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Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning

Autor: Shao, Zhiyu, Wu, Qiong, Fan, Pingyi, Wang, Kezhi, Fan, Qiang, Chen, Wen, Letaief, Khaled B.

This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is

Externí odkaz: http://arxiv.org/abs/2411.04672

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

Autor: Shao, Zhiyu, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Fan, Qiang, Wang, Jiangzhou

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

Externí odkaz: http://arxiv.org/abs/2406.07996

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Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning

Autor: Shao, Zhiyu, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Chen, Wen, Wang, Jiangzhou, Letaief, Khaled B.

This work aims to investigate semantic communication in high-speed mobile Internet of vehicles (IoV) environments, with a focus on the spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We specifical

Externí odkaz: http://arxiv.org/abs/2406.07213

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