基于快速独立成分分析的高效合作频谱感知方法

High efficient cooperative spectrum sensing method based on fast independent component analysis

针对能量检测无法区分主用户与次用户的局限性,将基于快速独立成分分析的盲源分离算法引入频谱感知以分离主次用户的混合信号。在此基础上,提出一种基于近似负熵的判决准则对分离后的信号进行检测。针对单节点频谱感知的不可靠性,将以上算法拓展到加权合作感知,同时提出一种选择合作感知算法以降低感知数据传输开销和检测延时。仿真结果表明:所提算法能够可靠地检测主用户并能有效地提高频谱利用率。

To overcome the limitations of energy detection which cannot differentiate primary user （ PU ） from secondary user（ SU ）, blind source separation algorithm based on fast independent component analysis （ICA）is introduced to separate the mixed signals of PU and SU. A decision rule based on approximation of negentropy is proposed to perform detection after separation. Then a weighted cooperative sensing method based on fast ICA is proposed to combat the unreliability of single node sensing. Finally, a selective cooperative sensing algorithm is also proposed to reduce the transmission overhead and detecting delay. Simulations results show that the proposed algorithm can realize reliable detection of PU and increase the spectrum efficiency.