Summary of Semantic-aware Spectrum Sharing in Internet Of Vehicles Based on Deep Reinforcement Learning, by Zhiyu Shao et al.
Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning
by Zhiyu Shao, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores semantic communication in high-speed mobile Internet of vehicles (IoV) environments, addressing the challenges of spectrum scarcity and network traffic. To tackle these issues, it proposes a semantic-aware spectrum sharing algorithm (SSS) based on deep reinforcement learning (DRL) soft actor-critic (SAC) approach. The SSS algorithm optimizes decision-making for V2V and V2I spectrum sharing based on semantic information, maximizing high-speed semantic spectrum efficiency (HSSE) of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V. Experimental results show that the proposed algorithm outperforms baseline algorithms, achieving a 15% increase in HSSE and approximately a 7% increase in SRS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make sure cars and infrastructure can talk to each other safely and efficiently on the road. It’s like trying to find a quiet spot on the highway so all the traffic can flow smoothly. The researchers came up with a special algorithm that helps figure out when and how cars should communicate with each other and with roadside equipment, making it easier for them to share information and avoid accidents. |
Keywords
» Artificial intelligence » Reinforcement learning
