Cooperative Spectrum Sensing and Localization in Cognitive Radio Systems Using Compressed Sensing

Abstract

We propose to fuse two main enabling features in cognitive radio systems (CRS): spectrum sensing and location awareness in a single compressed sensing based formalism. In this way, we exploit sparse characteristics of primary units to be detected, both in terms of spectrum used and location occupied. The compressed sensing approach also allows to overcome hardware limitations, in terms of the incapacity to acquire measurements and signals at the Nyquist rate when the spectrum to be scanned is large. Simulation results for realistic network topologies and different compressed sensing reconstruction algorithms testify to the performance and the feasibility of the proposed technique to enable in a single formalism the two main features of cognitive sensor networks.

Cooperative compressed sensing for joint terminal localization and spectrum sensing

“…According to the definition of restricted isometry property (RIP), θ is satisfying RIP if we obtain the following equation [5]:…”

“…This procedure is not acceptable because it is a NP-hard problem and numerically impossible [5], [14]. In order to solve this problem, reconstructed signal can be derived using reconstruction algorithms if we use l1 minimization problem.…”

“…In 2000, the FCC published the first notice about proposed rules for cognitive radio [4]. FCC considers a radio as cognitive radio, if includes frequency agility, dynamic frequency assignment, location awareness, negotiated use, adaptive modulation and transmit power control capabilities [5].…”

“…One of these factors is disability of analog-to-digital converter (ADC) for access to signals at Nyquist sampling rate. The second source of sparsity may be sparsity in spectrum usage because in many geographical areas or at some times, the spectrum occupancy is low [5], [10].…”

“…Recently, a set of studies in joint estimating the radio power, sensing the white frequency channels and localization using compressed sensing have been reported [5], [10], [11] and [12]. The main objective of this study is to do this type of CS for joint estimation of three above-mentioned primary user parameters using three reconstruction algorithms namely, orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP) and iterative hard thresholding (IHT), in jointly sparse signal in time and space.…”