- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | CUI Yaping, YING Zhaopeng, HE Peng, ZHENG Yufeng, WU Dapeng, WANG Ruyan, CHEN Luo. Ultra-Reliable Low-Latency Communication Multi-Unmanned Aerial Vehicle Network Assisted by Intelligent Reflecting Surface in Air[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 907-916. doi: 10.3969/j.issn.0258-2724.20230288
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In the ultra-reliable low-latency communications (URLLC) multi-unmanned aerial vehicle (UAV) network, to satisfy the ultra-reliable low-latency requirements, the intelligent reflecting surface (IRS) in the air was introduced to assist in communication, and a multi-intelligent deep deterministic policy gradient (MADDPG) method was proposed. First, the URLLC multi-UAV system model was established, in which multiple primary UAVs acted as airborne base stations to provide services for multiple ground users, and one auxiliary UAV carried an IRS as an airborne passive relay to assist the primary UAV in communicating with the ground users. The composite channel model and the total energy model were established respectively by considering multiple channel conditions and energy consumption. Second, the problem was analyzed to minimize the total decoding error rate by jointly optimizing the communication schedule, IRS phase shift, and block length while satisfying the constraints of finite block length, UAV energy, and IRS phase shift. Finally, the MADDPG framework with centralized training and distributed execution was designed by considering the delay-sensitive constraints of centralized training in URLLC scenarios and the energy constraints of distributed training under the resource limitations of UAVs. The results show that the total decoding error rate decreases sharply with the increase in IRS units. Meanwhile, the total decoding error rate decreases with the increase in block length and transmitted power. To be specific, the total decoding error rate decreases by 91.1% as every 20 symbols are added to the block length.
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