Receive antenna selection in diversely polarized MIMO transmissions with convex optimization

In this paper, we present a low complexity approach to receive antenna selection for capacity maximization, based on the theory of convex optimization. By relaxing the antenna selection variables from discrete to continuous, we arrive at a convex optimization problem. We show via extensive Monte-Carlo simulations that the proposed algorithm provides performance very close to that of the optimal selection based on an exhaustive search. We consecutively optimize not only the selection of the best antennas but also the angular orientation of individual antenna elements in the array for a so-called true polarization diversity system. Dual- and triple-polarized antenna structures are a very good solution for realizing compact devices and also robust against many imperfections as compared to spatially separated antenna structures. Effectively we extend our work from two dimensional antenna structures to three dimensions. We model such polarized antenna systems and then apply convex optimization theory for selecting the best possible antennas in terms of capacity maximization. Channel parameters like transmit and receive correlations, and cross-polarization discrimination (XPDXPD) are taken into consideration while modeling polarized systems. We compare our results with the Spatially Separated (SP) MIMO with and without selection by performing extensive Monte Carlo simulations. We found that by using a convex optimization algorithm, the performance of multiple polarized systems can be significantly enhanced. For certain channel conditions we observe that triple polarized systems increase the performance significantly compared to dual-polarized and spatially separated systems. We observed that applying selection at the receiver only boosts the performance in Non-Line of Sight (NLOS) channels compared to Line of Sight (LOS) channels.