Recently, massive multiple-input multiple-output (MIMO) systems have gained significant attention as a new network architecture to not only achieving unprecedented spectral and energy efficiencies, but also to alleviating propagation losses and inter-user/inter-cell interference. Therefore, massive MIMO has been identified as one of the key candidate technologies for the 5th generation wireless standard. This dissertation thus focuses on (1) developing a performance analysis framework for cognitive massive MIMO systems by investigating the uplink transmissions of multi-cell multi-user massive MIMO secondary systems, which are underlaid in multi-cell multi-user primary massive MIMO systems, with taking into consideration the detrimental effects of practical transmission impairments, (2) proposing a new wireless-powered underlay cognitive massive MIMO system model, as the secondary user nodes is empowered by the ability to efficiently harvest energy from the primary user transmissions, and then access and utilize the primary network spectrum for information transmission, and (3) developing a secure communication strategy for cognitive multi-user massive MIMO systems, where physical layer secure transmissions are provisioned for both primary and secondary systems by exploiting linear precoders and artificial noise (AN) generation in order to degrade the signal decodability at eavesdropper. The key design feature of the proposed cognitive systems is to leverage the spatial multiplexing strategies to serve a large number of spatially distributed user nodes by using very large numbers of antennas at the base-stations. Moreover, the fundamental performance metrics, the secondary transmit power constraints, which constitute the underlay secondary transmissions subject to a predefined primary interference temperature, and the achievable sum rates of the primary and secondary systems, are characterized under different antenna array configurations. Additionally, the detrimental impact of practical wireless transmission impairments on the performance of the aforementioned systems are quantified. The important insights obtained throughout these analyses can be used as benchmarks for designing practical cognitive spectrum sharing networks.

The broadcast nature of wireless medium has made information security as one of the most important and critical issues in wireless systems. Physical layer security, which is based on information-theoretic secrecy concepts, can be used to secure the wireless channels by exploiting the noisiness and imperfections of the channels. Massive multiple-input multiple-output (MIMO) systems, which are equipped with very large antenna arrays at the base stations, have a great potential to boost the physical layer security by generating the artificial noise (AN) with the exploitation of excess degrees-of-freedom available at the base stations. In this thesis, we investigate physical layer security provisions in the presence of passive/active eavesdroppers for single-hop massive MIMO, dual-hop relay-assisted massive MIMO and underlay spectrum-sharing massive MIMO systems. The performance of the proposed security provisions is investigated by deriving the achievable rates at the user nodes, the information rate leaked into the eavesdroppers, and the achievable secrecy rates. Moreover, the effects of active pilot contamination attacks, imperfect channel state information (CSI) acquisition at the base-stations, and the availability of statistical CSI at the user nodes are quantified. The secrecy rate/performance gap between two AN precoders, namely the random AN precoder and the null-space based AN precoder, is investigated. The performance of hybrid analog/digital precoding is compared with the full-dimensional digital precoding. Furthermore, the physical layer security breaches in underlay spectrum-sharing massive MIMO systems are investigated, and thereby, security provisions are designed/analyzed against active pilot contamination attacks during the channel estimation phase. A power-ratio based active pilot attack detection scheme is investigated, and thereby, the probability of detection is derived. Thereby, the vulnerability of uplink channel estimation based on the pilots transmitted by the user nodes in time division duplexing based massive MIMO systems is revealed, and the fundamental trade-offs among physical layer security provisions, implementation complexity and performance gains are discussed.

Massive multiple-input multiple-output is a promising technique for the next generation of wireless communication systems which addresses most of the critical challenges associated with concurrent relaying systems, such as digital signal processing complexity, long processing delay, and low latency wireless communications. However, the deployment of conventional fully digital beamforming methods, dedicates one radio frequency (RF) chain to each antenna, is not viable enough due to the high fabrication/implementation cost and power consumption. In this thesis, we envision to address this critical issue by reducing the number of RF chains in a viable analog/digital configuration paradigm which is usually referred to hybrid structure. From another viewpoint, the development of fifth generation enabling technologies brings new challenges to the design of power amplifiers (PAs). In particular, there is a strong demand for low-cost, nonlinear PAs which, however, introduce nonlinear distortions. On the other hand, contemporary expensive PAs show great power efficiency in their nonlinear region. Inspired by this trade-off between nonlinearity distortions and efficiency, finding an optimal operating point is highly desirable, and this is the second key contribution of this thesis.

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.

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.

During this project we have designed a new type of antenna that uses an array of antenna elements in order to emit electromagnetic radiation as signals and to be able to control the beam. After an extended time the design yielded a simulation which had the correct characteristics. After printing and constructing a prototype of the antenna it was tested in an anechoic chamber at Uppsala University. The array was divided into two different sub-arrays: the upper and the lower sub-arrays. Each of these consisted in itself of two sides: the long and the short sides. Each side had seven radiating elements, during the tests only one of the two sub-arrays (upper or lower) was running. Both sub-arrays are excited via a rat-race or 90 degree coupler. While the antenna was running it had 14 radiating elements and two phase shifters, two per sub-array and two per side. The idea was for a signal to travel passing the radiating elements and the phase shifter which would steer the induced electromagnetic signal in one direction, a traveling-wave array. This direction could be changed since the phase shifters were configurable in three different states per phase shifter, hence the induced electromagnetic beam was steerable. The beam was also steerable through the feed which was re-configurable, since there were two feeds per sub-array a phase shift could be introduced between the long and the short side. The beam steering range was between -2 degrees and 11 degrees oriented as 0 degrees would be a perpendicular line from the array to the receiving end. The design itself worked which could be stated from the results in the upper part of the array, the test results from the lower part however did not match the simulated results. This is likely due to an error in the construction of the antenna rather than the theory since the upper and lower part of the array was mirrored versions of each other. The phase shifters worked as intended in the bigger picture but were slightly different in the simulations compared to the physical ones, likely due to the same type of error source as regarding the full antenna.