Automatic classification of linear modulations using multiple receivers in the presence of symbol timing uncertainty


Efendi E., DÜLEK B.

ELECTRONICS LETTERS, vol.55, no.18, pp.994-996, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 55 Issue: 18
  • Publication Date: 2019
  • Doi Number: 10.1049/el.2019.0915
  • Journal Name: ELECTRONICS LETTERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.994-996
  • Keywords: particle swarm optimisation, fading channels, parameter estimation, AWGN channels, modulation, signal classification, radio receivers, radio transmitters, sequences, statistical testing, symbol timing uncertainty, multiple receivers, additive white Gaussian noise channels, hybrid likelihood framework, particle swarm optimisation technique, average likelihood expression, average received signal-to-noise ratio, automatic signal classification, linear modulation classification, unknown flat block fading channel, symbol sequence transmission, parameter estimation, statistical testing
  • Hacettepe University Affiliated: Yes

Abstract

Modulation classification of signals collected from multiple receivers over additive white Gaussian noise channels is considered. It is assumed that the channel between the transmitter and each receiver is subject to unknown flat block fading and symbol timing uncertainty. Following the hybrid likelihood framework, the likelihood function of the waveforms observed at the receivers is marginalised over the distribution of the transmitted symbol sequence. Then, the parameter estimates obtained by the particle swarm optimisation technique are substituted into the average likelihood expression in order to compute the test statistic for each assumed modulation scheme. A classification decision is declared in favour of the modulation scheme with the highest overall likelihood score. Numerical examples are provided to evaluate the performance of the proposed classifier as a function of the average received signal-to-noise ratio for different number of receivers and varying quality of the initial parameter estimates.