Adaptive equalizer using self-learning neural network
US5504780A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Jan 6, 1994 |
| Grant date | Apr 2, 1996 |
| Priority date | — |
| Expiry date | Jan 6, 2014 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L2025/03464
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.