Energy detection is of crucial importance in cognitive radio networks. However, its performance is poor when the channel fading is severe, which causes interference to the primary users. In order to tackle this issue, an intelligent reflecting surface (IRS)-enhanced energy detection for spectrum sensing is proposed. Both the cases with and without the direct link between the primary user and the secondary user are considered. By using the Gamma distribution approximation and central limit theorem, the closed-form expressions for the average probability of detection are derived. In order to further improve the detection performance, IRS-enhanced energy detection for cooperative spectrum sensing and multiple IRSs-enhanced square-law selection diversity reception are also proposed. Expressions for the average probability of detection for these two schemes are provided by using the ${K}$ -rank fushion criterion and square-law selection, respectively. Simulation results verify our theoretical analysis and demonstrate the superiority of our proposed IRS-enhanced energy detection compared with the benchmark schemes in terms of the spectrum sensing performance.

中文翻译：

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用于认知无线电网络中频谱感知的 IRS 增强型能量检测

能量检测在认知无线电网络中至关重要。但在信道衰落严重时性能较差，对主用户造成干扰。为了解决这个问题，提出了一种用于频谱感知的智能反射面（IRS）增强能量检测。考虑了主要用户和次要用户之间有和没有直接链接的情况。利用伽马分布近似和中心极限定理，推导出平均检测概率的闭式表达式。为了进一步提高检测性能，还提出了用于协作频谱感知的IRS增强能量检测和多个IRS增强平方律选择分集接收。 ${K}$ -秩融合准则和平方律选择，分别。仿真结果验证了我们的理论分析，并证明了我们提出的 IRS 增强能量检测在频谱感知性能方面与基准方案相比的优越性。