This letter proposes a semantic-aware resource allocation (SARA) framework with flexible duty cycle (DC) coexistence mechanism (SARADC) for 5G-V2X Heterogeneous Network (HetNets) based on deep reinforcement learning (DRL) proximal policy optimization (PPO). Specifically, we investigate V2X networks within a two-tiered HetNets structure. In response to the needs of high-speed vehicular networking in urban environments, we design a semantic communication system and introduce two resource allocation metrics: high-speed semantic transmission rate (HSR) and semantic spectrum efficiency (HSSE). Our main goal is to maximize HSSE. Additionally, we address the coexistence of vehicular users and WiFi users in 5G New Radio Unlicensed (NR-U) networks. To tackle this complex challenge, we propose a novel approach that jointly optimizes flexible DC coexistence mechanism and the allocation of resources and base stations (BSs). Unlike traditional bit transmission methods, our approach integrates the semantic communication paradigm into the communication system. Experimental results demonstrate that our proposed solution outperforms traditional bit transmission methods with traditional DC coexistence mechanism in terms of HSSE and semantic throughput (ST) for both vehicular and WiFi users.

翻译：本文提出了一种基于深度强化学习近端策略优化（PPO）的语义感知资源分配（SARA）框架，该框架结合灵活占空比（DC）共存机制（SARADC），适用于5G车联网（V2X）异构网络（HetNets）。具体而言，我们在双层HetNets结构内研究V2X网络。针对城市环境中高速车联网的需求，我们设计了一个语义通信系统，并引入了两个资源分配指标：高速语义传输速率（HSR）与语义频谱效率（HSSE）。我们的主要目标是最大化HSSE。此外，我们还解决了5G新空口非授权频谱（NR-U）网络中车载用户与WiFi用户的共存问题。为应对这一复杂挑战，我们提出了一种新颖方法，联合优化灵活的DC共存机制以及资源与基站（BSs）的分配。与传统比特传输方法不同，我们的方法将语义通信范式集成到通信系统中。实验结果表明，对于车载用户和WiFi用户，我们提出的解决方案在HSSE和语义吞吐量（ST）方面均优于采用传统DC共存机制的传统比特传输方法。