Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets

TL;DR

This paper introduces a semantic-aware resource allocation framework using deep reinforcement learning for 5G-V2X HetNets, optimizing spectrum efficiency and semantic throughput in vehicular and WiFi coexistence scenarios.

Contribution

It proposes a novel semantic communication-based resource allocation method with a flexible duty cycle mechanism for 5G-V2X HetNets, enhancing spectrum efficiency and throughput.

Findings

Outperforms traditional methods in spectrum efficiency and semantic throughput.

Effectively manages coexistence of vehicular and WiFi users.

Demonstrates benefits of semantic communication in resource allocation.

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 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.