Pattern based encoding for cognitive communication

Berk Ustundag*, Ozgur Orcay

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

A new encoding technique based on construction of communication signals with respect to optimal perceptual pattern sensitivity of the cognitive receivers is proposed here. Communication signals are directly generated end adapted according to medium properties without a modulator stage. Receivers recognize signal patterns from a chosen frequency band. These patterns are matched to respecting data sequence within a glossary to recover the information. Our purpose is increasing the bandwidth efficiency via manageable Signal to Noise Ratio. Error free recoverability of the information encoded and carried by multiple pattern arbitrations in a noisy communication channel is the key factor for this purpose. A pattern based encoding and neural network based decoding technique is used in this study. It is shown that if the predefined communication patterns are appropriately chosen then the transmitted information by multiple sources can be decoded by the receivers in the same time interval, frequency band and location.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom 2008
DOIs
Publication statusPublished - 2008
Event3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom 2008 - Singapore, Singapore
Duration: 15 May 200817 May 2008

Publication series

NameProceedings of the 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom 2008

Conference

Conference3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom 2008
Country/TerritorySingapore
CitySingapore
Period15/05/0817/05/08

Keywords

  • Cognitive radio
  • Neural network
  • Noise immunity
  • Pattern recognition
  • Spectrum management

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