Cluster-Based Content Distribution Integrating LTE and IEEE 802.11p with Fuzzy Logic and Q-Learning

Celimuge Wu, Tsutomu Yoshinaga, Xianfu Chen, Lin Zhang, Yusheng Ji

    Research output: Contribution to journalArticleScientificpeer-review

    52 Citations (Scopus)


    There is an increasing demand for distributing a large amount of content to vehicles on the road. However, the cellular network is not sufficient due to its limited bandwidth in a dense vehicle environment. In recent years, vehicular ad hoc networks (VANETs) have been attracting great interests for improving communications between vehicles using infrastructure-less wireless technologies. In this paper, we discuss integrating LTE (Long Term Evolution) with IEEE 802.11p for the content distribution in VANETs. We propose a two-level clustering approach where cluster head nodes in the first level try to reduce the MAC layer contentions for vehicle-tovehicle (V2V) communications, and cluster head nodes in the second level are responsible for providing a gateway functionality between V2V and LTE. A fuzzy logic-based algorithm is employed in the first-level clustering, and a Q-learning algorithm is used in the second-level clustering to tune the number of gateway nodes. We conduct extensive simulations to evaluate the performance of the proposed protocol under various network conditions. Simulation results show that the proposed protocol can achieve 23% throughput improvement in highdensity scenarios compared to the existing approaches.

    Original languageEnglish
    Article number8253732
    Pages (from-to)41-50
    Number of pages10
    JournalIEEE Computational Intelligence Magazine
    Issue number1
    Publication statusPublished - 1 Feb 2018
    MoE publication typeA1 Journal article-refereed


    Dive into the research topics of 'Cluster-Based Content Distribution Integrating LTE and IEEE 802.11p with Fuzzy Logic and Q-Learning'. Together they form a unique fingerprint.

    Cite this