Robust adaptive spread-spectrum receiver with neural-net preprocessing in non-Gaussian noise

Teong Chee Chuah, Bayan S. Sharif, Oliver R. Hinton

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Multiuser communications channels based on code division multiple access (CDMA) technique exhibit non-Gaussian statistics due to the presence of highly structured multiple access interference (MAI) and impulsive ambient noise. Linear adaptive interference suppression techniques are attractive for mitigating MAI under Gaussian noise. However, the Gaussian noise hypothesis has been found inadequate in many wireless channels characterized by impulsive disturbance. Linear finite impulse response (FIR) filters adapted with linear algorithms are limited by their structural formulation as a simple linear combiner with a hyperplanar decision boundary, which are extremely vulnerable to impulsive interference. This raises the issues of devising robust reception algorithms accounting at the design stage the non-Gaussian behavior of the interference. In this paper we propose a novel multiuser receiver that involves an adaptive nonlinear preprocessing front-end based on multilayer perceptron neural-network, which acts as a mechanism to reduce the influence of impulsive noise followed by a postprocessing stage using linear adaptive filters for MAI suppression. Theoretical arguments supported by promising simulation results suggest that the proposed receiver, which combines the relative merits of both nonlinear and linear signal processing, presents an effective approach for joint suppression of MAI and non-Gaussian ambient noise.

Original languageBritish English
Pages (from-to)546-558
Number of pages13
JournalIEEE Transactions on Neural Networks
Issue number3
StatePublished - May 2001


  • α-stable distributions
  • Influence functions
  • Multi-layer perceptions
  • Non-Gaussian noise
  • Nonlinear receiver
  • Robust algorithms


Dive into the research topics of 'Robust adaptive spread-spectrum receiver with neural-net preprocessing in non-Gaussian noise'. Together they form a unique fingerprint.

Cite this