A survey on compressive sensing techniques for cognitive radio networks

Abstract

In cognitive radio, one of the main challenges is wideband spectrum sensing. Existing spectrum 4 sensing techniques are based on a set of observations sampled by an analog/digital converter (ADC) at the 5 Nyquist rate. However, those techniques can sense only one band at a time because of the hardware limitations 6 on sampling rate. In addition, in order to sense a wideband spectrum, the band is divided into narrow bands or 7 multiple frequency bands. Secondary users (SU) have to sense each band using multiple RF frontends 8 simultaneously, which results in a very high processing time, hardware cost, and computational complexity. In 9 order to overcome this problem, the signal sampling should be as fast as possible, even with high dimensional 10 signals. Compressive sensing has been proposed as one of the solutions to reduce the processing time and 11 accelerate the scanning process. It allows reducing the number of samples required for high dimensional signal 12 acquisition while keeping the important information. Over the last decade, a number of papers related to 13 compressive sensing techniques have been published. However, most of these papers describe techniques 14 corresponding to one process either sparse representation, sensing matrix, or recovery. This paper provides an in 15 depth survey on compressive sensing techniques and classifies these techniques according to which process they 16 target, namely, sparse representation, sensing matrix, or recovery algorithms.It also discusses examples of 17 potential applicationsof these techniques including in spectrum sensing, channel estimation, and multiple-input 18 multiple-output (MIMO) based cognitive radio.Metrics to evaluate the efficiencies of existing compressive 19 sensing techniques are providedas well as the benefits and challenges in the context of cognitive radio networks.20 KeywordsCognitive radio network, spectrum sensing, compressive sensing, sparsity representation, sensing 21 matrix, processing time, recovery algorithms, restrict isometry property, analog to digital converter, Shannon-22 Nyquist theorem, channel estimation, MIMO. 23 1 Introduction 24 Over the last decade, a number of spectrum sensing techniques have been proposed to detect and locate 25 dynamically unused spectrum channels in a band of interest [1-6]. Examples of these techniques include energy 26 detection [7], matched filter [8], autocorrelation [9-11],and wavelet based detection [12].Energy detection 27operates by comparing the SU signal average energy with an estimated threshold. This technique does not 2 require any knowledge of the PU signal and itis simple and easy to implement. However,it has high false 29 detection ratesand it is not able to distinguish between signals and noise [7]. Matched filter requires the 30 knowledge of the PU signal characteristics including frequency, modulation type, and bandwidth. It operates by 31 comparing the matched filter output with a threshold [8].Autocorrelation based detection requires the knowledge 32 of the statistical distribut...

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