Rate and Channel Adaptation in Cognitive Radio Networks Under Time-Varying Constraints

We consider dynamic rate and channel adaptation in a cognitive radio network serving heterogeneous applications under dynamically varying channel availability and rate constraint. We formalize it as a Bayesian learning problem, and propose a novel learning algorithm, called Volatile Constrained Thompson Sampling (V-CoTS), which considers each rate-channel pair as a two-dimensional action. The set of available actions varies dynamically over time due to variations in primary user activity and rate requirements of the applications served by the users. Our algorithm learns to adapt its rate and opportunistically exploit spectrum holes when the channel conditions are unknown and channel state information is absent, by using acknowledgment only feedback. It uses the monotonicity of the transmission success probability in the transmission rate to optimally tradeoff exploration and exploitation of the actions. Numerical results demonstrate that V-CoTS achieves significant gains in throughput compared to the state-of-the-art methods.