Low-complexity antenna selection techniques for massive MIMO systems

Abstract

Massive Multiple-Input Multiple-Output (M-MIMO) is a state of the art technology in wireless communications, where hundreds of antennas are exploited at the base station (BS) to serve a much smaller number of users. Employing large antenna arrays can improve the performance dramatically in terms of the achievable rates and radiated energy, however, it comes at the price of increased cost, complexity, and power consumption. To reduce the hardware complexity and cost, while maintaining the advantages of M-MIMO, antenna selection (AS) techniques can be applied where only a subset of the available antennas at the BS are selected. Optimal AS can be obtained through exhaustive search, which is suitable for conventional MIMO systems, but is prohibited for systems with hundreds of antennas due to its enormous computational complexity. Therefore, this thesis address the problem of designing low complexity AS algorithms for multi-user (MU) M-MIMO systems. In chapter 3, different evolutionary algorithms including bio-inspired, quantuminspired, and heuristic methods are applied for AS in uplink MU M-MIMO systems. It was demonstrated that quantum-inspired and heuristic methods outperform the bio-inspired techniques in terms of both complexity and performance. In chapter 4, a downlink MU M-MIMO scenario is considered with Matched Filter (MF) precoding. Two novel AS algorithms are proposed where the antennas are selected without any vector multiplications, which resulted in a dramatic complexity reduction. The proposed algorithms outperform the case where all antennas are activated, in terms of both energy and spectral efficiencies. In chapter 5, three AS algorithms are designed and utilized to enhance the performance of cell-edge users, alongside Max-Min power allocation control. The algorithms aim to either maximize the channel gain, or minimize the interference for the worst-case user only. The proposed methods in this thesis are compared with other low complexity AS schemes and showed a great performance-complexity trade-off.