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This paper investigates the design of access policies in spectrum sharingnetworks by exploiting the retransmission protocol of legacy primary users(PUs) to improve the spectral efficiency via opportunistic retransmissions atsecondary users (SUs) and chain decoding. The optimal policy maximizing the SUthroughput under an interference constraint to the PU and its performance arefound in closed form. It is shown that the optimal policy randomizes amongthree modes: Idle, the SU remains idle over the retransmission window of thePU, to avoid causing interference; Interference cancellation, the SU transmitsonly after decoding the PU packet, to improve its own throughput viainterference cancellation; Always transmit, the SU transmits over theretransmission window of the PU to maximize the future potential ofinterference cancellation via chain decoding. This structure is exploited todesign a stochastic optimization algorithm to facilitate learning andadaptation when the model parameters are unknown or vary over time, based onARQ feedback from the PU and CSI measurements at the SU receiver. It is shownnumerically that, for a 10% interference constraint, the optimal access policyyields 15% improvement over a state-of-the-art scheme without SUretransmissions, and up to 2x gain over a scheme using a non-adaptive accesspolicy instead of the optimal one.

Optimal Spectrum Sharing with ARQ based Legacy Users via Chain Decoding