# Towards Low-Latency and Ultra-Reliable Vehicle-to-Vehicle Communication

arXiv: https://arxiv.org/abs/1704.06894 · HTML: https://mlsanity.com/paper/1704.06894/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications](https://mlsanity.com/paper/2104.08813/) (2021)
- [Explainable AI for Enhancing Efficiency of DL-based Channel Estimation](https://mlsanity.com/paper/2407.07009/) (2024)
- [EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models](https://mlsanity.com/paper/2412.09782/) (2024)
- [Multi-Agent Deep Reinforcement Learning in Vehicular OCC](https://mlsanity.com/paper/2205.02672/) (2022)

## Abstract (arXiv)

Recently vehicle-to-vehicle (V2V) communication emerged as a key enabling technology to ensure traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service (QoS)-aware resource allocation for V2V communication is proposed. The proposed approach exploits the spatial-temporal aspects of vehicles in terms of their physical proximity and traffic demands, to minimize the total transmission power while considering queuing latency and reliability. Due to the overhead caused by frequent information exchange between vehicles and the roadside unit (RSU), the centralized problem is decoupled into two interrelated subproblems. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles in zones based on their physical proximity. Given the vehicles' traffic demands and their QoS requirements, resource blocks are assigned to each zone. Second, leveraging techniques from Lyapunov stochastic optimization, a power minimization solution is proposed for each V2V pair within each zone. Simulation results for a Manhattan model have shown that the proposed scheme outperforms the baseline in terms of average queuing latency reduction up to 97% and significant improvement in reliability.