Abstract
This paper presents a Deep Q-Network (DQN) based scheme which adjusts the Transmission Opportunity (TXOP) time for Vehicle-to-Everything (V2X) communications in unlicensed bands. Our scheme adjusts the TXOP duration according to different traffic scenarios and communication requirements to efficiently utilize the unlicensed spectrum. Simulation results demonstrate the effectiveness of our scheme to flexibly allocate unlicensed spectrum resource in different traffic environments and balance the throughput and fairness for V2X and Wi-Fi users.
| Original language | English |
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| Title of host publication | ISPACS 2024 - International Symposium on Intelligent Signal Processing and Communication Systems |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Edition | 2024 |
| ISBN (Electronic) | 9798350389210 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024 - Kaohsiung, Taiwan Duration: 2024 Dec 10 → 2024 Dec 13 |
Conference
| Conference | 2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024 |
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| Country/Territory | Taiwan |
| City | Kaohsiung |
| Period | 2024/12/10 → 2024/12/13 |
Keywords
- 5G communication system
- Deep Q-Network (DQN)
- Reinforcement Learning (RL)
- Unlicensed Band
- Vehicle-to-Everything (V2X)
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Signal Processing