Robust nonlinear channel equalization using WNN trained by symbiotic organism search algorithm

• Published in: Applied soft computing Vol. 57; pp. 197 - 209
• Main Authors: Nanda, Satyasai Jagannath, Jonwal, Nidhi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2017
• Subjects: Burst error; Channel equalization; Symbiotic organism search algorithm; Wavelet neural network; Burst error; Symbiotic organism search algorithm; Wavelet neural network; Channel equalization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.03.029

The fundamental process of symbiosis in an ecosystem. [Display omitted] •A systematic review on all neural network and evolutionary equalizers.•Developed a new equalizer based on WNN trained by SOS algorithm.•Superior performance over RBF, FLANN and FIR equalizers trained by CSO, CLONAL, PSO and LMS.•Superior performance on 21 tap telephone channel.•The robust performance of proposed equalizer under burst error conditions. In the present world of ‘Big Data,’ the communication channels are always remaining busy and overloaded to transfer quintillion bytes of information. To design an effective equalizer to prevent the inter-symbol interference in such scenario is a challenging task. In this paper, we develop equalizers based on a nonlinear neural structure (wavelet neural network (WNN)) and train it's weighted by a recently developed meta-heuristic (symbiotic organisms search algorithm). The performance of the proposed equalizer is compared with WNN trained by cat swarm optimization (CSO) and clonal selection algorithm (CLONAL), particle swarm optimization (PSO) and least mean square algorithm (LMS). The performance is also compared with other equalizers with structure based on functional link artificial neural network (trigonometric FLANN), radial basis function network (RBF) and finite impulse response filter (FIR). The superior performance is demonstrated on equalization of two non-linear three taps channels and a linear twenty-three taps telephonic channel. It is observed that the performance of the gradient algorithm based equalizers fails in the presence of burst error. The robustness in the performance of the proposed equalizers to handle the burst error conditions is also demonstrated.