Wideband Spectrum Sensing: A Bayesian Compressive Sensing Approach

Abstract

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.

Spectrum sensing: Enhanced energy detection technique based on noise measurement

“…1. Energy detection According to [15], the probabilities of detection @ and false alarm HI are given by: In [19], the authors gives the formula of the detectionthreshold ; , which is obtained from Eq. 7 and given by:…”

“…Taking more samples can enhance the detection performance of these techniques up to a certain value of SNR, after which further increase in the number of samples does not improve their detection performance. Increasing the number of samples can also increase the sensing time and in some cases, for instance, the wideband sensing, it is impractical to increase the number of sample when researcher are demanding to use compressive sensing to minimize the number of samples [15][16]. This detection performance can also be enhanced by using a dynamic threshold adapted to the level of noise present in the received signal.…”

Low Energy Consumption Compressed Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Radio Network

“…Due to the non-coherent property of energy detection, the priori information of a signal does not need to be known. However, these wideband compressed spectrum sensing algorithms still require a large number of wideband filters for perceptual calculation and storage [22][23][24]. Both the configuration complexity and the delay are high, which fail to meet the requirements of fast and accurate detection for wideband signals.…”

Low-Complexity Subspace-Aided Compressive Spectrum Sensing Over Wideband Whitespace

“…Considering the demand of DSA applications, rapid spectrum reconstruction and robust spectrum availability detection are desired in compressive spectrum sensing (CSS). Optimization methods in CSS have been discussed in literature, including convex l 1 optimization [4], [5], non-convex l v (0 < v < 1) optimization [15], and sparse Bayesian learning [16], [17]. However, the complexity of CS recovery algorithms, especially these optimization methods, has been one of the major bottlenecks of the implementation of CSS applications.…”

“…The simple yet commonly adopted method is Neyman-Pearson (NP) energy detection (ED) on each channel's spectrum energy, which determines the channel occupancy in a soft decision manner by setting a proper threshold [24]. Some work in CSS proposes cyclostationary feature extraction on the recovered spectrum and then perform ED [17], [25]. Some prior information on the noise statistics is essential to optimal threshold setting.…”