Performance of Adaptive Coded Modulation Enabled by Long-Range Fading Prediction with Data-Aided Noise Reduction

Abstract

Performance of uncoded adaptive modulation (UAM) and adaptive coded modulation (ACM) enabled by the long-range prediction (LRP) that utilizes data-aided noise reduction (DANR) is investigated for rapidly varying mobile radio channels. Due to improved prediction accuracy and low pilot rate, the DANR-aided LRP outperforms previously proposed prediction methods that rely on oversampled pilots to achieve noise reduction (NR). While ACM is more sensitive to prediction errors than UAM, utilization of DANR substantially increases its spectral efficiency (SE) relative to previously proposed methods. The set of SNR values and prediction ranges where positive coding gain is achieved by ACM enabled by DANR-aided LRP is determined. It is also demonstrated that adaptive modulation (AM) aided by LRP has better performance for the realistic physical model than for the Jakes model in the practical SNR range.

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Document Details

Document Type
Technical Report
Publication Date
Aug 29, 2008
Accession Number
ADA500344

Entities

People

  • Alexandra Duel-hallen
  • Hans D. Hallen
  • Tao Jia

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Coders
  • Coding
  • Coefficients
  • Computer Programming
  • Contracts
  • Decoding
  • Geometry
  • Information Science
  • Noise
  • Noise Reduction
  • North Carolina
  • Random Variables
  • Simulations
  • Statistics
  • Symbols
  • Universities

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Cybersecurity.
  • Radio communications and signal processing.