@article{M8C894435,
title = "Throughput-aware and DQN-based Heterogeneous Interface Selection Mechanism in 5G NR-V2X Environments",
journal = "Journal of KIISE, JOK",
year = "2026",
issn = "2383-630X",
doi = "10.5626/JOK.2026.53.3.265",
author = "Yebin Lee, Young-Bae Ko",
keywords = "cellular-V2X, 5G NR-V2X, autonomous driving infrastructure, heterogeneous interface selection, reinforcement learning, deep Q-network (DQN)",
abstract = "As autonomous driving technology nears commercialization, maintaining stable communication performance in diverse Vehicle-to-Everything (V2X) environments becomes increasingly critical. However, delays, disconnections, and packet loss that occur during transitions between different interfaces can degrade throughput performance. While previous studies have mainly concentrated on seamless handover, they fall short in optimizing the data transmission and reception required in real-world autonomous driving scenarios. This paper proposes a Deep Q-Network (DQN)-based interface selection algorithm to address these limitations and enhance throughput. Performance analysis using ns-3 demonstrates that the proposed mechanism achieves an approximate 14% improvement in throughput compared to existing handover algorithms."
}