To meet the urgent demand of high-speed data rate and to support large number of users, the massive multiple-input multiple-output (MIMO) technology is becoming one of the most promising candidates for the next generation wireless communications, namely the 5G. To realize the full potential of massive MIMO, it is necessary to have the channel state information (CSI) (partially) available at the transmitter. Hence, an efficient channel estimation is one of the key enablers and also critical challenges for 5G communications. Dealing with such problems, this dissertation investigates the design of efficient pilot-data transmission pattern and channel estimation in massive MIMO for both multipair relaying and peer-to-peer systems. Firstly, this dissertation proposes a pilot-data transmission overlay scheme for multipair MIMO relaying systems employing either half- or full-duplex (HD or FD) communications at the relay station (RS). In the proposed scheme, pilots are transmitted in partial overlap with data to decrease the channel estimation overhead. The RS can detect the source data by exploiting the asymptotic orthogonality of massive MIMO channels. Due to the transmission overlay, the effective data period is extended, hence improving system throughput. Both theoretical and simulation results verify that the proposed pilot-data overlay scheme outperforms the conventional separate pilot-data design in the limited coherence interval scenario. Moreover, a power allocation problem is formulated to properly adjust the transmission power of source data transmission and relay data forwarding which further improves the system performance. Additionally, this dissertation proposes and analyzes an efficient HD decode-and-forward (DF) scheme, named sum decode-and-forward (SDF), with the physical layer network coding (PNC) in the multipair massive MIMO two-way relaying system. As comparison, a joint decode-and-forward (JDF) scheme applied to the multipair massive MIMO relaying is also proposed and investigated. In the SDF scheme, a half number of pilots are saved compared to the JDF scheme which in turn increases the spectral efficiency of the system. Both the theoretical analyses and numerical results verifies such superiority of the SDF scheme. Further, the power efficiency of the proposed schemes is also investigated. Simulation results show that the signal transmission power can be rapidly reduced if the massive antenna arrays are equipped on the RS and the required data transmission power can further decrease if the training power is fixed. Finally, this dissertation investigates the general channel estimation problem in the massive MIMO system which employs the hybrid analog/digital precoding structure with limited radio-frequency (RF) chains. By properly designing RF combiners and performing multiple trainings, the performance of the proposed channel estimation can approach that of full-chain estimations depending on the degree of channel spatial correlation and the number of RF chains which is verified by simulation results in terms of both mean square error (MSE) and spectral efficiency. Moreover, a covariance matching method is proposed to obtain channel correlation in practice and the simulation verifies its effectiveness by evaluating the spectral efficiency performance in parametric channel models. / Graduate / 0537 / 0544 / leyuanpan@gmail.com

abstract: The recent increase in users of cellular networks necessitates the use of new technologies to meet this demand. Massive multiple input multiple output (MIMO) communication systems have great potential for increasing the network capacity of the emerging 5G+ cellular networks. However, leveraging the multiplexing and beamforming gains from these large-scale MIMO systems requires the channel knowlege between each antenna and each user. Obtaining channel information on such a massive scale is not feasible with the current technology available due to the complexity of such large systems. Recent research shows that deep learning methods can lead to interesting gains for massive MIMO systems by mapping the channel information from the uplink frequency band to the channel information for the downlink frequency band as well as between antennas at nearby locations. This thesis presents the research to develop a deep learning based channel mapping proof-of-concept prototype. Due to deep neural networks' need of large training sets for accurate performance, this thesis outlines the design and implementation of an autonomous channel measurement system to analyze the performance of the proposed deep learning based channel mapping concept. This system obtains channel magnitude measurements from eight antennas autonomously using a mobile robot carrying a transmitter which receives wireless commands from the central computer connected to the static receiver system. The developed autonomous channel measurement system is capable of obtaining accurate and repeatable channel magnitude measurements. It is shown that the proposed deep learning based channel mapping system accurately predicts channel information containing few multi-path effects. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

Multiple-input-multiple-output (MIMO) systems of current LTE releases are capable of adaptation in the azimuth only. More recently, the trend is to enhance the system performance by exploiting the channel's degrees of freedom in the elevation through the dynamic adaptation of the vertical antenna beam pattern. This necessitates the derivation and characterization of three-dimensional (3D) channels. Over the years, channel models have evolved to address the challenges of wireless communication technologies. In parallel to theoretical studies on channel modeling, many standardized channel models like COST-based models, 3GPP SCM, WINNER, ITU have emerged that act as references for industries and telecommunication companies to assess system-level and link-level performances of advanced signal processing techniques over real-like channels. Given the existing channels are only two dimensional (2D) in nature; a large effort in channel modeling is needed to study the impact of the channel component in the elevation direction. The first part of this work sheds light on the current 3GPP activity around 3D channel modeling and beamforming, an aspect that to our knowledge has not been extensively covered by a research publication. The standardized MIMO channel model is presented, that incorporates both the propagation effects of the environment and the radio effects of the antennas. In order to facilitate future studies on the use of 3D beamforming, the main features of the proposed 3D channel model are discussed. A brief overview of the future 3GPP 3D channel model being outlined for the next generation of wireless networks is also provided. In the subsequent part of this work, we present an information-theoretic channel model for MIMO systems that supports the elevation dimension. The model is based on the principle of maximum entropy, which enables us to determine the distribution of the channel matrix consistent with the prior information on the angles of departure and angles of arrival of the propagation paths. Based on this model, an analytical expression for the cumulative density function (CDF) of the mutual information (MI) for systems with a single receive and finite number of transmit antennas in the general signal-to-interference-plus-noise-ratio (SINR) regime is provided. The result is extended to systems with multiple receive antennas in the low SINR regime. A Gaussian approximation to the asymptotic behavior of the MI distribution is derived for the large number of transmit antennas and paths regime. Simulation results study the performance gains realizable through meticulous selection of the transmit antenna down tilt angles, confirming the potential of elevation beamforming to enhance system performance. The results validate the proposed analytical expressions and elucidate the dependence of system performance on azimuth and elevation angular spreads and antenna patterns. We believe that the derived expressions will help evaluate the performance of 3D 5G massive MIMO systems in the future.

Massive MIMO systems are shown to be a promising technology for next generations of wireless communication networks. The realization of the attractive merits promised by massive MIMO systems requires advanced linear precoding and receiving techniques in order to mitigate the interference in downlink and uplink transmissions. This work considers the precoder and receiver design in massive MIMO systems. We first consider the design of the linear precoder and receiver that maximize the minimum signal-to-interference-plus-noise ratio (SINR) subject to a given power constraint. The analysis is carried out under the asymptotic regime in which the number of the BS antennas and that of the users grow large with a bounded ratio. This allows us to leverage tools from random matrix theory in order to approximate the parameters of the optimal linear precoder and receiver by their deterministic approximations. Such a result is of valuable practical interest, as it provides a handier way to implement the optimal precoder and receiver. To reduce further the complexity, we propose to apply the truncated polynomial expansion (TPE) concept on a per-user basis to approximate the inverse of large matrices that appear on the expressions of 4 the optimal linear transceivers. Using tools from random matrix theory, we determine deterministic approximations of the SINR and the transmit power in the asymptotic regime. Then, the optimal per-user weight coefficients that solve the max-min SINR problem are derived. The simulation results show that the proposed precoder and receiver provide very close to optimal performance while reducing significantly the computational complexity. As a second part of this work, the TPE technique in a per-user basis is applied to the optimal linear precoding that minimizes the transmit power while satisfying a set of target SINR constraints. Due to the emerging research field of green cellular networks, such a problem is receiving increasing interest nowadays. Closed form expressions of the optimal parameters of the proposed low complexity precoding for power minimization are derived. Numerical results show that the proposed power minimization precoding approximates well the performance of the optimal linear precoding while being more practical for implementation.

Full dimension (FD) multiple-input multiple-output (MIMO) technology has recently attracted substantial research attention in the 3rd Generation Partnership Project (3GPP) as a promising technique for the next-generation of wireless communication networks. FD-MIMO scenarios utilize a planar two-dimensional (2D) active antenna system (AAS) that not only allows a large number of antenna elements to be placed within feasible base station (BS) form factors, but also provides the ability of elevation beamforming. This dissertation presents the elevation beamforming analysis for cellular networks utilizing FD massive MIMO antenna arrays. In particular, two architectures are proposed for the AAS - the uniform linear array (ULA) and the uniform circular array (UCA) of antenna ports, where each port is mapped to a group of vertically arranged antenna elements with a corresponding downtilt weight vector. To support FD-MIMO techniques, this dissertation presents two different 3D ray-tracing channel modeling approaches, the ITU based ‘antenna port approach’ and the 3GPP technical report (TR) 36.873 based ‘antenna element approach’. The spatial correlation functions (SCF)s for both FD-MIMO arrays are characterized based on the antenna port approach. The resulting expressions depend on the underlying angular distributions and antenna patterns through the Fourier series coefficients of the power spectra and are therefore valid for any 3D propagation environment. Simulation results investigate the performance patterns of the two arrays as a function of several channel and array parameters. The SCF for the ULA of antenna ports is then characterized in terms of the downtilt weight vectors, based on the more recent antenna element approach. The derived SCFs are used to form the Rayleigh correlated 3D channel model. All these aspects are put together to provide a mathematical framework for the design of elevation beamforming schemes in single-cell and multi-cell scenarios. Finally, this dissertation proposes to use the double scattering channel to model limited scattering in realistic propagation environments and derives deterministic equivalents of the signal-to-interference-plus-noise ratio (SINR) and ergodic rate with regularized zeroforcing (RZF) precoding. The performance of a massive MIMO system is shown to be limited by the number of scatterers. To this end, this dissertation points out future research directions

Massive MU-MIMO (Multiuser-Multiple Input and Multple Output) is a promising technology for 5G wireless communications because of its spectrum and energy efficiency. To combat the distortion from multipath fading channel, the acquisition of channel state information is essential, which generally requires the training signal that lowers the data rate. In addition, coarse quantization can reduce the high computational energy and cost, yet results in the loss of information. In this thesis, an iterative decision feedback receiver, including iterative Channel Estimation (CE) and equalization, is constructed for a Massive MU-MIMO uplink system. The impact of multipath distortion and coarse quantization can be gradually reduced due to the iterative structure that exploits extrinsic feedback to improve the CE and data detection, so that the data rate is improved by reducing training signals for CE and by using low precision quantization. To observe and evaluate the convergence behaviour, an Extrinsic Information Transfer (EXIT) chart method is utilized to visualize the performance of the iterative receiver. / Graduate

Entrées multiples, sorties multiples (MIMO) massif est considéré comme l'une des technologies clés de la prochaine génération de communications sans fil. Afin d'effectuer des algorithmes de formation de faisceau en liaison descendante (DL) avec un grand réseau d'antennes, le plus grand défi est l'acquisition d'informations précises d'état de canal à l'émetteur (CSIT). Pour relever ce défi, le duplex à division temporelle (TDD) est favorable aux systèmes MIMO massif grâce à sa réciprocité de canal de la DL et la liaison montante (UL). Cependant, alors que le canal physique dans l'air est réciproque, les front-ends de radiofréquence (RF) dans les émetteurs-récepteurs ne le sont pas ; par conséquent, la calibration devrait être utilisée dans des systèmes pratiques pour compenser l'asymétrie matérielle RF. Dans cette thèse, nous nous efforçons de transformer le concept MIMO massif en réalité en utilisant la calibration de la réciprocité TDD. Les contributions peuvent être résumées comme suit. Tout d'abord, nous proposons un cadre unifié pour la calibration de la réciprocité, qui généralise diverses méthodes de calibration existant dans la littérature, offrant une vue supérieure sur le problème de calibration ainsi que l'ouverture de nombreuses innovations sur les méthodes de calibration. Deuxièmement, sur la base de cette représentation générale, nous proposons trois nouveaux schémas de calibration : une méthode de calibration rapide basée sur le groupement d'antennes, un schéma de calibration pour l'architecture hybride de formation de faisceau, ainsi qu'un mécanisme de suivi des paramètres de calibration et de surveillance de la santé du système qui permet une détection rapide du changement de paramètre. Troisièmement, nous avons effectué des mesures des paramètres de calibration sur une plate-forme réelle afin de révéler les propriétés matérielles. Quatrièmement, nous étudions, du point de vue du système, avec quelle précision un système MIMO massif TDD devrait être calibré. Enfin, grâce à la calibration de réciprocité TDD, nous avons construit un banc d’essai pour MIMO massif, qui est compatible avec l'évolution à long terme (LTE) basé sur la plate-forme « open source » OpenAirInterface, et peut directement fournir un service Internet à un appareil commercial. Le banc d'essai démontre la faisabilité d'intégrer le MIMO massif dans les normes actuelles du projet de partenariat de troisième génération (3GPP) et son utilisation dans le 5G peut être une évolution à partir des systèmes 4G actuels. / Massive multiple-input multiple-output (MIMO) is considered as one of the key technologies that will enable the next generation of wireless communications. In order to perform downlink (DL) beamforming algorithms with large antenna arrays, the biggest challenge is the acquisition of accurate channel state information at the transmitter (CSIT). To take up this challenge, time division duplex (TDD) is favorable to massive MIMO systems thanks to its channel reciprocity in DL and uplink (UL). However, while the physical channel in the air is reciprocal, the radio-frequency (RF) front-ends in transceivers are not; therefore, calibration should be used in practical systems to compensate the RF hardware asymmetry. In this thesis, we focus on turning massive MIMO concept into reality based on TDD reciprocity calibration. The contributions can be summarized as follows. First, we propose a unified framework for reciprocity calibration, which generalizes various calibration methods existing in literature, providing a higher level view on the calibration problem as well as opening up possibilities of numerous innovations on calibration methods. Second, based on this general representation, we propose three new calibration schemes: a fast calibration method based on antenna grouping, a calibration scheme for hybrid beamforming architecture, as well as a calibration parameter tracking and system health monitoring mechanism which allows fast detection of parameter change. Third, we carried out measurements of calibration parameters on a real platform in order to reveal the hardware properties. Fourth, we study, from a system point of view, how accurately a TDD massive MIMO system should be calibrated. Last but not least, enabled by TDD reciprocity calibration, we build up an open source long term evolution (LTE) compatible massive MIMO testbed based on the OpenAirInterface platform, which can directly provide Internet service to a commercial device. The testbed demonstrates the feasibility of integrating massive MIMO into current 3rd Generation Partnership Project (3GPP) standards and its usage in 5G can be a smooth evolution from current 4G systems.

Compressed sensing has been a very active area of research and several elegant algorithms have been developed for the recovery of sparse signals in the past few years. However, most of these algorithms are either computationally expensive or make some assumptions that are not suitable for all real world problems. Recently, focus has shifted to Bayesian-based approaches that are able to perform sparse signal recovery at much lower complexity while invoking constraint and/or a priori information about the data. While Bayesian approaches have their advantages, these methods must have access to a priori statistics. Usually, these statistics are unknown and are often difficult or even impossible to predict. An effective workaround is to assume a distribution which is typically considered to be Gaussian, as it makes many signal processing problems mathematically tractable. Seemingly attractive, this assumption necessitates the estimation of the associated parameters; which could be hard if not impossible. In the thesis, we focus on this aspect of Bayesian recovery and present a framework to address the challenges mentioned above. The proposed framework allows Bayesian recovery of sparse signals but at the same time is agnostic to the distribution of the unknown sparse signal components. The algorithms based on this framework are agnostic to signal statistics and utilize a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. In the thesis, we propose several algorithms based on this framework which utilize the structure present in signals for improved recovery. In addition to the algorithm that considers just the sparsity structure of sparse signals, tools that target additional structure of the sparsity recovery problem are proposed. These include several algorithms for a) block-sparse signal estimation, b) joint reconstruction of several common support sparse signals, and c) distributed estimation of sparse signals. Extensive experiments are conducted to demonstrate the power and robustness of our proposed sparse signal estimation algorithms. Specifically, we target the problems of a) channel estimation in massive-MIMO, and b) Narrowband interference mitigation in SC-FDMA. We model these problems as sparse recovery problems and demonstrate how these could be solved naturally using the proposed algorithms.

Terahertz (THz)-band communication is envisioned as a critical technology that could satisfy the need for much higher data rates in sixth generation wireless communi- cation (6G) systems and beyond. Although THz signal propagation suffers from huge spreading and molecular absorption losses that limit the achievable commu- nication ranges, ultra-massive multiple-input multiple-output (UM-MIMO) antenna arrays can introduce the required beamforming gains to compensate for these losses. The reconfigurable UM-MIMO systems of small footprints motivate the use of spatial modulation techniques. Furthermore, the ultra-wideband fragmented THz spectrum motivates the use of index modulation techniques over multicarrier channels. In this thesis, we consider the problem of efficient index mapping and data detection in THz- band index modulation paradigms. We first propose an accurate frequency-domain statistical UM-MIMO channel model for wideband multicarrier THz-band commu- nications by considering THz-specific features. We then propose several THz-band generalized index modulation schemes that provide various performance and complex- ity tradeoffs. We propose efficient algorithms for mapping information bits to antenna and frequency indices at the transmitter side to enhance the achievable data rates in THz channel uses. We further propose complementary low-complexity parameter estimation and data detection techniques at the receiver side that can scale efficiently with very high rates. We derive theoretical bounds on the achievable performance gains of the proposed solutions and generate extensive numerical results promoting the corresponding future 6G use cases.

In this work, a coordinate wise descent algorithm is implemented which serves the purpose of estimating active users in a base station/client wireless communication setup. The implemented algorithm utilizes the sporadic nature of users, which is believed to be the norm with 5G Massive MIMO and Internet of Things, meaning that only a subset of all users are active simultaneously at any given time. This work attempts to estimate the viability of a direct algorithm implementation to test if the performance requirements can be satisfied or if a more sophisticated implementation, such as a parallelized version, needs to be created.The result is an isomorphic ASIC implementation made in a 28 nm FD-SOI process, with proper internal word lengths extracted through simulation. Some techniques to lessen the burden on hardware without losing performance is presented which helps reduce area and increase speed of the implementation. Finally, a parallelized version of the algorithm is proposed, if one should desire to explore an implementation with higher system throughput, at almost no furtherexpense of user estimation error.