Chapter 11. Characterization, Modeling and Simulation of Linear Fading Channels

  1. Heinrich Meyr,
  2. Marc Moeneclaey and
  3. Stefan A. Fechtel

Published Online: 9 OCT 2001

DOI: 10.1002/0471200573.ch11

Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing

Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing

How to Cite

Meyr, H., Moeneclaey, M. and Fechtel, S. A. (2001) Characterization, Modeling and Simulation of Linear Fading Channels, in Digital Communication Receivers: Synchronization, Channel Estimation, and Signal Processing, John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471200573.ch11

Publication History

  1. Published Online: 9 OCT 2001

Book Series:

  1. Wiley Series in Telecommunications and Signal Processing

Book Series Editors:

  1. John G. Proakis

Series Editor Information

  1. Northeastern University

ISBN Information

Print ISBN: 9780471502753

Online ISBN: 9780471200574



  • characterization;
  • modeling;
  • simulation;
  • linear fading channels;
  • digital transmission;
  • discrete-equivalent fading channels;
  • examples


In order to meet the ever-increasing need for both increased mobility and higher quality of a larger selection of services, wireless radio transmission of digital information such as digitized speech, still or moving images, written messages, and other data plays an increasingly important role in the design and implementation of mobile and personal communication systems.

Nearly all radio channels of interest are more or less time-variant and dispersive in nature. However, many electromagnetic environments, e.g., satellite or line-of-sight (LOS) microwave channels, may often be regarded as effectively time-invariant. In such cases, receiver structures, including synchronizers that have been derived for static channels may be applied.

On the other hand, when environments such as the land-mobile (LM), satellite-mobile (SM), or ionospheric shortwave (high-frequency, HF) channels exhibit significant signal variations on a short-term time scale, this signal fading affects nearly every stage of the communication system. Here we focus on linear modulation formats. Large variations of received signal levels caused by fading put additional strain on linear digital receiver components; the resolution of A/D converters and the precision of digital signal processing must be higher than in the case of static channels. More importantly, deep signal fades that may occur quite frequently must be bridged by applying diversity techniques, most often explicit or implicit time diversity, antenna, frequency, spatial, and/or polarization diversity. Moreover, if the channel dispersion results in intersymbol interference (ISI), this must be counteracted by means of an (adaptive) equalizer. Finally, transmission over fading channels necessitates specifically designed synchronizer structures and algorithms that are, in general, substantially different from those for static channels.

We are primarily interested in synchronizers that are mathematically derived in a systematic manner, based upon a suitable model of all signals and systems involved. In particular, adequate modeling of the fading channel is of highest concern. Since the channel variations as observed by the receiver appear to be random, the channel model will most often be a statistical one. Furthermore, as synchronizers primarily have to cope with short-term variations of quantities such as amplitude(s) and phase(s) of received signals, it often suffices to assume stationary statistical channel properties, at least over a reasonably short time frame.