Low Complexity Signal Processing and Optimal Joint Detection for Over-Saturated Multiple Access Communications

Abstract

This paper addresses the problem of uncoded multiple access (MA) joint detection for the case in which user signatures are linearly dependent. The linearly dependent scenario would occur when the number of users in a communication system is increased beyond the dimension of the signal space available for transmission. This "over-saturating" of the signal space can, in principle, be accomplished with minimal impact on system performance, assuming that optimal detection can he implemented. The optimal detector for the general over-saturated case has a complexity that is exponential in the number of users. To find a joint detector for the MA communication of K > N users in N dimensional signal space, the authors impose a hierarchical cross-correlation structure on the user signature waveforms. This paper develops a tree processing procedure that takes advantage of this structure to give the optimal estimate with an extremely low computational complexity. The authors show this complexity to be (in typical cases) a low-order-polynomial in the number of users. This is an enormous savings in computations over the O(2 sup K) computations needed if the signatures did not exhibit any structure.

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Document Details

Document Type
Technical Report
Publication Date
Dec 29, 1995
Accession Number
ADA459653

Entities

People

  • Alan S. Willsky
  • Don M. Boroson
  • Rachel E. Learned

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Coefficients
  • Communication Channels
  • Communication Systems
  • Computational Complexity
  • Computations
  • Cross Correlation
  • Detection
  • Detectors
  • Filters
  • Matched Filters
  • Multiple Access
  • Optimal Estimators
  • Signal Processing
  • Standards
  • Waveforms

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computer Science.
  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • Space