Classification of Digital Modulation Types in Multipath Environments

Abstract

As the expansion of digital communication applications still continues, the need for automated classification of digital modulation types increases. This study attempts to give a partial solution to this problem by proposing a classification scheme which identifies nine of the most popular digital modulation types; namely 2-FSK, 4-FSK, 8-FSK, 2-PSK, 4-PSK, 8-PSK, 16-QAM, 64-QAM and 256-QAM. Higher-order statistics parameters are selected as class features, and a hierarchical neural network-based classifier set-up proposed for the identification of all modulation types considered except those within the M-QAM family. Specific M-QAM types identification is obtained via equalization-based schemes. This study considers the effects due to real-world multipath propagation channels and additive white Gaussian noise. Results show a consistent overall classification performance of at least 68% for severe multipath propagation models and for SNR levels as low as 11dB.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2001
Accession Number
ADA390810

Entities

People

  • George Hatzichristos

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Communication Systems
  • Data Science
  • Data Sets
  • Digital Communications
  • Digital Information
  • Doppler Effect
  • Frequency Shift
  • Gaussian Noise
  • Information Science
  • Modulation
  • Multipath Transmission
  • Neural Networks
  • Order Statistics
  • Random Variables
  • Signal Processing
  • Statistics
  • Wave Propagation

Fields of Study

  • Engineering

Readers

  • Computer Vision.
  • Radio communications and signal processing.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks