Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter

TL;DR

This paper explores the use of an evolutionary algorithm-based adaptive filter, specifically particle swarm optimization, to improve noise denoising in cognitive radio systems, outperforming traditional LMS methods in various noisy environments.

Contribution

The study introduces a novel application of particle swarm optimization for noise cancellation in cognitive radios, demonstrating superior performance over LMS algorithms.

Findings

PSO outperforms LMS in noise reduction

Enhanced denoising in non-linear and Gaussian noise environments

Improved bit error rate and mean square error metrics

Abstract

Noise originating from several sources in a RF environment degrades the performance of communication systems. In wideband systems, such as cognitive radios, noise at the receiver can originate from non-linearity present in the RF front end, time-varying thermal noise within the receiver radio system, and noise from adjacent network nodes. Several denoising techniques have been proposed for cognitive radios, some of which are applied during spectrum sensing and others to received noisy signal during communication. Examples of some of these techniques used for noise cancellation in received signals are least mean square (LMS) and its variants. However, these algorithms have low performance with non-linear signals and cannot locate a global optimum solution for noise cancellation. Therefore, application of global search optimization techniques, such as evolutionary algorithms, is considered for noise cancellation. In this paper, particle swarm optimization (PSO) and LMS algorithms are implemented and their performances are evaluated. Extensive simulations were performed where Gaussian and non-linear random noise were added to the transmitted signal. The performance comparison was done using two metrics: bit error rate and mean square error. The results show that PSO outperforms LMS under both Gaussian and nonlinear random noise.