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Efficient compressive spectrum sensing algorithm for M2M devices

Zhijin Qin, , Yue Gao, , Plumbley, MD, Parini, CG and Cuthbert, LG (2014) Efficient compressive spectrum sensing algorithm for M2M devices In: Proceedings of the 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014-12-03 - 2014-12-05, Atlanta, Georgia.

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Spectrum used for Machine-to-Machine (M2M) communications should be as cheap as possible or even free in order to connect billions of devices. Recently, both UK and US regulators have conducted trails and pilots to release the UHF TV spectrum for secondary licence-exempt applications. However, it is a very challenging task to implement wideband spectrum sensing in compact and low power M2M devices as high sampling rates are very expensive and difficult to achieve. In recent years, compressive sensing (CS) technique makes fast wideband spectrum sensing possible by taking samples at sub-Nyquist sampling rates. In this paper, we propose a two-step CS based spectrum sensing algorithm. In the first step, the CS is implemented in an SU and only part of the spectrum of interest is supposed to be sensed by an SU in each sensing period to reduce the complexity in the signal recovery process. In the second step, a denoising algorithm is proposed to improve the detection performance of spectrum sensing. The proposed two-step CS based spectrum sensing is compared with the traditional scheme and the theoretical curves.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Zhijin Qin,
Yue Gao,
Plumbley, MD
Parini, CG
Cuthbert, LG
Date : December 2014
DOI : 10.1109/GlobalSIP.2014.7032306
Contributors :
Additional Information : Copyright 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 22 Apr 2015 14:03
Last Modified : 31 Oct 2017 17:25

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