  Noise cancellation is one of the important signal processing functions of any
communication system, as noise affects data integrity. In existing systems,
traditional filters are used to cancel the noise from the received signals.
These filters use fixed hardware which is capable of filtering specific
frequency or a range of frequencies. However, next generation communication
technologies, such as cognitive radio, will require the use of adaptive filters
that can dynamically reconfigure their filtering parameters for any frequency.
To this end, a few noise cancellation techniques have been proposed, including
least mean squares (LMS) and its variants. However, these algorithms are
susceptible to non-linear noise and fail to locate the global optimum solution
for de-noising. In this paper, we investigate the efficiency of two global
search optimization based algorithms, genetic algorithm and particle swarm
optimization in performing noise cancellation in cognitive radio systems. These
algorithms are implemented and their performances are compared to that of LMS
using bit error rate and mean square error as performance evaluation metrics.
Simulations are performed with additive white Gaussian noise and random
nonlinear noise. Results indicate that GA and PSO perform better than LMS for
the case of AWGN corrupted signal but for non-linear random noise PSO
outperforms the other two algorithms.
