Compress-and-Forward Based Strategy in Overlay Cognitive Radio Channel with Partial CSIT

In this paper, we consider an extension of the cognitive radio channel model in which the secondary transmitter has to obtain (learn) the primary message rather than having non-causal knowledge of it. We formulate an achievable rate region that combines elements of compress-and-forward relaying with coding for the pure cognitive radio channel model. Moreover, we provide parameters design that maximizes the secondary rate without affecting the primary rate in the Rician channel with partial channel state information at the transmitter (CSIT). Simulation results demonstrate that the proposed compress-and-forward based strategy achieves good performance.