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Network-Wide Throughput Optimization for Highway Vehicle-To-Vehicle Communications

Electronics Engineering Department, Chungbuk National University, Cheongju 28644, Korea
Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia
Independent Researcher, Mountain View, CA 94043, USA
Author to whom correspondence should be addressed.
Electronics 2019, 8(8), 830;
Received: 3 June 2019 / Revised: 19 July 2019 / Accepted: 22 July 2019 / Published: 26 July 2019
(This article belongs to the Section Networks)
PDF [547 KB, uploaded 26 July 2019]


This letter analyzes the meta-distribution of the transmission success probability (TSP) for vehicular networks based on a one-dimensional Poisson point process (PPP). We also propose a method to maximize the throughput across the vehicular network. Compared to the conventional spatial average performance assessment, the meta-distribution reveals the fraction of vehicles that operate at a target success rate of transmission across the highway. To this end, we propose a per-vehicle rate selection scheme to keep a target quality of service (QoS) level for all vehicles. The results reveal that operating at the spatially-averaged maximum throughput may lead to excessive variation in the performance of individual vehicles. However, with the proposed meta-distribution-aware rate selection scheme, the throughput variation among the vehicles can be significantly reduced (e.g., up to a 60 % reduction). View Full-Text
Keywords: VANETs; meta-distribution; PPP; stochastic geometry; rate adaptation VANETs; meta-distribution; PPP; stochastic geometry; rate adaptation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Abd El-Gawad, M.A.; ElSawy, H.; Sakr, A.H.; Kim, H. Network-Wide Throughput Optimization for Highway Vehicle-To-Vehicle Communications. Electronics 2019, 8, 830.

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