协同认知无线网络中自适应中继节点选择算法

最优停止规则算法的优点是避免主用户考察所有次用户,使主用户能在较短观测时间内挑选出满足通信服务质量的中继节点。但存在些许不足之处,如仅考虑了单次协同通信情况,没有充分利用历史中继节点选择信息。为了克服上述缺点,需要优化该算法的性能,通过引入反馈机制,提出了自适应中继节点选择算法,在每次中继节点选择结束后更新信息库中次用户中继概率,主用户从中继概率高的次用户开始观测。研究结果表明:自适应中继节点选择算法的效率要比最优停止规则算法高;该算法可进一步减少观测次数,降低系统的传输时延和观测能耗,提高主用户的平均吞吐率。

Self-Adaptive Relay Selection Algorithm in Cooperative Cognitive Radio Networks

The advantage of optimal stopping rule algorithm is to avoid PU scanning all SU, and the PU can find out the relay with good channel quality within a short observation time. However, there is a deficiency tha it just considers the single cooperation and does not take full advantage of the previous relay node selection information. By introducing the feedback mechanism, a self-adaptive relay selection algorithm is proposed,thus to solve the above problem and optimize the performance of the algorithm. The relay probability of SU is updated after every relay selection, and PU starts the observation of SU with high relay probability. Simulation results indicate that this self-adaptive relay selection algorithm is more efficient than optimal stopping rule algorithm, and could reduce observation frequency, decrease system latency and power consumption while improving the average throughput of PU.