LAT模型中基于次用户权值优化的频谱感知

全双工认知无线电LAT(Listen-and-Talk)模型因自干扰消除不完全而使得频谱感知能力下降.为提升频谱感知性能,提出了LAT模型下基于次用户加权的协作式频谱感知方法,分别研究了决策融合和数据融合下次用户最优权值的算法.求解最优权值时利用了凸优化方法,在处理非凸问题时利用了序列规划的方法,得到了较好的优化结果.仿真实验表明,在加权融合机制下的LAT模型得到了趋近于传统LBT(Listen-before-Talk)模型的频谱感知能力.

Spectrum Sensing for Listen-and-Talk( LAT) Protocol Based on SUs' Weights Optimized

The full duplex cognitive radio Listen-and-Talk(LAT) protocol performs unsatisfactory spec-trum sensing ability due to the imperfect self-interference cancellation. To improve spectrum sensing abili-ty,a cooperative spectrum sensing method based on secondary users'( SUs') weights optimized is proposed, and the decision fusion and data fusion are discussed respectively. Convex optimization is used to solve the weights. When dealing with non convex problems, the method of Sequential Cone Programming algorithm is proved efficient. Simulation results show that the spectrum sensing ability of LAT protocol with fusion optimized nearly obtains the sensing ability of Listen-before-Talk( LBT) protocol.