Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Receive antenna selection for MIMO spatial multiplexing: theory and algorithms
IEEE Transactions on Signal Processing
Capacity of MIMO systems with antenna selection
IEEE Transactions on Wireless Communications
Capacity of MIMO Channels With Antenna Selection
IEEE Transactions on Information Theory
Antenna selection in MIMO systems
IEEE Communications Magazine
Computationally efficient near-optimal combined antenna selection algorithms for V-BLAST systems
Digital Signal Processing
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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.