Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities
🤖 AI Summary
To address the stringent requirements of ultra-low latency, high throughput, and application-awareness in V2X systems under dynamic vehicle density and heterogeneous network conditions, this paper proposes an adaptive multi-access network selection framework. The framework jointly models application sensitivity, end-to-end latency, edge computing load, and vehicular mobility directionality constraints, integrating heuristic optimization, mixed-integer linear programming (MILP) modeling, and a Q-learning comparative mechanism. A novel lightweight heuristic algorithm is designed to achieve >90% of MILP-optimal performance while reducing decision latency to <15 ms—over 85% faster than both MILP and Q-learning, with lower latency. Experimental evaluation in coexisting 4G/5G/VANET scenarios demonstrates real-time deployability, significantly improving network selection efficiency and reliability in highly dynamic environments.
Application Category
📝 Abstract
This paper presents ANS-V2X, an Adaptive Network Selection framework tailored for latency-aware V2X systems operating under varying vehicle densities and heterogeneous network conditions. Modern vehicular environments demand low-latency and high-throughput communication, yet real-time network selection is hindered by diverse application requirements and the coexistence of multiple Radio Access Technologies (RATs) such as 4G, 5G, and ad hoc links. ANS-V2X employs a heuristic-driven approach to assign vehicles to networks by considering application sensitivity, latency, computational load, and directionality constraints. The framework is benchmarked against a Mixed-Integer Linear Programming (MILP) formulation for optimal solutions and a Q-learning-based method representing reinforcement learning. Simulation results demonstrate that ANS-V2X achieves near-optimal performance, typically within 5 to 10% of the utility achieved by MILP-V2X, while reducing execution time by more than 85%. Although MILP-V2X offers globally optimal results, its computation time often exceeds 100 milliseconds, making it unsuitable for real-time applications. The Q-learning-based method is more adaptable but requires extensive training and converges slowly in dynamic scenarios. In contrast, ANS-V2X completes decisions in under 15 milliseconds and consistently delivers lower latency than both alternatives. This confirms its suitability for real-time, edge-level deployment in latency-critical V2X systems
Problem
Research questions and friction points this paper is trying to address.
Adaptive network selection for latency-aware V2X systems
Real-time network assignment under varying vehicle densities
Optimizing communication across multiple RAT technologies
Innovation
Methods, ideas, or system contributions that make the work stand out.
Heuristic-driven adaptive network selection framework
Considers application sensitivity and latency constraints
Achieves near-optimal performance with real-time execution
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