Massive MIMO Technologies for 5G and Beyond-5G Wireless Networks

Massive multiple input multiple output (MIMO) is a promising 5G and beyond5G wireless access technology that can provide huge throughput, compared with the current technology, in order to satisfy some requirements for the future generations of wireless networks. The research described in this thesis proposes the design of some applications of the massive MIMO technology that can be implemented in order to increase the spectral efficiency per cell of the future wireless networks through a simple and low complexity signal processing. In particular, massive MIMO is studied in conjunction with two other topics that are currently under investigation for the future wireless systems, both in academia and in industry: the millimeter wave frequencies and the distributed antenna systems. The first part of the thesis gives a brief overview on the requirements of the future wireless networks and it explains some of the mathematical tools used in the current massive MIMO literature. Then, an overview on the differences between massive MIMO techniques at the conventional cellular frequencies and at millimeter wave frequencies is presented and exhaustively discussed. Six key basic differences are pinpointed, along with the implications that they have on the architecture and algorithms of the communication transceivers and on the attainable performance in terms of reliability and multiplexing capabilities. Subsequently, “doubly massive MIMO” systems at millimeter wave frequencies are introduced, i.e., systems with a large number of antennas at both the transmitter and the receiver. For complexity reasons and energy consumption issues, fully digital pre-coding and post-coding structures may turn out to be unfeasible, and thus suboptimal structures, making use of simplified hardware and a limited number of radio-frequency chains, have been investigated. A comparative assessment of several suboptimal pre-coding and post-coding structures with large number of antennas is discussed. Numerical results show that fullydigital beamformers may actually achieve a larger energy efficiency than lowercomplexity solutions, as well as that low-complexity beam-steering purely analog beamforming may in some cases represent a good performance-complexity trade-off solution. Finally, the thesis focuses on the recently introduced cell-free massive MIMO architecture, wherein a very large number of distributed access points, connected to a central processing unit, simultaneously and jointly serve a much smaller number of mobile stations. It contrasts the originally proposed formulation of cell-free massive MIMO with a user-centric approach wherein each mobile station is served only by a limited number of access points. Exploiting the framework of successive lower-bound maximization, this thesis also proposes and analyzes two power allocation strategies aimed at maximizing the throughput and the fairness of these systems. Additionally, advanced signal processing techniques, to improve the performance of the user-centric approach both in uplink and in downlink, are proposed. The proposed schemes can be implemented locally, i.e., with no need to exchange information with the central processing unit. Numerical results show that the user-centric approach, which requires smaller backhaul overhead and it is more scalable than the cell-free massive MIMO deployment, also achieves generally better performance than the cell-free massive MIMO approach for the vast majority of the users in the system, especially on the uplink. Regarding the proposed advanced signal processing techniques, the results show that they provide remarkable performance improvements with respect to the competing alternatives.