a simplified problem and leads to a suboptimal solution. Then, based on the derived average SINR lower bounds, two novel energy efficient pilot-data power control problems are formulatedwithin the first optimization framework,aiming to minimize the total transmit power budget subject to the per-user SINR requirement and power consumption constraint in multi-cell MU-MIMO systems. For the EE-optimal power allocation problems with MRT precoder and MRC detector, it is revealed that such minimization problems can be converted to a standard geometric programming (GP) procedure which can be further converted to a convex optimization problem. For the pilot-data power control scheme with ZF precoder and ZF detector, geometric inequality is used to approximate the original non-convex optimization to GP problem. The very large number of BS station situation is also discussed by assuming infinite antennas at BS. Numerical results validate the tightness of the derived SINR lower bounds and the advantages of the proposed energy efficient power allocation schemes. Next, two pilot and data power control schemes are developed based on the second power allocation optimization framework to jointly maximize the total EE for both uplink and downlink transmissions in multi-cell MU-MIMO systems under per-user and BS power constraints. The original power control problems are simplified to equivalent convex problems based on the derived SINR lower bounds along with the Dinkelbach's method and the FrankWolfe (FW) iteration. By assuming infinite antennas at BS, the pilot-data power control in massive MIMO case is also discussed. The performance of the proposed pilot-data power allocation schemes based on the two frameworks, namely total transmit power minimization and total EE maximization, are evaluated and compared with the SE maximization scheme. Furthermore, we investigate the pilot-data power allocation for EE communications in single-cell MU-MIMO systems with circuit power consumption in consideration. The pilot and data power allocation schemes are proposed to minimize the total weighted uplink and downlink transmit power as well as processing circuit power consumption while meeting the per-user SINR and BS power consumption constraints. In our proposed schemes, both fixed and flexible numbers of BS antennas are investigated. For the fixed number of BS antennas case, the non-convex optimization problems are converted to a general GP problem to facilitate the solution. An iterative algorithm is proposed to solve the EE-optimal power control problems in the flexible number of BS antennas casebased on the partial convexity of both the cost function and the constraints. It is shown that the convergence of the proposed iterative algorithm is guaranteed due to the fact that each iteration follows convex optimization

MIMO designs for filter bank multicarrier and multiantenna systems based on OQAM

From the perspective of increasingly data rate requirements in mobile communications, it is deemed necessary to do further research so that the future goals can be reached. To that end, the radio-based communications are resorting to multicarrier modulations and spatial diversity. Until today, the orthogonal frequency division multiplexing (OFDM) modulation is regarded as the dominant technology. On one hand, the OFDM modulation is able to accommodate multiantenna configurations in a very straightforward manner. On the other hand, the poor stopband attenuation exhibited by the OFDM modulation, highlights that a definitely tight synchronization is required. In addition, the cyclic prefix (CP) has to be sufficiently long to avoid inter-block interference, which may substantially reduce the spectral efficiency. In order to overcome the OFDM drawbacks, the filter bank multicarrier modulation based on OQAM (FBMC/OQAM) is introduced. This modulation does not need any CP and benefits from pulse shaping techniques. This aspect becomes crucial in cognitive radio networks and communication systems where nodes are unlikely to be synchronized. In principle, the poor frequency confinement exhibited by OFDM should tip the balance towards FBMC/OQAM. However, the perfect reconstruction property of FBMC/OQAM systems does not hold in presence of multipath fading. This means that the FBMC/OQAM modulation is affected by inter-symbol and inter-carrier interference, unless the channel is equalized to some extent. This observation highlights that the FBMC/OQAM extension to MIMO architectures becomes a big challenge due to the need to cope with both modulation- and multiantenna-induced interference. The goal of this thesis is to study how the FBMC/OQAM modulation scheme can benefit from the degrees of freedom provided by the spatial dimension. In this regard, the first attempt to put the research on track is based on designing signal processing techniques at reception. In this case the emphasis is on single-input-multiple-output (SIMO) architectures. Next, the possibility of pre-equalizing the channel at transmission is investigated. It is considered that multiple antennas are placed at the transmit side giving rise to a multiple-input-single-output (MISO) configuration. In this scenario, the research is not only focused on counteracting the channel but also on distributing the power among subcarriers. Finally, the joint transmitter and receiver design in multiple-input-multiple-output (MIMO) communication systems is covered. From the theory developed in this thesis, it is possible to conclude that the techniques originally devised in the OFDM context can be easily adapted to FBMC/OQAM systems if the channel frequency response is flat within the subchannels. However, metrics such as the peak to average power ratio or the sensitivity to the carrier frequency offset constraint the number of subcarriers, so that the frequency selectivity may be appreciable at the subcarrier level. Then, the flat fading assumption is not satisfied and the specificities of FBMC/OQAM systems have to be considered. In this situation, the proposed techniques allow FBMC/OQAM to remain competitive with OFDM. In addition, for some multiantenna configurations and propagation conditions FBMC/OQAM turns out to be the best choice. The simulation-based results together with the theoretical analysis conducted in this thesis contribute to make progress towards the application of FBMC/OQAM to MIMO channels. The signal processing techniques that are described in this dissertation allow designers to exploit the potentials of FBMC/OQAM and MIMO to improve the link reliability as well as the spectral efficiency

Deep Learning Designs for Physical Layer Communications

Wireless communication systems and their underlying technologies have undergone unprecedented advances over the last two decades to assuage the ever-increasing demands for various applications and emerging technologies. However, the traditional signal processing schemes and algorithms for wireless communications cannot handle the upsurging complexity associated with fifth-generation (5G) and beyond communication systems due to network expansion, new emerging technologies, high data rate, and the ever-increasing demands for low latency. This thesis extends the traditional downlink transmission schemes to deep learning-based precoding and detection techniques that are hardware-efficient and of lower complexity than the current state-of-the-art. The thesis focuses on: precoding/beamforming in massive multiple-inputs-multiple-outputs (MIMO), signal detection and lightweight neural network (NN) architectures for precoder and decoder designs. We introduce a learning-based precoder design via constructive interference (CI) that performs the precoding on a symbol-by-symbol basis. Instead of conventionally training a NN without considering the specifics of the optimisation objective, we unfold a power minimisation symbol level precoding (SLP) formulation based on the interior-point-method (IPM) proximal ‘log’ barrier function. Furthermore, we propose a concept of NN compression, where the weights are quantised to lower numerical precision formats based on binary and ternary quantisations. We further introduce a stochastic quantisation technique, where parts of the NN weight matrix are quantised while the remaining is not. Finally, we propose a systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. Furthermore, we investigate performance complexity tradeoffs via regularisation constraints on the layer weights such that, at inference, parts of network layers can be removed with minimal impact on the detection accuracy. Simulation results show that our proposed learning-based techniques offer better complexity-vs-BER (bit-error-rate) and complexity-vs-transmit power performances compared to the state-of-the-art MIMO detection and precoding techniques

Multi-Antenna Techniques for Next Generation Cellular Communications

Future cellular communications are expected to offer substantial improvements for the pre- existing mobile services with higher data rates and lower latency as well as pioneer new types of applications that must comply with strict demands from a wider range of user types. All of these tasks require utmost efficiency in the use of spectral resources. Deploying multiple antennas introduces an additional signal dimension to wireless data transmissions, which provides a significant alternative solution against the plateauing capacity issue of the limited available spectrum. Multi-antenna techniques and the associated key enabling technologies possess unquestionable potential to play a key role in the evolution of next generation cellular systems. Spectral efficiency can be improved on downlink by concurrently serving multiple users with high-rate data connections on shared resources. In this thesis optimized multi-user multi-input multi-output (MIMO) transmissions are investigated on downlink from both filter design and resource allocation/assignment points of view. Regarding filter design, a joint baseband processing method is proposed specifically for high signal-to-noise ratio (SNR) conditions, where the necessary signaling overhead can be compensated for. Regarding resource scheduling, greedy- and genetic-based algorithms are proposed that demand lower complexity with large number of resource blocks relative to prior implementations. Channel estimation techniques are investigated for massive MIMO technology. In case of channel reciprocity, this thesis proposes an overhead reduction scheme for the signaling of user channel state information (CSI) feedback during a relative antenna calibration. In addition, a multi-cell coordination method is proposed for subspace-based blind estimators on uplink, which can be implicitly translated to downlink CSI in the presence of ideal reciprocity. Regarding non-reciprocal channels, a novel estimation technique is proposed based on reconstructing full downlink CSI from a select number of dominant propagation paths. The proposed method offers drastic compressions in user feedback reports and requires much simpler downlink training processes. Full-duplex technology can provide up to twice the spectral efficiency of conventional resource divisions. This thesis considers a full-duplex two-hop link with a MIMO relay and investigates mitigation techniques against the inherent loop-interference. Spatial-domain suppression schemes are developed for the optimization of full-duplex MIMO relaying in a coverage extension scenario on downlink. The proposed methods are demonstrated to generate data rates that closely approximate their global bounds

Advanced Trends in Wireless Communications

Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics

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Digital signal processing waveform aggregation and its experimental demonstration for next generation mobile fronthaul

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