认知无线电中基于特征信念的协作频谱检测算法

Cooperation Spectrum Sensing Detecting Algorithm Based on Featured Belief Points in Cognitive Radio Network

针对认知无线网络(CRN)中频谱检测准确性与检测效率难以平衡的问题,本文提出一种特征信念的认知无线网络ED/FD协作频谱检测算法。通过单认知用户能量检测与特征信号检测协作模式代替多认知用户协作检测模式,降低通信开销,利用部分可观察马尔可夫决策过程(POMDP)对CRN建模,将检测准确性与检测效率平衡优化问题转化为POMDP最优值函数求解过程,并采用特征信念控制信念状态规模和在线最大报酬值迭代法求解法逼近最优值,降低算法复杂度。实验结果表明,本文算法能有效取得频谱检测准确性与检测效率之间的平衡,达到在不干扰授权用户的同时提高检测效率的目的。

In order to solve the dilemma of the tradeoff between spectrum sensing performance and spectrum sensing efficiency in cognitive radio network, a novel ED/FD cooperation spectrum sensing algorithm based on featured belief points was proposed. Firstly, this algorithm desployed the single cognitive user energy detection and feature detection collaborative detection mode instead of multiple cognitive user cooperative detection, reducing communication overhead. Secondly, it modeled cognitive radio network under dynamic uncertainty using partially observable Markov decision processes （POMDP）, and transformed the optimization of the tradeoff between sensing performance and sensing efficiency into yielding the optimal value function of POMDP. Finally, a novel approach using characteristics of belief to control the scale of belief states was presented, which exploited the maximum online reward value iteration algorithm to approximate the optimal value. The numerical results show that the proposed algorithm is able to meet the requirement of high tracking performance with constraint of low probability of interfering primary users.