Abstract: To deal with the attacks of Spectrum Sensing Data Falsification(SSDF) in cognitive radio network,this paper proposes a Wiener Filter Detection(WFD) algorithm by using the Wiener filter based on the minimum Mean Square Error(MSE) to train the optimal weight and threshold for fusion decision.The algorithm uses the gradient algorithm to train the optimal weight,based on which the training data is weighted and fused,and the average of the fusion results is taken as the threshold.The weight obtained by training and the threshold are used to weight and fuse the data sent by each cognitive user to get the decision results.Simulation results show that compared with the traditional Equal Gain Combination(EGC) algorithm,the error probability of the WFD algorithm can be reduced by more than 20% under the same Signal-to-Noise Ratio(SNR).Also,the WFD algorithm has better robustness,and is less affected by the key parameters of SSDF attacks(including the proportion of malicious users,attack probability and relative attack intensity).

Key words: Cognitive Radio(CR), Spectrum Sensing Data Falsification(SSDF), minimum Mean Square Error(MSE), Wiener filter, Equal Gain Combination(EGC)

摘要： 针对认知无线电网络中随机概率式频谱感知数据篡改（SSDF）的攻击，利用基于最小均方误差建立的维纳滤波器对目标信号进行估计，提出一种维纳滤波器检测（WFD）算法。基于梯度算法训练最优权重，根据权重对训练数据加权融合并对融合结果取平均作为门限，将训练得到的权重和门限与各认知用户发送的数据加权融合得出判决结果。仿真结果表明，与传统的等增益合并算法相比，在相同的信噪比下，WFD算法的错误概率降低20%以上，且受SSDF攻击的恶意用户所占比例、攻击概率和相对攻击强度等关键参数影响较小，具有更好的鲁棒性。

关键词: 认知无线电, 频谱感知数据篡改, 最小均方误差, 维纳滤波器, 等增益合并

CLC Number: 

• TP309