Distributed User Connectivity Maximization in UAV-Based Communication Networks

Saugat Tripathi,Ran Zhang,Miao Wang

IEEE CONFERENCE ON GLOBAL COMMUNICATIONS, GLOBECOM(2023)

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摘要
Multi-agent reinforcement learning has been applied to Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) to effectively solve the problem of time-coupled sequential decision making while achieving scalability. Nevertheless, a transverse comparison on the impact of different levels of inter-agent information exchange on the learning convergence has not been well studied. In this work, we study a distributed user connectivity maximization problem in a UCN, aiming to obtain a trajectory design to optimally guide UAVs' movements in a time horizon to maximize the accumulated number of connected users. Specifically, the problem is first formulated into a time-coupled mixed-integer non-convex optimization problem. A two-stage user association policy is proposed to determine the UAV-user connectivity. A multi-agent deep Q learning algorithm is then designed to solve the optimization, featuring four different levels of information exchange and reward function design. Simulations are conducted to compare the convergence speed and total number of connected users per episode between different levels. The results show that exchanging state information with a deliberated task-specific reward function design yields the best convergence performance in both cases of stationary and dynamic user distributions.
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关键词
User Connections,Learning Algorithms,Information Exchange,Unmanned Aerial Vehicles,Central Level,Non-convex Problem,Reward Function,Convergence Performance,Q-learning,User Association,Impact Of Different Levels,Deep Q-learning,Multi-agent Reinforcement Learning,Trajectory Design,Time Step,State Space,Global Status,Channel Gain,Local Observations,Target Network,Deep Q-network,Resource Block,Unmanned Aerial Vehicle Position,Individual Reward,Ground Users,Dimension Of The State Space,Stable Convergence,Convergence Value,Reinforcement Learning Agent,Distributed Manner
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