Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets
Zhiyu Shao, Qiong Wu, Pingyi Fan, Nan Cheng, Qiang Fan, Jiangzhou Wang
Abstract
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 three-tiered HetNets structure. To meet the demands 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). Additionally, we address the coexistence of vehicular users and WiFi users in 5G New Radio Unlicensed (NR-U) networks. Our approach jointly optimizes the DC coexistence mechanism and the allocation of resources and base stations (BSs). Unlike traditional bit-based transmission methods, our approach integrates the semantic communication into the communication system. Experimental results show that our proposed framework significantly improves HSSE and semantic throughput (ST) for both vehicular and WiFi users compared to conventional methods.