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Robust adaptive spread-spectrum receiver with neural net preprocessing in non-Gaussian noise
T C Chuah1, B S Sharif, O R Hinton
1Department of Electrical and Electronic Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK. T.C.Chuah@newcastle.ac.uk
IEEE Transactions on Neural Networks
|February 6, 2008
Summary
This study introduces a novel multiuser receiver for code division multiple access (CDMA) systems. It effectively suppresses non-Gaussian noise and multiple access interference (MAI) using a hybrid nonlinear and linear approach.
Area of Science:
- Wireless Communications
- Signal Processing
- Machine Learning
Background:
- Code division multiple access (CDMA) channels often exhibit non-Gaussian statistics.
- Impulsive ambient noise and multiple access interference (MAI) degrade performance.
- Linear adaptive filters are insufficient against non-Gaussian interference.
Purpose of the Study:
- To develop a robust multiuser receiver for CDMA systems.
- To address the limitations of linear filters in non-Gaussian noise environments.
- To jointly suppress MAI and non-Gaussian ambient noise.
Main Methods:
- Proposed a hybrid receiver architecture.
- Employed a nonlinear multilayer perceptron neural network for impulsive noise reduction.
- Utilized linear adaptive filters for postprocessing and MAI suppression.
Main Results:
- The proposed receiver effectively reduces the impact of impulsive noise.
- Achieved significant suppression of multiple access interference (MAI).
- Simulation results demonstrate superior performance compared to linear methods.
Conclusions:
- The combined nonlinear and linear signal processing approach is effective.
- The novel receiver offers robust performance in challenging wireless channels.
- This method provides a promising solution for joint interference and noise suppression.
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