Electrical Engineering Department, University of North Dakota, Grand Forks, ND 58202, USA.
Sensors (Basel). 2018 Jun 5;18(6):1839. doi: 10.3390/s18061839.
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.
宽带频谱感知是下一代无线通信系统的重要过程。频谱感知主要旨在检测宽频带上未使用的频谱空洞，以便辅助用户利用这些频谱空洞来满足其服务质量要求。然而，这个感知过程需要耗费大量的时间，这对于实时通信来说是无法接受的。此外，感知测量通常会受到不确定性的影响。在本文中，我们提出了一种基于贝叶斯压缩感知的方法，以加速感知过程并处理不确定性。该方法仅使用 Toeplitz 矩阵进行少量测量，通过快速拉普拉斯先验从少量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用户的存在或不存在。所提出的方法使用 GNU Radio 软件和通用软件无线电外围设备实现，并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速感知过程，同时在检测概率和虚警概率方面与基于奈奎斯特的感知技术具有相同的性能。
