In cognitive radio networks (CRNs), TCP goodput is one of the key issues to measure it's performance. However, most existing research efforts on TCP performance improvement have two weaknesses as follows: first of all, most of them only consider the underlying parameters to optimize the physical performance, the TCP performance have been neglected; Second, they are largely formulated as a Markov Decision Process (MDP), which requires a complete knowledge of network and cannot be directly applied to distributed CRNs. To solve the above problems, a Q-BMDP algorithm is proposed in this paper: Each user in CRN autonomously decides modulation type and transmitting power in PHY, channels to access in MAC to find the best TCP goodput. Due to the existence of perception error of environment, this issue is formulated as a Partial Observable Markov Decision Process (POMDP) which is then converted to belief state MDP, with Q-value iteration to find the optimal strategy. Simulation results show that the network can learn optimal strategy to effectively improve TCP goodput in dynamic wireless network.
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