Analysis of Opportunistic Relaying and Load Balancing Gains Through V2V Clustering

• Published in: IEEE transactions on vehicular technology Vol. 71; no. 9; pp. 9896 - 9911
• Main Authors: Kassir, Saadallah, de Veciana, Gustavo, Wang, Nannan, Wang, Xi, Palacharla, Paparao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Automobile industry; Autonomous cars; Cellular networks; Clustering; Computer architecture; Electronic devices; Infrastructure; Load balancing; Load management; Load modeling; Loads (forces); opportunism; Optimization; Parameter sensitivity; Performance evaluation; Relaying; Roads; Throughput; Transportation networks; vehicle-to-vehicle communication; Vehicles; Vehicular ad hoc networks; vehicular network; Wireless communication
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3178129

Recent advances in digitization, artificial intelligence, and wireless networking are revolutionizing the traditional transportation networks and the automotive industry, leading for instance to the widespread adoption of ride-sharing services and the emergence of self-driving vehicles. As a result, a considerable rise in the volume of infotainment data consumed by the vehicles' passengers is expected. To support such traffic loads, we explore leveraging vehicles as part of the cellular infrastructure. This paper investigates and quantifies the performance gains that using Vehicle-to-Vehicle (V2V) communication to form relay clusters would enable, including (1) improving opportunistic channel access to the cellular infrastructure by relaying the cluster's traffic through the vehicle seeing the best channel quality; and (2) balancing traffic loads across cells through cluster multihoming. We introduce a stochastic geometric model allowing us to provide a deeper understanding of the possible gains associated with cluster-based opportunistic relaying and its sensitivity to the system parameters, e.g., base station density, vehicle density on the roads, etc. We develop a natural network utility maximization problem to serve as a baseline to evaluate the performance of a simpler distributed cluster management algorithm which we show to be near-optimal. Overall the results suggest that <inline-formula><tex-math notation="LaTeX">10-20\times</tex-math></inline-formula> gains in the vehicles' shared rate can be achieved along with significant fairness improvements, while other mobile devices also benefit from the proposed network architecture.