`Stop-and-go' decision-directed blind adaptive equalization using the complex-valued multilayer perceptron

Cheolwoo You, Daesik Hong

Research output: Contribution to journalConference articlepeer-review


In this paper, a `stop-and-go' decision-directed blind equalization scheme is newly proposed. This scheme uses the structure of complex-valued multilayer feedforward neural networks, instead of the linear transversal filters that are usually used in conventional LMS-type blind equalization schemes. A complex-valued activation function composed of two real functions is used. Each real activation function has multi-saturated output region in order to deal with QAM signals of any constellation sizes. Also, the complex backpropagation algorithm is modified for the proposed scheme. Computer simulation are performed to compare the proposed scheme with the conventional `stop-and-go' algorithm in terms of convergence speed, MSE value in the steady state, and constellation of QAM signals after the initial convergence. Simulation results demonstrate the effectiveness of the proposed scheme.

Original languageEnglish
Pages (from-to)3289-3292
Number of pages4
JournalElectronic Journal of Combinatorics
Publication statusPublished - 1997
EventProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger
Duration: 1997 Apr 211997 Apr 24

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Geometry and Topology
  • Discrete Mathematics and Combinatorics
  • Computational Theory and Mathematics
  • Applied Mathematics


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