Nonlinear channel equalizer design using directional evolutionary multi-objective optimization

Zong, N. and Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 (2005) Nonlinear channel equalizer design using directional evolutionary multi-objective optimization. International Journal of Systems Science, 36 (12). pp. 737-755. ISSN 0020-7721 doi: 10.1080/00207720500218908

Abstract/Summary

In this paper, a new equalizer learning scheme is introduced based on the algorithm of the directional evolutionary multi-objective optimization (EMOO). Whilst nonlinear channel equalizers such as the radial basis function (RBF) equalizers have been widely studied to combat the linear and nonlinear distortions in the modern communication systems, most of them do not take into account the equalizers' generalization capabilities. In this paper, equalizers are designed aiming at improving their generalization capabilities. It is proposed that this objective can be achieved by treating the equalizer design problem as a multi-objective optimization (MOO) problem, with each objective based on one of several training sets, followed by deriving equalizers with good capabilities of recovering the signals for all the training sets. Conventional EMOO which is widely applied in the MOO problems suffers from disadvantages such as slow convergence speed. Directional EMOO improves the computational efficiency of the conventional EMOO by explicitly making use of the directional information. The new equalizer learning scheme based on the directional EMOO is applied to the RBF equalizer design. Computer simulation demonstrates that the new scheme can be used to derive RBF equalizers with good generalization capabilities, i.e., good performance on predicting the unseen samples.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/15499
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	channel equalization, RBF network, multi-objective optimization, evolutionary algorithms, gradient descent, BLIND EQUALIZATION, DECISION-FEEDBACK, ALGORITHMS
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