LPWS Algorithm for Wideband Spectrum Sensing

Abstract

Wideband or multi-band spectrum sensing is an essential functionality for cognitive radio (CR) networks. It enables a secondary user to identify spectral holes dynamically and transmit opportunistically so as not to interfere with cohabiting primary users over the same bands. This paper presents an effective wideband spectrum sensing method based on linear programming (named as LPWS algorithm) for a CR user equipped with a single receiving antenna. Firstly, the proposed method utilizes the temporal smoothing technique to form a virtual multi-antenna structure. Secondly, the wideband spectrum sensing problem is reformulated as a sparse reconstruction problem by exploiting a sparse representation of the virtual multi-antenna array covariance vector. Finally, making use of $$l_{\infty }$$lź-norm, the sparse reconstruction problem is modelled as a linear programming (LP) problem and hence can be solved efficiently. The presented method offers a number of advantages over other recently proposed methods. For examples, (1) it can reduce system overhead since single antenna is used instead of multiple antennas or sensing nodes. (2) The unknown noise variances can be eliminated effectively by a linear transformation. (3) It is computationally simpler since it is efficiently formulated in terms of the LP problem based on real-valued computation, etc. Simulation results are presented to verify the efficiency of the proposed method.