Convergence Study of an Iterative Joint Detector for Wavelet Packet Multiple-Access Communication

Abstract

The joint detection of all users in a multiple access communication system is known to greatly enhance the system performance. The optimal joint detector has a complexity which is exponential in the number of users and is, therefore, impractical. Suboptimal algorithms attempt to achieve near optimal performance with reduced computational complexity. The suboptimal joint detection literature deals only with receiver design, allowing for the use of any set of signature waveforms. Using the structure of the wavelet packet transform [1] we are able to choose signal sets and design a detection algorithm which results in significantly lower computational complexity than other proposed suboptimal joint detectors. The wedding of detector development and wavelet packet signal set design, therefore, becomes an interesting problem. Convergence, performance and receiver complexity of suboptimal MA detectors crucially depend upon the signal set structure; it is, thus, of paramount importance to study the convergence of our iterative algorithm for our signal structure. In this paper, sufficient conditions for the convergence of an iterative joint detection scheme are developed.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1994
Accession Number
ADA456795

Entities

People

  • A. S. Willsky
  • Benjamin Claus
  • Hamid Krim
  • R. E. Learned
  • W. C. Karl

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Communication Systems
  • Computational Complexity
  • Convergence
  • Cross Correlation
  • Detection
  • Detectors
  • Equations
  • Filters
  • Frequency
  • Frequency Bands
  • Matched Filters
  • Multiple Access
  • Sequences
  • Signal Processing
  • Waveforms

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.
  • Radio communications and signal processing.