Sparse Bayesian learning, beamforming techniques and asymptotic analysis for massive MIMO

Multiple antennas at the base station side can be used to enhance the spectral efficiency

and energy efficiency of the next generation wireless technologies. Indeed, massive

multi-input multi-output (MIMO) is seen as one promising technology to bring the aforementioned benefits for fifth generation wireless standard, commonly known as 5G New Radio (5G NR). In this monograph, we will explore a wide range of potential topics in multi-user MIMO (MU-MIMO) relevant to 5G NR, 

• Sum rate maximizing beamforming (BF) design and robustness to partial channel state information at the transmitter (CSIT)

• Asymptotic analysis of the various BF techniques in massiveMIMO and

• Bayesian channel estimationmethods using sparse Bayesian learning.

While massive MIMO has the aforementioned benefits, it makes the acquisition of the channel state information at the transmitter (CSIT) very challenging. Since it requires large amount of uplink (UL) pilots for channel estimation phase. Moreover, each antenna has associated with a radio frequency (RF) chain which in turn leads to high power consumption and hardware complexity at the base station (BS) side. One promising technology to overcome these issues is to utilize a hybrid beamforming (HBF) system. In HBF, the number of RF chains at the transmitter side is reduced significantly compared to number of antennas. Hence, it involves a two stage beamforming scheme. With the analog BF generates multiple beams in the spatial domain and thereby providing BF gain. The digital BF is used at the baseband for multiplexing the different user streams across the beams generated by the analog BF. Analog beamforming is implemented at the RF chain using phase shifters. One of our main focus in thesis is to propose efficient phase shifter design which can attain performance very close to that of the fully digital BF systems. For this purpose, we proposed an efficient scheme for analog phasor design using the technique of deterministic annealing. Fully digital BF scheme becomes a special case of our HBF design and further for the performance analysis, we focus on fully digital BF schemes itself. In a fully digital massive MIMO system, it is important to consider low complexity BF solutions. With this direction in mind, we proposed a low complexity but close to optimal (linear minimum mean square error-LMMSE) BF solution termed as reduced order zero forcing (ZF). However, it is quite incomplete if we stop with the various BF designs, we do require extensive theoretical analysis to evaluate the spectral

efficiency (SE) behaviour of the massiveMIMO system which we consider in the next part of the thesis.