Decision Feedback Equalization using Particle Swarm Optimization

It is well-known that the Decision Feedback Equalizer (DFE) outperforms the Linear Equalizer (LE) for highly dispersive channels. For time-varying channels, adaptive equalizers are commonly designed based on the Least Mean Square (LMS) algorithm which, unfortunately, has the limitation of slow convergence specially in channels having large eigen-value spread. The eigenvalue problem becomes even more pronounced in Multiple-Input Multiple-Output (MIMO) channels. Particle Swarm Optimization (PSO) enjoys fast convergence and, therefore, its application to the DFE merits investigation. In this paper, we show that a PSO-DFE with a variable constriction factor is superior to the LMS/RLS-based DFE (LMS/RLS-DFE) and PSO-based LE (PSO-LE), especially on channels with large eigenvalue spread. We also propose a hybrid PSO-LMS-DFE algorithm, and modify it to deal with complex-valued data. The PSO-LMS-DFE not only outperforms the PSO-DFE in terms of performance but its complexity is also low. To further reduce its complexity, a fast PSO-LMS-DFE algorithm is introduced. (C) 2014 Elsevier B.V. All rights reserved.