Transmission control in cognitive radio systems with latency constraints as a switching control dynamic game

This paper addresses the secondary user rate adaptation problem in cognitive radio networks. By modeling primary user activities and secondary user block fading channels as finite state Markov chains, we formulate the transmission rate adaptation problem of each secondary user as a zero-sum dynamic Markovian game with a delay constraint. The Nash equilibrium of the resulting game is available and all of the Nash equilibria have a unique value vector. Conditions are given so that the Nash equilibrium transmission policy of each user is a randomized mixture of pure threshold policies. Such threshold policies are easily implementable. Finally, we present a stochastic approximation algorithm which can adaptively estimate Nash equilibrium policies and track such policies for non-stationary problems where the statistics of the channel and user parameters evolve with time.