Effects of Channel Prediction for Transmit Antenna Selection With Maximal-Ratio Combining in Rayleigh Fading

Antenna selection is a low-complexity method for pragmatically exploiting spatial diversity in wireless systems. It has potentially reduced hardware cost compared with space-time or multiple-input-multiple-output (MIMO) coding due to the reduction in the amount of radio-frequency hardware required. Although receive antenna selection is, perhaps, more common, transmit antenna selection (TAS) offers several advantages, particularly for hardware-costly transmit schemes, such as, methods that require linearization. In use, TAS requires at least partial channel knowledge at the transmitter to perform selection. This knowledge usually comes in the form of an index to the best set of antenna/antennas that are fed back from the receiver, which implies a delay between the channel that is sampled (at the receiver) and this knowledge being acted upon (at the transmitter). In this paper, performance degradation due to outdated channel knowledge is analytically determined and related to channel characteristics. A predictive scheme is then developed to mitigate delay-induced degradation. Several factors that are related to TAS system performance under different channel scenarios, both with and without mitigation, are explored. Closed-form expressions for performance metrics such as bit error rate, outage probability, average signal-to-noise ratio (SNR) gain, and higher order moments of the output SNR, are derived and verified by simulations. The impact of prediction is analyzed for different TAS setups and channel prediction scenarios, as well as various system design parameters.