AI 中文总结
针对无电池V2X网络的被动叠加通信难题，本文提出基于DSRC的分组式架构，构建物理层模型，采用MARL方法实现参数自适应，使平均吞吐量提升15%。
AI 中文摘要
无电池设备的被动叠加通信是下一代车万物联网（V2X）网络的重要使能能力，但在不占用额外频谱的前提下实现可靠的被动有效载荷传输颇具挑战，因为叠加信令必须嵌入短且时变的车载数据包中，同时还要保证传统主机传输的可解码性。本文研究一种基于专用短程通信（DSRC）的分组式无电池V2X叠加架构，该架构使单个分组同时承载传统V2X数据与被动叠加有效载荷。本文构建了紧凑的物理层模型，以表征衰减深度、嵌入比特率以及传统调制与编码方案（MCS）对主机链路和被动链路可靠性的耦合影响，以及分组级嵌入可行性。随后，本文构建了总和吞吐量最大化问题，该问题同时考虑了传统分组误码率与被动解码误码率。本文进一步提出一种基于多智能体强化学习（MARL）的自适应参数选择方法。仿真结果表明，所提MARL控制器实现了稳定收敛，并将平均吞吐量提升了15%，证明了面向无电池V2X叠加通信的吞吐量驱动型物理层自适应方法的有效性。
英文摘要
Passive overlay communication for batteryless devices is an important enabling capability for next-generation vehicle-to-everything (V2X) networks. However, enabling reliable passive payload delivery without occupying additional spectrum remains challenging, since overlay signaling must be embedded into short and time-varying vehicular packets while preserving the decodability of the legacy host transmission. This paper investigates a packetized batteryless V2X overlay architecture in which a dedicated short-range communications (DSRC)-based packet simultaneously carries conventional V2X data and a passive overlay payload. A compact PHY-layer model is developed to characterize the coupled effects of attenuation depth, embedded-bit rate, and legacy modulation and coding scheme (MCS) on host-link and passive-link reliability, as well as packet-level embedding feasibility. We then formulate a sum-throughput maximization problem that jointly accounts for the legacy packet error rate and passive decoding error rate. We further propose a multi-agent reinforcement learning (MARL)-based adaptive parameter-selection method. Simulation results show that the proposed MARL controller achieves stable convergence and improves the average throughput by 15\%, demonstrating the effectiveness of throughput-driven PHY adaptation for batteryless V2X overlay communications.
CommentsThis work has been accepted to the 2026 IEEE Global Communications Conference: Green Communication Systems and Networks.6 pages,4 figures,conference paper
