L1/2-Regularization Based Antenna Selection for RF-Chain Limited Massive MIMO Systems

Massive Multi-input Multi-output (MIMO) technique shows great potentials in improving energy and spectral efficiency while suffers high costs of the requirement for large amount of radio frequency (RF) chains. In this paper, we develop an l1/2-regularity based downlink transmit antenna selection scheme for massive MIMO systems with limited RF chains. With the objective to minimize the transmission power at the given number of RF-chains and transmission quality, we formulate and decompose the original l1/2-norm optimization problem into two separated problems with l1/2-sparsity and signal-interference-to-noise-ratio (SINR) requirements, respectively. An iterative algorithm based on coordinate descent and feasible set projection are then developed. This scheme provides an efficient way to evaluate the minimum required RF chains for the deployment of a massive MIMO system. Simulation results show that the proposed scheme achieves higher energy efficiency compared with the lp(p > 1/2)-norm based antenna selection methods, especially for large numbers of correlated antennas.