In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). Therefore, accurate and timely spectrum sensing is vital for efficient utilization of available spectrum. The sensing result at each SU is unauthentic due to fading, shadowing, and receiver uncertainty problems. Cooperative spectrum sensing (CSS) provides a solution to these problems. In CSS, false sensing reports at the fusion center (FC) received from malicious users (MUs) drastically degrade the performance of cooperation in PU detection. In this paper, we propose a robust spectrum sensing scheme to minimize the effects of false sensing reports by MUs. The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. The simulation results show that the sensing results are more accurate and reliable for the proposed GA majority-voting hard decision fusion (GAMV-HDF) and GA weighted soft decision fusion (GAW-SDF) compared to conventional equal gain combination soft decision fusion (EGC-SDF), maximum gain combination soft decision fusion (MGC-SDF), and majority-voting hard decision fusion (MV-HDF) schemes in the presence of MUs.

中文翻译：

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认知无线电网络中基于遗传算法的双向邻域距离鲁棒频谱感知

在认知无线电网络（CRN）中，辅助用户（SU）可以访问许可给主要用户（PU）的空闲频谱。因此，准确，及时的频谱感测对于有效利用可用频谱至关重要。由于衰落，阴影和接收器不确定性问题，每个SU处的感测结果都是不真实的。合作频谱感测（CSS）为这些问题提供了解决方案。在CSS中，从恶意用户（MU）接收到的融合中心（FC）的虚假报告严重降低了PU检测合作的性能。在本文中，我们提出了一种鲁棒的频谱感知方案，以最大程度地减少MU的错误感知报告的影响。提出的方案着重于基于遗传算法（GA）的双面邻居距离（DSND），以过滤CSS中的MU感知报告。