The Cognitive Radio (CR) plays a key role in identifying free bandwidths in the Radio Frequency (RF) spectrum. High-speed Analog-to-Digital Converters are normally applied for spectrum sensing of sideband signals providing several raw data for digital signal processors resulted in energy-inefficient complex circuits and hardware resources in digital signal processing blocks. In some instances, the frequency spectrum is sparsely occupied by various users, i.e., only a few active frequency bands exist at the same time. This feature enables CR application to use sub-Nyquist sampling approaches for designing a system representing significantly reduced cost and power consumption, as well as improved processing speed. The current paper introduced a novel Cooperative Real-valued Sparse Spread Spectrum Sensing algorithm (CR4S) based on a sub-Nyquist sampling approach by employing the sparsity of the frequency spectrum and the real-valued properties of the RF signal to identify free bandwidth with minimum computational complexity. The CR4S algorithm aimed at improving CR spectrum sensing by utilizing techniques such as Real-valued FFT, Sparse Fast Fourier Transform, and collaborative spectrum sensing. The proposed algorithm has been approved by simulation to above 95% detection performance. The performance enhancement in the CR4S algorithm is an emerging advance being fascinating for portable CR devices.

中文翻译：

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基于稀疏实值快速傅立叶变换的认知无线电合作宽带频谱感知

认知无线电（CR）在识别射频（RF）频谱中的空闲带宽方面起着关键作用。高速模数转换器通常用于边带信号的频谱感测，为数字信号处理器提供多个原始数据，从而导致数字信号处理模块中的能源效率低下的复杂电路和硬件资源。在某些情况下，频谱被各种用户稀疏占用，即，同时仅存在几个活动频带。此功能使CR应用程序能够使用次奈奎斯特采样方法来设计系统，从而显着降低了成本和功耗，并提高了处理速度。当前论文介绍了一种基于亚奈奎斯特采样方法的新型协作实值稀疏扩展频谱感知算法（CR4S），它利用频谱的稀疏性和RF信号的实值属性来以最小的频率识别自由带宽计算复杂度。CR4S算法旨在通过利用诸如实值FFT，稀疏快速傅立叶变换和协作频谱检测之类的技术来改善CR频谱检测。该算法已通过仿真验证，检测性能达到95％以上。CR4S算法的性能增强是便携式CR设备引人入胜的新兴进步。