Sensing the wideband spectrum is an important process for next-generation wireless communication systems. Spectrum sensing primarily aims at detecting unused spectrum holes over wide frequency bands so that secondary users can use them to meet their requirements in terms of quality-of-service. However, this sensing process requires a great deal of time, which is not acceptable for timely communications. In addition, the sensing measurements are often affected by uncertainty. In this paper, we propose an approach based on Bayesian compressive sensing to speed up the process of sensing and to handle uncertainty. This approach takes only a few measurements using a Toeplitz matrix, recovers the wideband signal from a few measurements using Bayesian compressive sensing via fast Laplace prior, and detects either the presence or absence of the primary user using an autocorrelation-based detection method. The proposed approach was implemented using GNU Radio software and Universal Software Radio Peripheral units and was tested on real-world signals. The results show that the proposed approach speeds up the sensing process by minimizing the number of samples while achieving the same performance as Nyquist-based sensing techniques regarding both the probabilities of detection and false alarm.
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机译：感应宽带频谱是下一代无线通信系统的重要过程。频谱感测的主要目的是检测宽频带上未使用的频谱空洞，以便二级用户可以使用它们满足服务质量方面的要求。然而，这种感测过程需要大量时间，这对于及时的通信是不可接受的。另外，感测测量常常受到不确定性的影响。在本文中，我们提出一种基于贝叶斯压缩感测的方法，以加快感测过程并处理不确定性。该方法仅使用Toeplitz矩阵进行几次测量，使用贝叶斯压缩感知通过快速Laplace先验从几次测量中恢复宽带信号，并使用基于自相关的检测方法检测主要用户的存在与否。所提出的方法是使用GNU Radio软件和Universal Software Radio Peripheral单元实现的，并已在实际信号上进行了测试。结果表明，所提出的方法通过最小化样本数量来加快感测过程，同时在检测概率和虚警率方面都达到了与基于奈奎斯特的感测技术相同的性能。
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