Year: 2026
Type: preprint
Abstract: Vehicular ad hoc networks (VANETs) face persistent challenges in maintaining reliable message routing and communication. Existing methods often rely on exhaustive path comparisons or excessive control messages, making them unsuit- able for high-mobility environments. Motivated by these limitations, a Deep neural network (DNN)-based Lookahead routing scheme is proposed that utilizes local spatial-temporal information to enable real-time, globally effcient next- hop decisions. A novel grid-based representation of the vehicular environment is introduced to enable learning-based routing using local observations. First, an analytical model is introduced to minimize per-hop transmission time, effectively reducing end-to-end delay without requiring global topology awareness. Secondly, a U-Net-style DNN is proposed and trained to perform predictive, learning-based routing in dynamic vehicular environments, significantly reducing computational overhead. Empirical evaluations demonstrate that the proposed method outper- forms two state-of-the-art baselines by increasing throughput and packet delivery ratio by 5-10%. Additionally, the outage probability is reduced from 58% to 45% and from 91% to 82%, demonstrating robustness in high mobility.
Source: Research Square
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225040550