Adaptive equalizer using self-learning neural network

Details Adaptive equalizer using self-learning neural network Adaptive equalizer using self-learning neural network

1994-01-06

1996-04-02

Coles, Sr., Edward L.

Pulse or digital communications

Equalizers

Automatic

375229, 375232, 395 21, 395 23, H03H 730

Patent

active

055047807

ABSTRACT:
A channel equalizer is formed using a self-learning neural network. During a training period, the neural network is taught the channel response function. The network is then used to equalize distortions introduced into signals by the channel. The neural network may be a Boltzmann Machine type of neural network comprising neurons arranged in an input layer, a hidden layer, and an output layer. The neurons are interconnected by bidirectional symmetric weighted synapses. Each neuron is preferably implemented by an analog integrated circuit. Direct communication between the input and output layers helps in faster channel acquisition. The scheme can very easily be extended to multilevel and multisymbol modulation schemes such as QAM and PSK.

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