Reactive relay selection in underlay cognitive networks with fixed gain relays

Abstract

Best relay selection is a bandwidth efficient technique for multiple relay environments without compromising the system performance. The problem of relay selection is more challenging in underlay cognitive networks due to strict interference constraints to the primary users. Generally, relay selection is done on the basis of maximum end-to-end signal to noise ratio (SNR). However, it requires large amounts of channel state information (CSI) at different network nodes. In this paper, we present and analyze a reactive relay selection scheme in underlay cognitive networks where the relays are operating with fixed gains near a primary user. The system model minimizes the amount of CSI required at different nodes and the destination selects the best relay on the basis of maximum relay to destination SNR. We derive close form expressions for the received SNR statistics, outage probability, bit error probability and average channel capacity of the system. Simulation results are also presented to confirm the validity of the derived expressions.