Collaborative compressive spectrum sensing using kronecker sparsifying basis

Abstract

Abstract-Spectrum sensing in wideband cognitive radio networks is challenged by several factors such as hidden primary users, overhead on network resources, and the requirement of high sampling rate. Compressive sensing has been proven effective to elevate some of these problems through efficient sampling and exploiting the underlying sparse structure of the measured frequency spectrum. In this paper, we propose an approach for collaborative compressive spectrum sensing. The proposed approach achieves improved sensing performance through utilizing Kronecker sparsifying bases to exploit the two dimensional sparse structure in the measured spectrum at different, spatially separated cognitive radios. Experimental analysis through simulation shows that the proposed scheme can substantially reduce the mean square error (MSE) of the recovered power spectrum density over conventional schemes while maintaining the use of a low-rate ADC. We also show that we can achieve dramatically lower MSE under low compression ratios using a dense measurement matrix but using Nyquist rate ADC.

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

“…In the case of a soft cooperative scheme, the CS further helps to reduce the cooperative burden as well as the number of cooperative nodes and in the hard cooperative scheme, the CS is more useful for local sensing. Several works exist in the literature in the context of applying CS for cooperative sensing in centralized [64], [68], [71] and distributed [67], [69], [83], [124] settings. In Section III-B1 and Section III-B2, we provide a detailed discussion on the application of CS in centralized and distributed cooperative SS by referring to the current state of the art.…”

“…Subsequently, the distributed JSM-2 model has been used to obtain an estimate of the signal spectrum. Similarly, the correlation between the measurements of different CRs may be utilized by using a Kronecker product matrix as a basis, called Kronecker sparsifying basis [67]. This basis helps to exploit the two dimensional sparse structure in the measured spectrum using the collaborative measurements taken by several spatially separated CRs.…”

“…This basis helps to exploit the two dimensional sparse structure in the measured spectrum using the collaborative measurements taken by several spatially separated CRs. In this context, authors in [67] proposed a modified JSM-2 (JSM2M) model which relaxes the assumptions of the the original JSM-1 and JSM-2 models and generates signals which have a common sparse support in the frequency domain with different amplitudes plus innovations due to the hidden PU problem.…”

Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

“…Undoubtedly, better PSD estimate was achieved exploiting Kronecker matrix as sparsifying basis compared to traditional approaches through substantially reduction of Mean Square Error (MSE) [48]. On the other hand, if sparsity constraints on the power spectrum were relaxed using Least Squares (LSs) and after some rank conditions were hold, blind sampling of power spectrum was pro-Journal of Signal and Information Processing posed in [49].…”

Adaptive spectrum hole detection using Sequential Compressive Sensing

“…A following approach was proposed in [7] and [8], where the wideband analog signal is directly captured by an Analog to Information Converter (AIC), solving the bottleneck in the sampling rate presented in the previous scheme, collaborative distributed spectrum sensing was also considered. A different collaborative approach using Kronecker Compressive Sensing was proposed in [9]. Another interesting approach, which is more relevant to this work, was proposed in [10].…”