Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization
Stop-and-go decision-directed (S&G-DD) equalization is the most primitive blind equalization (BE) method for the cancelling of intersymbol-interference in data communication systems. Recently, this scheme has been applied to complex-valued multilayer feedforward neural network, giving robust res...
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| Other Authors: | , , |
| Format: | article |
| Published: |
2002
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| Online Access: | https://eprints.kfupm.edu.sa/id/eprint/14125/1/14125_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14125/2/14125_2.doc |
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| Summary: | Stop-and-go decision-directed (S&G-DD) equalization is the most primitive blind equalization (BE) method for the cancelling of intersymbol-interference in data communication systems. Recently, this scheme has been applied to complex-valued multilayer feedforward neural network, giving robust results with a lower mean-square error at the expense of slow convergence. To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. Simulation results show the effectiveness of the proposed algorithm in terms of initial convergence. |
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