Channel equalization using adaptive complex radial basis function networks
- 1 January 1995
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal on Selected Areas in Communications
- Vol. 13 (1) , 122-131
- https://doi.org/10.1109/49.363139
Abstract
It is generally recognized that digital channel equalization can be interpreted as a problem of nonlinear classification. Networks capable of approximating nonlinear mappings can be quite useful in such applications. The radial basis function network (RBFN) is one such network. We consider an extension of the RBFN for complex-valued signals (the complex RBFN or CRBFN). We also propose a stochastic-gradient (SG) training algorithm that adapts all free parameters of the network. We then consider the problem of equalization of complex nonlinear channels using the CRBFN as part of an equalizer. Results of simulations we have carried out show that the CRBFN with the SG algorithm can be quite effective in channel equalizationKeywords
This publication has 16 references indexed in Scilit:
- Radial basis function networks in nonlinear signal processing applicationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Complex-valued radial basis function network, Part II: Application to digital communications channel equalisationSignal Processing, 1994
- Complex-valued radial basic function network, Part I: Network architecture and learning algorithmsSignal Processing, 1994
- Recursive hybrid algorithm for non-linear system identification using radial basis function networksInternational Journal of Control, 1992
- Universal Approximation Using Radial-Basis-Function NetworksNeural Computation, 1991
- Orthogonal least squares learning algorithm for radial basis function networksIEEE Transactions on Neural Networks, 1991
- The complex backpropagation algorithmIEEE Transactions on Signal Processing, 1991
- Reconstruction of binary signals using an adaptive radial-basis-function equalizerSignal Processing, 1991
- Networks for approximation and learningProceedings of the IEEE, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989