Compressed Wideband Spectrum Sensing Based on Discrete Cosine Transform

Yulin Wang,Gengxin Zhang

doi:10.1155/2014/464895

Yulin Wang, Gengxin Zhang

Open Access

https://doi.org/10.1155/2014/464895

Copy DOI

Journal: TheScientificWorldJournal	Publication Date: Jan 1, 2014
Citations: 20	License type: CC BY 3.0

Affiliation: PLA Army Engineering University

Abstract

Discrete cosine transform (DCT) is a special type of transform which is widely used for compression of speech and image. However, its use for spectrum sensing has not yet received widespread attention. This paper aims to alleviate the sampling requirements of wideband spectrum sensing by utilizing the compressive sampling (CS) principle and exploiting the unique sparsity structure in the DCT domain. Compared with discrete Fourier transform (DFT), wideband communication signal has much sparser representation and easier implementation in DCT domain. Simulation result shows that the proposed DCT-CSS scheme outperforms the conventional DFT-CSS scheme in terms of MSE of reconstruction signal, detection probability, and computational complexity.

Highlights

In cognitive radio networks (CRNs), secondary CR users should fleetly and accurately sense the wideband spectrum, so that they can detect the unused spectrum holes, reconfigure their parameters to utilize the spectrum available, and avoid interference to primary users (PUs) [1, 2]
In the literature, [4] firstly applied compressed sampling (CS) for acquiring wideband signals using sub-Nyquist sampling rates, [5] exploited a structured compressed sensing, and [6] studied a power spectrum blind sampling (PSBS) algorithm trying to reconstruct the power spectrum. All of these systems belonged to the class of discrete Fourier transform (DFT) based compressed spectrum acquisition, which employed the complex exponential functions set as orthogonal sparse basis
As discussed in the preceding section, the signal response is sparse in Discrete cosine transform (DCT) domain, so the DCT-compressed spectrum sensing (CSS) problem can be solved with a three-step scheme: (1) use compressed measurements y to estimate the sparse sequence rd, (2) reconstruct signal rt according to rd, which can be done by an inverse DCT transfer, and (3) get frequency response rf from rt via a fast Fourier transform (FFT)

Summary

Introduction

In cognitive radio networks (CRNs), secondary CR users should fleetly and accurately sense the wideband spectrum, so that they can detect the unused spectrum holes, reconfigure their parameters to utilize the spectrum available, and avoid interference to primary users (PUs) [1, 2]. In the literature, [4] firstly applied CS for acquiring wideband signals using sub-Nyquist sampling rates, [5] exploited a structured compressed sensing, and [6] studied a power spectrum blind sampling (PSBS) algorithm trying to reconstruct the power spectrum. All of these systems belonged to the class of discrete Fourier transform (DFT) based compressed spectrum acquisition, which employed the complex exponential functions set as orthogonal sparse basis. Our main contribution is to reconstruct a wideband spectrum signal from sub-Nyquistrate compressive samples by DCT-CSS.

System Model

Comparison between DCT and DFT

DCT Based Compressed Spectrum Sensing

Simulation

Conclusion