Fast Fading Channel Neural Equalization Using Levenberg-Marquardt Training Algorithm and Pulse Shaping Filters

• 作者: Ant?nio Lima Jorgean Leal Tiago Mota
• 来源期刊: 通讯、网络与系统学国际期刊(英文)
• 学科: 工学
• 关键词: Decision Feedback EQUALIZERS LEVENBERG-MARQUARDT Algorithm Pulse SHAPING RECURRENT NEURAL Networks
• 年，卷（期）: 2014,（2）
• 页码范围: 71-74
• 页数: 4页
• 分类号: TP39

摘要

Artificial Neural Network (ANN) equalizers have been successfully applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced by linear or nonlinear communication channels. The ANN architecture is chosen according to the type of ISI produced by fixed, fast or slow fading channels. In this work, we propose a combination of two techniques in order to minimize ISI yield by fast fading channels, i.e., pulse shape filtering and ANN equalizer. Levenberg-Marquardt algorithm is used to update the synaptic weights of an ANN comprise only by two recurrent perceptrons. The proposed system outperformed more complex structures such as those based on Kalman filtering approach.