Denoising signals in cognitive radio systems using an evolutionary algorithm based adaptive filter

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 also 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 signals. Examples of these techniques include least mean square (LMS) and its variants. However, these algorithms have low performance with non-linear signals and cannot locate the 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 compared. Extensive simulations were performed where Gaussian and nonlinear random noise were added to the transmitted signals. 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 non-linear random noise.