Model of deep fading

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Introduction: Fading, i. e. random changes in the level of a radio signal, is one of the main problems in signal processing, as the result strongly depends on the fading depth. The distribution of the received signal envelope is sometimes left-shifted relative to the Rayleigh law. A radio propagation channel with such a sub-Rayleigh fading^ may be considered a "critical" one. Purpose: Synthesizing a model of a narrow-band random process with an envelope distribution left-shifted relative to the Rayleigh law. Results: The synthesis of a model is based on representing the process as a reaction of a stable dynamic system to white Gaussian noise excitation. We have obtained nonlinear stochastic second-order differential equations to simulate the fading of a radio signal having an envelope with Nakagami or Weibull distribution. The envelope is considered a Markov continuous process. An analytical expression for the envelope correlation function is obtained. It is shown that, at least, for Nakagami fading, the correlation interval of the envelope almost does not depend on its depth. Practical relevance: In various applications, including indoor radio communication, the level of the received signal can be critically low during long time intervals. The proposed model used as a simulator core for such a propagation channel provides the opportunity to evaluate the performance quality of a communication system at the stage of its development.

Original languageEnglish
Pages (from-to)123-127
Number of pages5
JournalInformatsionno-Upravliaiushchie Sistemy
Volume2018
Issue number1
DOIs
StatePublished - 1 Jan 2018

Keywords

  • Markovian Diffusion Processes
  • Nakagami Distribution Envelope
  • Radio Signal Propagation Model
  • Stochastic Differential Equations
  • Sub-Rayleigh Fading
  • Weibull Distribution Envelope

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Control and Optimization

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