Distributed and coordinated spectrum access methods for heterogeneous channel bonding

Zaheer Khan, Janne Lehtomaki, Simon Scott, Zhu Han, Marwan Krunz, Alan Marshall

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Channel bonding (CB) is a technique that enables a wireless link to combine channels and achieve higher data rates. In this paper, competition for efficient spectrum access among autonomous users with heterogeneous CB capabilities is considered. Specifically, we propose distributed and coordinated channel/bonding selection methods under signal-to-interference-plus-noise ratio (SINR) and collision-protocol models. In our methods, users utilize only limited feedback to distributively arrive at CB selections that minimize their probability of conflict. The proposed method utilizes a novel channel quality metric, which is based on the ratio of noise power to the sum of interference and noise power. It is shown that CB can lead to higher data rates, and it is most beneficial when users have a high SINR. However, it is also shown that as the ratio of users to available channels increases, CB performance degrades. Our results show that under certain scenarios, the proposed coordinated and distributed channel/bonding selection schemes help users converge fast to conflict-free channel selections as compared to the other channel/bonding selection schemes. Moreover, the proposed schemes result in considerably superior performance to existing CB schemes in terms of network data rate.

Original languageEnglish (US)
Article number7935503
Pages (from-to)267-281
Number of pages15
JournalIEEE Transactions on Cognitive Communications and Networking
Volume3
Issue number3
DOIs
StatePublished - Sep 2017

Keywords

  • Channel bonding
  • SINR-protocol model
  • collision-protocol model
  • distributed users
  • heterogeneous capabilities
  • opportunistic spectrum access
  • spectrum access system

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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