Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks
University of Southampton · Queen Mary University of London
Abstract
Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing both cost-effective and on-demand wireless communications. This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. More particularly, each UAV communicates with a ground user by automatically selecting its communicating user, power level and subchannel without any information exchange among UAVs. To model the dynamics and uncertainty in environments, we formulate the long-term resource allocation problem as a stochastic game for maximizing the expected rewards, where each UAV becomes a learning agent and each resource…
Citation impact
- FWCI
- 431.95
- Percentile
- 100%
- References
- 55
Authors
3Topics & keywords
- Reinforcement learning
- Computer science
- Resource allocation
- Base station
- Information exchange
- Resource management (computing)
- Resource (disambiguation)
- Distributed computing