arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2202.02550cs.ITcs.ETeess.SPmath.IT
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智能反射面辅助认知无线电频谱感知

Intelligent Reflecting Surface-Aided Spectrum Sensing for Cognitive Radio

Shaoe Lin, Beixiong Zheng, Fangjiong Chen, Rui Zhang

摘要:

频谱感知是认知无线电的关键使能技术,它提供有关频谱可用性的重要信息。然而,由于严重的无线信道衰落和路径损耗,在CR或次要用户(SU)处接收的主要用户(PU)信号实际上可能太弱而不能进行可靠检测。为了解决这个问题,我们认为在这封信中一个新的智能反射表面(IRS)辅助频谱感知方案CR,通过利用大孔径和被动波束形成增益的IRS提高PU信号强度接收在SU,以促进其频谱感知。具体地,通过根据给定的码本随时间动态地改变IRS反射,其反射信号功率在SU处基本上变化,这被用于机会信号检测。此外,我们提出了一种加权的能量检测方法,通过结合不同的IRS反射的接收信号的功率值,这显着提高了检测性能。仿真结果验证了所提出的IRS辅助频谱感知方案的性能增益,与不同的基准方案相比。

英文摘要:

Spectrum sensing is a key enabling technique for cognitive radio (CR), which provides essential information on the spectrum availability. However, due to severe wireless channel fading and path loss, the primary user (PU) signals received at the CR or secondary user (SU) can be practically too weak for reliable detection. To tackle this issue, we consider in this letter a new intelligent reflecting surface (IRS)-aided spectrum sensing scheme for CR, by exploiting the large aperture and passive beamforming gains of IRS to boost the PU signal strength received at the SU to facilitate its spectrum sensing. Specifically, by dynamically changing the IRS reflection over time according to a given codebook, its reflected signal power varies substantially at the SU, which is utilized for opportunistic signal detection. Furthermore, we propose a weighted energy detection method by combining the received signal power values over different IRS reflections, which significantly improves the detection performance. Simulation results validate the performance gain of the proposed IRS-aided spectrum sensing scheme, as compared to different benchmark schemes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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