Distributed diffusion LMS based energy detection

Abstract

Cognitive radio (CR) is seen as a promising technology to make radio spectrum usage more effective by providing an opportunistic access for secondary users to the licensed spectrum areas. CR systems need to detect the presence of a primary user (PU) signal by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. This paper studies distributed spectrum sensing in a cognitive radio context. We investigate distributed energy detection schemes without using any fusion center. We propose the usage of distributed, diffusion least mean square (LMS) type of power estimation algorithms. In this paper an Adapt and Combine (ATC) diffusion based power estimation scheme is proposed and the performance is compared with the Combine and Adapt (CTA) and ring-around schemes in a common framework. The PU signal is assumed to be slowly fading. We analyse the resulting energy detection performance and verify the theoretical findings through simulations.

Diffusion Maximum Correntropy Criterion Based Robust Spectrum Sensing in Non-Gaussian Noise Environments

“…It is necessary to note that the condition of (35) is similar to those in [ 17 , 18 ]; the only difference is the extra term , which is the expectation of the error nonlinearity introduced by MCC.…”

“…Because of the central processing unit, high communication overhead and enormous computing power are needed [ 13 ]. To solve these problems, decentralized cooperative sensing methods have been proposed, such as consensus algorithms [ 14 , 15 ], belief propagation [ 16 ] and diffusion methods [ 17 , 18 , 19 ].…”

“…Some recent literature [ 17 , 18 , 19 ] has proposed distributed diffusion methods (diffusion least mean square (DLMS)), which allow each CU to collect data from its neighbors and make detection decisions based on these data without any central processing unit. As it is a distributed and adaptive diffusion scheme, DLMS has an ability to enhance network failure resistance.…”

“…The correntropy, as a nonlinear similarity measure in information theoretic learning (ITL), has been successfully used in non-Gaussian noise for its robust and cost-efficient function [ 28 , 29 , 30 ]. The new distributed robust spectrum sensing is motivated by the desirable features of correntropy and the diffusion model schemes [ 17 , 18 ]. The main contributions of this paper are three-fold: A DMCC-based robust spectrum sensing scheme is presented to solve the distributed power estimation with non-Gaussian noise; a version of DMCC-based robust spectrum sensing, namely adaptation to combination DMCC (ATC DMCC) algorithm, is derived, which can solve the non-Gaussian noise problem in spectrum sensing; the mean and mean square performance of the new power estimation algorithm have been analyzed.…”

Implementation Issues of Adaptive Energy Detection in Heterogeneous Wireless Networks

“…Whenever possible, cooperative strategies are employed to increase the detection reliability in either centralized or distributed manners [ 17 , 18 ]. In this context, distributed least-mean-squares (LMS)-based algorithms have been widely employed for adaptive ED purposes [ 19 , 20 , 21 , 22 , 23 , 24 ]. Commonplace among those skeptical researchers and practitioner engineers is the fact that most of the works in this area are analyzed from theoretical and simulation viewpoints solely, thus relying on several assumptions and simplifications which, eventually, lead to conclusions that do not necessarily meet the requirements imposed by real propagation environments.…”