Abstract
The results of linear and nonlinear channel equalisation in data communications are presented, using a recently developed minimal radial basis function neural network structure, referred to as the minimal resource allocation network (MRAN). The MRAN algorithm uses online learning, and has the capability to grow and prune the RBF network's hidden neurons ensuring a parsimonious network structure. Compared to earlier methods, the proposed scheme does not have to estimate the channel order first, and fix the model parameters. Results showing the superior performance of the MRAN algorithm for two linear channels (minimum and non-minimum phase) for 2PAM signalling, and three nonlinear channels for 2PAM and 4QAM signalling, are presented.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEE Proceedings - Vision, Image, and Signal Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
