There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for wideband spectrum sensing, in this paper, we study the problem of compressed power spectrum estimation whose objective is to reconstruct the power spectrum of a wide-sense stationary signal based on sub-Nyquist samples. By exploring the sampling structure inherent in the multicoset sampling scheme, we develop a computationally efficient method for power spectrum reconstruction. An important advantage of our proposed method over existing compressed power spectrum estimation methods is that our proposed method, whose primary computational task consists of fast Fourier transform (FFT), has a very low computational complexity. Such a merit makes it possible to efficiently implement the proposed algorithm in a practical field-programmable gate array (FPGA)-based system for real-time wideband spectrum sensing. Our proposed method also provides a new perspective on the power spectrum recovery condition, which leads to a result similar to what was reported in prior works. Simulation results are presented to show the computational efficiency and the effectiveness of the proposed method.

中文翻译：

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快速压缩功率谱估计：实现宽带频谱传感的实用解决方案

由于宽带频谱感知在认知无线电和电子监视中的应用，人们对宽带频谱感知越来越感兴趣。为了克服宽带频谱感知的采样率瓶颈，在本文中，我们研究了压缩功率谱估计问题，其目的是重建基于子奈奎斯特样本的广义平稳信号的功率谱。通过探索多陪集采样方案中固有的采样结构，我们开发了一种计算效率高的功率谱重建方法。我们提出的方法相对于现有压缩功率谱估计方法的一个重要优点是，我们提出的方法的主要计算任务包括快速傅立叶变换 (FFT)，具有非常低的计算复杂度。这种优点使得在实际的基于现场可编程门阵列 (FPGA) 的实时宽带频谱感知系统中有效地实现所提出的算法成为可能。我们提出的方法还为功率谱恢复条件提供了一个新的视角，这导致了与先前工作中报告的结果相似的结果。仿真结果表明了该方法的计算效率和有效性。