Receiver Optimization and Error Rates for Pseudo-Noise Spread Spectrum Systems with Narrowband Interference Suppression.

Abstract

This report describes the results of a study of maximum likelihood receiver structures for direct sequence spread spectrum communications over channels with multipath distortion and narrowband interference, and several methods of assessing the bit error probability performance of bit-by-bit receivers and maximum likelihood receivers. This work is a continuation of work which focused on the performance of various methods of suppressing narrowband interference using spectral estimation. The previous work gave performance results in terms of SNR improvement provided by interference suppression. The previous results are extended in this report by deriving a receiver structure based on the maximum likelihood (minimum probability of error) principle. Error probability results given include simulation of a bit-by-bit receiver operated in conjunction with dispersive and nondispersive channels with narrowband interference. Also, a technique for assessing the error rate by averaging conditional probabilities is stated which applies to both fixed nondispersive and fixed dispersive channels, and bit-by-bit and maximum likelihood receivers. Probability of error results obtained using this approach are given. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1982
Accession Number
ADA115007

Entities

People

  • John G. Proakis
  • John W. Ketchum

Organizations

  • Northeastern University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Communication Systems
  • Detection
  • Distortion
  • Frequency
  • Intersymbol Interference
  • Matched Filters
  • Narrowband
  • Probability
  • Random Variables
  • Self Noise
  • Sequences
  • Simulations
  • Spectra
  • Spread Spectrum
  • White Noise

Fields of Study

  • Engineering

Readers

  • Radar Systems Engineering.
  • Regression Analysis.