Connectivity Probability Analysis for Green Cooperative Cognitive Vehicular Networks

Xuan Li, Ruiwei Zhou, Tianqing Zhou, Lei Liu, Keping Yu

Research output: Contribution to journalArticlepeer-review

Abstract

To meet the increasing demands of green communications for vehicular services, cognitive vehicular networks (CVNs) are expected to improve spectrum efficiency via cognitive radio (CR) technology. The connectivity of CVNs not only related to the communication between cognitive vehicles (CVs), but also detection of idle spectrum. Nevertheless, few existing works simultaneously consider the communication and cognitive probabilities on the road. In this paper, we use probability theory to deduce the connectivity probabilities of CVNs. The analysis scenario consists of two parts: single-hop scenario and multi-hop clustering scenario. Particularly, in multi-hop clustering scenario, we propose Inter-Cluster integration process and Intra-Cluster communication process respectively, and deduce the probability expressions for two cases. The relationships between connectivity and parameters of both channel circumstance and traffic factors are given via theoretical analysis. The simulation results show that connectivity in multi-hop clustering scenario is better than that in single-hop case and the effect of signal-to-noise ratio (SNR) on connectivity is prominent, which indicates that the cognitive probability deeply affects the connectivity probability in CVNs.

Original languageEnglish
JournalIEEE Transactions on Green Communications and Networking
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Cognitive Vehicular Networks
  • Connectivity Analysis
  • Green Communications.
  • Green products
  • Sensors
  • Shadow mapping
  • Signal to noise ratio
  • Spread spectrum communication
  • Vehicular ad hoc networks
  • Vehicular Clustering
  • Wireless sensor networks

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications

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