DRL-based Multi-UAV trajectory optimization for ultra-dense small cells
In this paper, we propose a deep reinforcement learning (DRL) based unmanned aerial vehicles (UAV)-assisted trajectory optimization for ultra-dense small cell networks. We assume that each UAV is equipped with a sensing radio to obtain distance information to the UEs and other UAVs in the network wh...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | ICT Express |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959523000619 |
| Summary: | In this paper, we propose a deep reinforcement learning (DRL) based unmanned aerial vehicles (UAV)-assisted trajectory optimization for ultra-dense small cell networks. We assume that each UAV is equipped with a sensing radio to obtain distance information to the UEs and other UAVs in the network which are used to update the UAV’s trajectory. The proposed DRL-based system selects the optimal joint control actions for the UAVs that maximizes the system sum-rate. The simulation results show that the proposed DRL-based UAV controller provides fast UAV placement in the network with a high system performance when compared with the benchmark schemes. |
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| ISSN: | 2405-9595 |