A Multi-Agent Collaborative Environment Learning Method for UAV Deployment and Resource Allocation

Z.J. Dai

doi:10.1109/TSIPN.2022.3150911

2022-01-01

PRE

DOI：10.1109/TSIPN.2022.3150911

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摘要

En 中文

The dynamic position deployment and resource allocation of the unmanned aerial vehicle (UAV) communication networks has great significance in terms of interference management, coverage enhancement, and capacity improvement. Since the transmission power and energy resources of the UAVs are limited and the actual communication environment is complex and time-varying, it is challenging for the multiple UAVs to dynamically make decisions to ensure the communication performance of the system. Meanwhile, the centralized architecture may generate a certain degree of communication delay and affect communication efficiency. Facing this challenge, a resource allocation algorithm for the UAV networks based on multi-agent collaborative environment learning is proposed. This method is based on a distributed architecture. Each UAV is modeled as an independent agent, which improves the utility of the UAV networks through the dynamic selection decisions of its deployment position, transmission power, and occupied sub-channels. Each UAV learns the mapping of the network information to the position deployment and resource selection decisions based on the reinforcement learning algorithm according to partial of the state information it can observe. For the overall network, a multi-agent reinforcement learning method based on federated learning is designed on the purpose of realizing information interaction and combined dispatching of the UAVs. In the multi-agent system, the framework of federated learning is introduced to realize the sharing of non-privacy data among the UAVs. Simulation results indicate that the proposed method can effectively improve the network utility compared with the multi-agent deep reinforcement learning algorithm without information interaction.

Keyword:

Resource management

Autonomous aerial vehicles

Communication networks

Reinforcement learning

Information processing

Machine learning algorithms

Heuristic algorithms

Federated learning

location deployment

reinforcement learning

resource allocation