Joint transmit and receive antenna selection using a probabilistic distribution learning algorithm in MIMO systems

In this paper, we present a real-time low-complexity joint transmit and receive antenna selection (JTRAS) algorithm. The computational complexity of finding an optimal JTRAS by exhaustive search grows exponentially with the number of transmit and receive antennas. The proposed Estimation of Distribution Algorithm (EDA) is resorts to probabilistic distribution learning evolutionary computation. EDA updates its chosen population at each iteration on the basis of the probability distribution learned from the population of superior candidate solutions chosen at the previous iterations. The proposed EDA has a low computational complexity and can find a nearly optimal solution in real time. Beyond applying the general EDA to JTRAS, we also present a specific improvement to EDA, which reduces computation time by generating cyclic shifted initial population. The proposed EDA for JTRAS has a low computational complexity, and its effectiveness is verified through simulation results.