Sparsity Order Estimation and its Application in Compressive Spectrum Sensing for Cognitive Radios

Abstract

Abstract-Compressive sampling techniques can effectively reduce the acquisition costs of high-dimensional signals by utilizing the fact that typical signals of interest are often sparse in a certain domain. For compressive samplers, the number of samples Mr needed to reconstruct a sparse signal is determined by the actual sparsity order Snz of the signal, which can be much smaller than the signal dimension N . However, Snz is often unknown or dynamically varying in practice, and the practical sampling rate has to be chosen conservatively according to an upper bound Smax of the actual sparsity order in lieu of Snz, which can be unnecessarily high. To circumvent such wastage of the sampling resources, this paper introduces the concept of sparsity order estimation, which aims to accurately acquire Snz prior to sparse signal recovery, by using a very small number of samples Me less than Mr. A statistical learning methodology is used to quantify the gap between Mr and Me in a closed form via data fitting, which offers useful design guideline for compressive samplers. It is shown that Me ≥ 1.2Snz log(N/Snz + 2) + 3 for a broad range of sampling matrices. Capitalizing on this gap, this paper also develops a two-step compressive spectrum sensing algorithm for wideband cognitive radios as an illustrative application. The first step quickly estimates the actual sparsity order of the wide spectrum of interest using a small number of samples, and the second step adjusts the total number of collected samples according to the estimated signal sparsity order. By doing so, the overall sampling cost can be minimized adaptively, without degrading the sensing performance.

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

“…Techniques under this type include wavelet detection [71,72,73,74,75,76], multi-band joint detection [77,78], and filter bank based sensing [79,80,81,82]. Compressive sensing techniques sample signals below the Nyquist rate to reduce the high sampling rate [83,84,85,86,87,88,89]. Examples of these techniques include analog-to-information converter-based (AIC), two-step sensing [83,87], adaptive CS [88], and geo-location based CS [89].…”

“…Compressive sensing techniques sample signals below the Nyquist rate to reduce the high sampling rate [83,84,85,86,87,88,89]. Examples of these techniques include analog-to-information converter-based (AIC), two-step sensing [83,87], adaptive CS [88], and geo-location based CS [89]. These techniques can be classified into several types based on specific criteria.…”

“…Several techniques have been proposed to determine these parameters [83,84,85,86,87]. For instance, in [83,87], the authors proposed a concept for estimating the wideband signal sparsity order.…”

“…Several techniques have been proposed to determine these parameters [83,84,85,86,87]. For instance, in [83,87], the authors proposed a concept for estimating the wideband signal sparsity order. This technique uses a small number of compressed samples, Me, for which the signal is recovered.…”

Adaptive Adjustment of Compressed Measurements for Wideband Spectrum Sensing

“…In [29], an eigenvalue-based compressive SOE technique was proposed in terms of asymptotic random matrix theory. In addition, A SNR-based sparsity estimation method was introduced to detect the sparsity level of the channel in [30]. Sparsity estimation for different kinds of random matrix has been discussed in [31]- [32].…”