Recent Developments in Applying Neural Nets to Equalization and Interference Rejection

Since the introductory neural net equalization paper by Gibson in 1989, there have been a number of impressive contributions in the area of applying neural nets for equalization and interference rejection. Demonstrated advantages of neural nets over conventional linear filtering and equalization include: 1. Better compensation of non-linear distortion, 2. Superior rejection of noise, 3. Better equalization of non-minimal phase channels, 4. Better rejection of non-Gaussian interference, 5. Capability of rejecting CDMA interference, 6. Availability of additional blind equalization algorithms, and 7. More robust equalization startup. This paper reviews recent developments and research trends in the application of neural nets to equalization and interference rejection.

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