Efficient blind spectrum sensing for cognitive radio networks based on compressed sensing

نویسندگان

  • Shancang Li
  • Xinheng Wang
  • Xu Zhou
چکیده

Spectrum sensing is a key technique in cognitive radio networks (CRNs), which enables cognitive radio nodes to detect the unused spectrum holes for dynamic spectrum access. In practice, only a small part of spectrum is occupied by the primary users. Too high sampling rate can cause immense computational costs and sensing problem. Based on sparse representation of signals in the frequency domain, it is possible to exploit compressed sensing to transfer the sampling burden to the digital signal processor. In this article, an effective spectrum sensing approach is proposed for CRNs, which enables cognitive radio nodes to sense the blind spectrum at a sub-Nyquist rate. Perfect reconstruction from fewer samples is achieved by a blind signal reconstruction algorithm which exploits p-norm (0 < p < 1) minimization instead of 1 or 1/ 2 mixed minimization that are commonly used in existing signal recovery schemes. Simulation results demonstrated that the p-norm spectrum reconstruction scheme can be used to break through the bandwidth barrier of existing sampling schemes in CRNs. Introduction In cognitive radio networks (CRNs), spectrum sensing aims to identify the frequency support of a signal, which consists of spectrum intervals that the power of the signal exceeds that of noise [1]. Recently, many researchers have focused their attentions on spectrum sensing in CRNs, in which the cognitive radio (CR) nodes are able to perform the wideband spectrum sensing to detect the unoccupied frequency bands for temporal using. As a very promising technology in CRNs [2,3], the compressed sensing theory can be used to alleviate the dynamic spectrum sensing problem by blindly detecting the spectrum holes [3-5]. The basic idea behind compressed sensing (CS in short) is to sample compressible signals at a lower rate than the traditional Nyquist, and then reconstruct these signals with compressed measurements [3]. In CS, the sampling and compression operations are combined into a low complexity compressed sampling [4], in which compressible signals can accurately be reconstructed from a set of random linear measurements by using nonlinear or convex reconstruction algorithms [6,7]. Typically, *Correspondence: [email protected] 1Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi’an Jiaotong University, Xi’an 710049, China 2College of Engineering, Swansea University, Swansea, SA2 8PP, UK Full list of author information is available at the end of the article the number of measurements in CS is much fewer than that in Nyquist sampling, thus leading to a significant reduction in sampling rates. Therefore, the requirements to analog-to-digital converter resource can be reduced significantly, which is of great importance for wideband communication systems [4]. Previously, a lot of CS-based techniques have been proposed [3,4]. The new CS theory is hoped to significantly reduce the sampling rate and computational costs at a CR node for compressible signals [8]. A compressible signal means that it can sparsely be represented in some basis, and can exactly be reconstructed only with a small set of random projections on an incoherent basis [8-10]. Recently, many research efforts have been done on random projection. The authors of [11] proposed a collaborative compressed spectrum sensing, where the compressed spectrum reconstruction is modeled with a gaussian process framework model. The authors of [12] investigate the problem of dynamic resource allocation in CRNs, where several CS-based techniques are used to detect occupied spectral bands from compressed measurements. For current CRNs, the CS has been used to alleviate the sampling bottleneck, which aims at decreasing the sampling rates for the acquisition of compressible signals [13,14]. © 2012 Li et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Li et al. EURASIP Journal onWireless Communications and Networking 2012, 2012:306 Page 2 of 10 http://jwcn.eurasipjournals.com/content/2012/1/306 The CS techniques that have been used in spectrum sensing can be classified into two categories: (1) Convex relaxing-based methods, such as basis pursuit (BP) [15,16] and Dantzig Selector [17]; (2) Greedy algorithmbased methods, such as matched pursuit (MP) and its variants [18]. Recently, many improved MP-based methods have been reported such as orthogonal matched pursuit [19], regularized orthogonal matched pursuit [20], compressive sampling matching pursuit [21], and so forth. Actually, the former or its variants can get higher reconstruction accuracy, however it may cause expensive computation costs. The greedy algorithm-based methods have less computing complex, however the reconstruction accuracy is limited compared with the convex programming. The basis pursuit denoising is commonly used in signal processing due to its additional denoising performance advantage [16,22]. The advantages of the greedy algorithm-based approaches are fast, stable, uniform guarantees, however it requires a slightly stronger condition on the restricted isometry property (RIP) condition than first category [23]. The spectrum sensing in CRNs faces three main technical challenges: (1) The sampling rate, too high sampling rate may cause very high cost of signal processing and storage; (2) The design of radio front-end is very difficult, the computation intensive energy or feature detection operations are applied in many existing spectrum sensing methods. However, by using CS-based approach, the spectrum detection can be simplified; and (3) highspeed DSP that operates at or above the Nyquist rate is used in conventional spectrum estimation, which may cause failure of exactly signals reconstruction because of the high requirement on spectrum sensing timing windows [3]. In this article, we aim at developing an effective CSbased spectrum sensing approach at affordable complexity. First, we take a multi-coset scheme to decompose the spectrum in CRNs. One of the goals of each CR is to effectively detect the unused spectrum holes while the spectrum sparsity is known a priori for the dynamic spectrum access of CRs. The cognitive spectrum sensing is decomposed into two stages: spectrum sensing and spectrum reconstruction. In spectrum sensing, the sensing time to find the spectrum holes is critical for the ‘cognition’ of the CRs. On the other hand, the spectrum recovery requires better anti-noise performance. In order to cope with these challenges, we focus our works on the following issues: 1. A CS-based spectrum sensing scheme is proposed which can adaptively sense the blind occupied bands with a sampling rate lower than that of Nyquist; 2. In the spectrum reconstruction, we proposed an improved block sparse signal model, in which an approximate p-norm (0 < p < 1) minimization is used to improve the reconstruction quality and speed spectrum. 3. To further enhance the performance and reconstruction speed, an iterative weighted scheme is proposed to approximate the p-norm optimization problem, by doing this the convergence speed can be enhanced in reconstruction. Notation: For vectors/matrices the superscript, T, denotes transpose. Al,k represents the (l, k)th element of a matrixA. ‖x‖ denotes the 2-norm of vector x. In general, ‖x‖p denotes the p-norm of x that is defined as ‖x‖p = ( ∑N i=1 |xi|p)1/p. The common notations that summarized in Table 1 is used in this article.

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عنوان ژورنال:
  • EURASIP J. Wireless Comm. and Networking

دوره 2012  شماره 

صفحات  -

تاریخ انتشار 2012