在认知无线电网络中，传输层端到端(TCP)吞吐率是衡量网络性能的重要指标。前期相关研究大都具有以下两方面缺点：第一，大部分研究只考虑了协议底层参数来优化物理链路性能，对传输层性能有所忽略；第二，目前的研究大都基于马尔可夫决策过程建模，这需要网络具有完全知识，使得这类模型的应用受到很大限制。针对以上问题，本文提出一种新的算法：网络中每个节点通过联合配置物理层调制方式、发射功率、链路层信道接入和TCP拥塞控制因子来找到传输层端到端近似最优吞吐率。由于无线设备对环境感知存在误差，本文将网络模型建模为部分可观测马尔可夫决策过程，并将其转换成信念状态马尔可夫决策过程，采用Q值迭代找到近似最优策略。仿真分析表明，提出的算法能在动态无线环境下以一定的误差限收敛于最优策略，能在功率受限条件下，有效提高传输层端到端吞吐率。%In cognitive radio network (CRN), TCP end to end throughput is one of the key issues to measure its performance. However, most of existing research efforts devoted to TCP performance improvement have two weaknesses as follows. First, most of them only consider the underlying parameters to optimize the physical performance, but the TCP performance is 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 CRNs. To solve the above problems, a Q-BMDP algorithm is proposed in this paper. Each user in CRN combines modulation type and transmitting power at the physical layer, access channels at the media access control layer and TCP congestion control factor to maximize the TCP throughput. 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 approximately optimal strategy. Simulation and analysis results show that the proposed algorithm can be approximately converged to optimal strategy under a maximum error limit, and can effectively improve TCP throughput in a dynamic wireless network under the premise of the limited power consumption.
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