Adaptive MIMO antenna selection

The performance of multiple-input multiple-output (MIMO) systems can be improved by employing a larger number of antennas than actually used and selecting the optimal subset based on the channel state information. Existing antenna selection algorithms assume perfect channel knowledge and optimize criteria such as Shannon capacity or various bounds on error rate. This paper examines MIMO antenna selection algorithms when the set of possible solutions is large and only a noisy estimate of the channel is available. We propose discrete stochastic approximation algorithms to adoptively select a better antenna subset using criteria such as maximum channel capacity, minimum error rate, etc. We also consider scenarios of time-varying channels for which the antenna selection algorithms can track the time-varying optimal antenna configuration. We present numerical examples to show the convergence of these algorithms and the excellent tracking capabilities.