基于迭代攻击检测的联合压缩频谱感知算法

Collaborative Compressive Spectrum Sensing Using Iterative Attack Detection

针对宽带认知无线电网络中频谱感知所需采样率过高以及频谱感知性能容易受到模仿主用户攻击威胁的问题,提出了基于迭代攻击检测的联合压缩频谱感知算法。认知无线电用户利用压缩感知理论降低信号的采样率,并将感知结果报告给融合中心。融合中心利用来自各认知无线电用户的感知报告估计主用户发射信号功率和路径损失参数,根据估计误差得到测量剩余值。在迭代攻击检测中,通过比较测量剩余值去除受到模仿主用户攻击影响的异常感知报告,得到可靠的联合频谱感知结果。仿真表明,该算法能够有效抵抗模仿主用户攻击,并且能够以低于奈奎斯特准则的采样率实现准确可靠的频谱感知。

To overcome the challenges encountered by spectrum sensing in wideband cognitive radio( CR) networks,such as high sampling rates and performance degradation caused by primary user emulation attack( PUEA),a collaborative spectrum sensing algorithm was proposed. CRs performed compressive sensing to reduce the sampling rates,and sent the sensing reports to the fusion center,where primary user's transmit power and pathloss exponent were estimated. Measurement residual was utilized in iterative attack detection to detect sensing reports compromised by PUEA,and reliable collaborative spectrum sensing was achieved by removing the abnormal reports. Simulations showed that the proposed algorithm is robust to PUEA,and can achieve reliable spectrum sensing at sub-Nyquist sampling rates.