一种基于马尔可夫决策过程的认知无线电网络传输调度方案
doi: 10.3724/SP.J.1146.2008.00960
A Transmission and Scheduling Scheme Based on Markov Decision Process in Cognitive Radio Networks
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摘要: 该文提出了一种适用于认知无线电网络的跨层传输调度方案，即满足掉包率约束的前提下最小化平均功率消耗。此方案被建模为约束马尔可夫决策过程(MDP)。采用拉格朗日乘子法求解此MDP，并且提出了一种黄金分割乘子搜索法。提出两种简化方法，即状态聚合以及行动集缩减来解决维灾问题。仿真结果显示简化方法对该方案的性能影响很小，且该方案的平均功耗最低。Abstract: A cross-layer transmission and scheduling scheme of average power minimization in cognitive radio networks under the constraint of packet drop probability is addressed. The scheme is formulated by constrained Markov Decision Process (MDP). Lagrangian multiplier approach is used to solve the MDP, and a golden section search method is proposed to find the multiplier. Two simplifying methods, namely, state aggregate and action set reduction are employed to cope with the curse of dimensionality. Simulation results show that simplifying methods have little influence on the performance of the scheme and average power consumption of the scheme is the lowest.
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