基于线性加权数据融合的协作频谱感知优化

在认知无线电网络中,协作频谱感知技术可有效地缓解本地感知场景中存在的隐藏终端等问题。为了获得更大的协作增益,该文采用基于数据融合的协作频谱感知策略,融合中心依次收集各次用户上报的本地能量检测数据,然后进行线性加权融合,并做出最终判决。重点研究了线性加权融合方案的优化,推导了各次用户分别在Neyman-Pearson(N-P)和Bayesian两种不同准则下的最优融合权重,并在Suzuki感知信道下进行了蒙特卡洛仿真和数值验证。结果表明,N-P准则下给出的两种优化加权融合方案MDC和NDC性能相近,且均比EGC、SC、MRC等常用的融合方案具有更高的协作检测概率;而Bayesian准则下推导的优化加权融合方案BAY在检测可靠性方面明显优于其他方案。

Optimal Cooperative Spectrum Sensing Based on Linear Data Fusion

Cooperative spectrum sensing is regarded as a key technology to tackle the challenges such as hidden terminal problem in local spectrum sensing of cognitive radio networks. In this paper, the cooperation strategy based on data fusion is chosen for better collective sensing performance, in which all cooperative users send their own local results of energy detection to the fusion centre for linear data combination and final decision. As the main focus of this work, the optimization of linear data fusion is investigated. Specifically, the optimal weight vectors for all users are derived under Neyman-Pearson (N-P) and Bayesian criteria, respectively. Monte Carlo simulations and numerical results are given under the assumption that the sensing channels follow Suzuki distribution. Obtained results demonstrate that the two optimal fusion schemes under N-P criterion, MDC and NDC have the similar detection performance, and they both outperform three other generally used schemes, including EGC, SC and MRC. Further, the optimal fusion scheme BAY, which is derived under Bayesian criterion, is verified to be more reliable than other schemes.