基于马尔科夫模型的认知无线电动态双门限能量检测策略

该文针对低信噪比条件下频谱感知精度低的问题，提出一种基于马尔科夫模型的动态双门限能量检测算法。该算法根据信道时变特性建立基于马尔科夫的频谱占用模型，利用信道历史状态信息实现模型参数的修正。然后采用先听后说的机制对处于双门限之间的困惑信道状态进行判决，并详细分析了噪声不确定性对频谱感知性能的影响。在此基础上，为了克服噪声不确定性的影响，以频谱检测概率最大为优化目标，对双门限进行实时更新。仿真结果表明，所提频谱感知算法在减小噪声不确定性影响的同时增加了频谱感知精度，降低了认知用户的感知时间。

Dynamic Double Threshold Energy Detection Based on Markov Model in Cognitive Radio

With the development of the technology of cognitive radio, the standards of spectrum sensing performance become the higher and the higher, especially in low Signal-to-Noise Ratio (SNR) environments. A Dynamic Double-threshold Energy sensing method based on Markov Model (DDEMM) is proposed in this paper. By following the double-threshold energy sensing approach, the modified Markov model that accounts for the time varying nature of the channel occupancy is presented to resolve the confused channel state. Furthermore, in order to overcome the effect of noise uncertainty, a dynamic double-threshold spectrum sensing method is proposed, which adjusts its thresholds according to the achievable maximal detection probability. The results of extensive simulation demonstrate that the proposed DDEMM can achieve better detection performance than the conventional double-threshold energy sensing schemes, especially under very low SNR region.