Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets
CoRR(2024)
摘要
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.
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关键词
Semantic communication,vehicular networks,resource allocation,unlicensed spectrum bands,5G NR-U coexistence,deep reinforcement learning
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