基于深度强化学习的 NR-V2X 系统中年龄信息与能耗联合优化
摘要
自动驾驶可能是下一代无线接入技术最重要的应用场景，其发展对于实现可靠且低延迟的车辆间通信至关重要。为此，3GPP 基于 5G New Radio (NR) 技术开发了面向车辆到一切 (V2X) 的规范，其中 Mode 2 Side-Link (SL) 通信类似 LTE-V2X 中的 Mode 4，允许车辆之间进行直接通信。这补充了 LTE-V2X 中的 SL 通信，代表了基于 NR 的 V2X (NR-V2X) 最新的进展，具有 improved performance。然而，在 NR-V2X Mode 2 中，仍发生资源冲突，导致年龄信息 (AOI) 降低。因此，采用干扰消除方法来缓解此影响，通过将 NR-V2X 与非正交多访问 (NOMA) 技术结合来实现。 In NR-V2X, when vehicles select smaller resource reservation interval (RRI), higher-frequency transmissions take ore energy to reduce AoI. Hence, it is important to jointly consider AoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations have demonstrated the performance of our proposed algorithm.
引用
@article{arxiv.2407.08458,
title = {Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning},
author = {Shulin Song and Zheng Zhang and Qiong Wu and Qiang Fan and Pingyi Fan},
journal= {arXiv preprint arXiv:2407.08458},
year = {2025}
}
备注
This paper has been accepted by sensors. The source code has been released at: https://github.com/qiongwu86/Joint-Optimization-of-AoI-and-Energy-Consumption-in-NR-V2X-System-based-on-DRL