Particle swarm optimization algorithm based decision feedback equalizer for underwater acoustic communication

In this paper, we proposed particle swarm optimization (PSO) algorithm based adaptive decision feedback equalizer (DFE) for underwater acoustic communication (UWAC). In the literature, although ocean ambient noise is generally modeled as pink Gaussian noise, there is also site-specific ocean noise which can be modeled as pink Laplace noise. In this study we consider both noises. Rayleigh distributed, frequency selective fading channels (as UWAC channel) with Laplacian and Gaussian distributed, pink noise are considered. Unlike recursive least squares (RLS) and least mean squares (LMS) algorithms, PSO is independent from channel characteristics and has faster convergence. To the best of our knowledge PSO algorithm has not been used for adaptive DFE over UWAC channel. The communication performances and computational complexities of LMS, RLS and PSO based adaptive DFEs are compared. Although PSO has the highest computational complexity, our simulation results show PSO-DFE outperforms the other algorithms.