基于权值自适应优化的协作频谱认知算法

Cooperative Spectrum sensing Algorithm Based on Weight Adaptive Optimization

提出一种基于权值自适应优化的协作频谱认知算法。根据各协作认知节点的信噪比分配合适的权值向量,反映对检测统计量的贡献大小。基于最小均方误差原则,权值向量可根据实际的各节点信噪比向量进行自适应优化,从而提高认知网络中存在低信噪比节点时的检测性能。仿真结果表明,与传统协作算法相比,该算法无论在节点高信噪比或低信噪比条件下均有更优的检测性能,且收敛速度较快。

Based on weight adaptive optimization, a novel cooperative spectrum-sensing algorithm is proposed. According to the Signal to Noise Ratio（SNR） of each cooperative user, appropriate weight vector is chosen, reflecting each cooperative user＇s contribution to the system detecting statistic. The weight vector has the ability of adaptive optimization on the principle of minimum mean square error, considering the actual SNR of each cooperative user, so that the detecting performance is improved on the condition of the low SNR users existence in cognitive network. Simulation results imply that the proposed algorithm has better detecting performance than traditional algorithm and agreeable convergence speed, on both condition of low SNR and high SNR of cooperative users.