Hybrid structured neural network receiver in digital communication systems

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

In order to reduce the complexity of a radial basis function (RBF) network as a multiuser demodulator and an equalizer, we propose a simplified hybrid neural network architecture. The proposed neural network, which is called RN, has the structure of combining a radial basis function network with multilayer perceptrons (MLPs). The RBF network yield the linear combining output of the hidden layer while the proposed hybrid neural network produces the output using nonlinear combining techniques. From computer simulation results, the RN with the reduced structure from about 50% to about 70% over the RBF network shows better than or almost equal performance to the RBF network as a multiuser demodulator and an equalizer.

Original languageEnglish
Pages378-383
Number of pages6
Publication statusPublished - 2000 Jan 1
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: 2000 Jul 242000 Jul 27

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy
Period00/7/2400/7/27

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Choi, S., & Hong, D. (2000). Hybrid structured neural network receiver in digital communication systems. 378-383. Paper presented at International Joint Conference on Neural Networks (IJCNN'2000), Como, Italy, .