Signal Processing Aspects of Massive MIMO and IRS-Aided Communications

The data traffic in cellular networks has grown at an exponential pace for decades. This trend will most probably continue in the future, driven by new innovative applications. One of the key enablers of future cellular networks is the massive MIMO technology, and it has been started to be commercially deployed in many countries. A massive MIMO base station is equipped with a massive number (e.g., a hundred) of individually steerable antennas, which can be effectively used to serve tens of user equipments simultaneously on the same time-frequency resource. It can provide a notable enhancement of both spectral efficiency and energy efficiency in comparison with conventional MIMO. In the prior literature, the achievable spectral efficiencies of massive MIMO systems with a practical number of antennas have been rigorously characterized and optimized when the channels are subject to either spatially uncorrelated or correlated Rayleigh fading. Typically, in massive MIMO research, i.i.d. Rayleigh fading or less frequently free-space line-of-sight (LoS) channel models are assumed since they simplify the analysis. Massive MIMO technology is able to support both rich scattering and LoS scenarios. Practical channels can consist of a combination of an LoS path and a correlated small-scale fading component caused by a finite number of scattering clusters that can be modeled by spatially correlated Rician fading. In Paper \ref{PaperA}, we consider a multi-cell scenario with spatially correlated Rician fading channels and derive closed-form achievable spectral efficiency expressions for different signal processing techniques. Alternatively, a massive number of antennas can be spread over a large geographical area and this concept is called cell-free massive MIMO. In the canonical form of cell-free massive MIMO, the access points cooperate via a fronthaul network to spatially multiplex the users on the same time-frequency resource using network MIMO methods that only require locally obtained channel state information. Cell-free massive MIMO is a densely deployed system. Hence, the probability of having an LoS path between some access points and the users is quite high. In Paper B, we consider a practical scenario where the channels between the access points and the users are modeled with Rician fading. The main theory for massive MIMO has been developed for uni-polarized single-antenna users. Wireless signals are polarized electromagnetic waves, and there exist two orthogonal polarization dimensions. The practical base stations and user equipments typically utilize dual-polarized antennas (i.e., two co-located antennas that respond to orthogonal polarizations) to squeeze in twice the number of antennas in the same physical enclosure, as well as capturing signal components from both dimensions. In Paper C, we study a single-cell massive MIMO system with dual-polarized antennas at both the base station and users. The channel modeling for dual-polarized channels is substantially more complicated than for conventional uni-polarized channels. A channel model that takes into account several practical aspects that arise when utilizing dual-polarization, such as channel cross-polar discrimination (XPD) and cross-polar receive and transmit correlations (XPC) is considered. Another technology that has exciting prospects and is quickly gaining traction in wireless communications is intelligent reflecting surfaces (IRS). It is also known under the names reconfigurable intelligent surfaces and software-controlled metasurfaces. IRS is a thin two-dimensional metasurface that is used to aid communications. According to the application of interest, an IRS has the ability to control and transform electromagnetic waves that are impinging on it. In this thesis, we study different aspects of this technology such as pathloss modeling, channel estimation, and different technology use cases. In Paper D, we derive the pathloss model using physical optics techniques for an IRS that is configured to reflect an incoming wave from a far-field source towards a receiver in the far-field. In Paper E, we demonstrate how an IRS can be used to increase the rank of the channel matrix in LoS point-to-point MIMO communications by creating a controllable path that complements the uncontrollable paths. Bringing IRS technology into reality requires addressing many practical challenges. For instance, the proper configuration of an IRS critically depends on accurate channel state information. However, there are two main issues that complicate the channel acquisition with IRS. First, the IRS is not inherently equipped with transceiver chains. Therefore, it can not sense the pilot signals. Besides, introducing an IRS into an existing setup will increase the number of channel coefficients proportionally to the number of IRS elements. In Paper F, we present a deep learning-based approach for phase reconfiguration at an IRS in order to learn and make use of the local propagation environment