Convex optimization for receive antenna selection in multi-polarized MIMO transmissions

Diversity and achievable rate in wireless transmissions can be enhanced by using multiple-input multiple-output (MIMO) systems. This directly increases the overall cost due to additional Radio Frequency (RF) chains for each antenna element. The issue can be resolved with antenna selection techniques at the transmitter or the receiver by using a small number of RF chains. These RF chains are multiplexed between larger number of transmitter/receiver antenna elements. 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. 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, cross-polarization discrimination (XPD) 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 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.