Optimal and Suboptimal Access and Transmission Polices for Dynamic Spectrum Access over Fading Channels in Cognitive Radio Networks

We propose a cross-layer optimal access and transmission framework for dynamic spectrum access to maximize expected long-term average throughput under power and collision constraints by a dynamic programming method namely Constrained Markov decision process (CMDP). The optimal policy for CMDP is capable of guiding transmitter to choose an available channel and transmission rate at the beginning of each frame for its long-term goals according to current channel sensing results and prior channel fading information. The complexity of finding the optimal policy by Linear programming (LP) approach increases exponentially with the number of channels and fading levels, which incurs so-called curse of dimensionality. Therefore we propose two complexity-reduced suboptimal policies, namely, policy separation and heuristic algorithms. Finally, we compare the performances of policies by numerical results.