Deep Reinforcement Learning-aided Transmission Design for Energy-efficient Link Optimization in Vehicular Communications Article Swipe
Zhengpeng Wang
,
Yanqun Tang
,
Yingzhe Mao
,
Tao Wang
,
Xiunan Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.12595
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.12595
This letter presents a deep reinforcement learning (DRL) approach for transmission design to optimize the energy efficiency in vehicle-to-vehicle (V2V) communication links. Considering the dynamic environment of vehicular communications, the optimization problem is non-convex and mathematically difficult to solve. Hence, we propose scenario identification-based double and Dueling deep Q-Network (SI-D3QN), a DRL algorithm integrating both double deep Q-Network and Dueling deep Q-Network, for the joint design of modulation and coding scheme (MCS) selection and power control. To be more specific, we employ SI techique to enhance link performance and assit the D3QN agent in refining its decision-making processes. The experiment results demonstrate that, across various optimization tasks, our proposed SI-D3QN agent outperforms the benchmark algorithms in terms of the valid actions and link performance metrics. Particularly, while ensuring significant improvement in energy efficiency, the agent facilitates a 29.6% enhancement in the link throughput under the same energy consumption.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.12595
- https://arxiv.org/pdf/2404.12595
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4395022129
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4395022129Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.12595Digital Object Identifier
- Title
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Deep Reinforcement Learning-aided Transmission Design for Energy-efficient Link Optimization in Vehicular CommunicationsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-19Full publication date if available
- Authors
-
Zhengpeng Wang, Yanqun Tang, Yingzhe Mao, Tao Wang, Xiunan HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.12595Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2404.12595Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2404.12595Direct OA link when available
- Concepts
-
Reinforcement learning, Link (geometry), Computer science, Transmission (telecommunications), Reinforcement, Artificial intelligence, Computer network, Telecommunications, Engineering, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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