Thompson Sampling-Based Antenna Selection With Partial CSI for TDD Massive MIMO Systems

Antenna selection (AS) is a promising technology that can reduce the implementation complexity and hardware cost of a massive MIMO system, in which a part of the attainable antennas are selected and connected to the radio frequency chains in each time slot. In this article, we propose a low-complexity AS algorithm to maximize the downlink achievable rate in the time-division duplexing massive MIMO system, which is based on online Thompson sampling technique and significantly reduces the pilot overhead required for channel estimation with only partial channel state information. We prove that the distribution-dependent upper bound of the proposed algorithm is sub-linear as a function of time slot by introducing the concept of regret. We also develop three discounting factors to accommodate the large-scale variations across antennas. Numerical results show that the downlink achievable rate can be greatly improved with our proposed scheme as compared to other typical ones.