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This paper considers an uplink time division multiple access (TDMA) cognitive radio network where multiple cognitive radios (secondary users) attempt to access a spectrum hole. We assume that each secondary user can access the channel according to a decentralized predefined access rule based on the channel quality and the transmission delay of each secondary user. By modeling secondary user block fading channel qualities as a finite state Markov chain, we formulate the transmission rate adaptation problem of each secondary user as a general-sum Markovian dynamic game with a delay constraint. Conditions are given so that the Nash equilibrium transmission policy of each secondary user is a randomized mixture of pure threshold policies. Such threshold policies can be easily implemented. We then present a stochastic approximation algorithm that can adaptively estimate the Nash equilibrium policies and track such policies for non-stationary problems where the statistics of the channel and user parameters evolve with time.