Parameter Estimation and Modeling of Interference Cancellation Technique for Multiple Signal Recovery

Abstract

In this thesis, the amplitude gain of received quadrature phase shift keying (QPSK) signals and interferences are estimated via the least squares error method in order to facilitate the implementation of reference-based successive interference cancellation (RSIC). In the scenario considered for this thesis, a system of multi-platform receivers is assumed to be positioned within the overlapping coverage areas of transmitting base stations. The first receiver initiates the RSIC technique by obtaining a demodulated reference signal and forwarding that reference to the second receiver that separately collects a second signal corrupted by interference, which is actually a scaled version of the first signal with an unknown amplitude gain. This amplitude gain is estimated and applied to the known reference signal so that it is subtracted from the collected signal at the second receiver. The process continues with a third and fourth receiver (or potentially more receivers) until the final desired signal is separated from all the interferences. Finally, the accuracy of the estimations is evaluated and the symbol error rate performances via Monte Carlo simulations for QPSK modulation in a multi-platform system are presented.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2013
Accession Number
ADA585605

Entities

People

  • Alexander Rios

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Amplitude
  • Cancellation
  • Communication Systems
  • Electrical Engineering
  • Errors
  • Estimators
  • Gaussian Noise
  • Geographic Regions
  • Modulation
  • Monte Carlo Method
  • Multiple Input Multiple Output
  • Platforms
  • Signal Processing
  • Simulations
  • United States Naval Academy
  • Wireless Communications

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Computational Modeling and Simulation
  • Radio communications and signal processing.