Implications Of Decentralized Q-learning Resource Allocation In Wireless Networks
2017 · Francesc Wilhelmi, Boris Bellalta, Cristina Cano, et al.
Abstract
Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results. In this work we propose a stateless variation of Q-learning, which we apply to exploit spatial reuse in a wireless network. In particular, we allow networks to modify both their transmission power and the channel used solely based on the experienced throughput. We concentrate in a completely decentralized scenario in which no information about neighbouring nodes is available to the learners. Our results show that although the algorithm is able to find the best-performing actions to enhance aggregate throughput, there is high variability in the throughput experienced by the individual networks. We identify the cause of this variability as the adversarial setting of our setup, in which the most played actions provide intermittent good/poor performance depending on the neighbouring decisions. We also evaluate the effe
Authors
(none)
Tags
Stats
Related papers
- A Multi-agent Multi-environment Mixed Q-learning For Partially Decentralized Wireless Network Optimization (2024)0.00
- Coverage Analysis Of Multi-environment Q-learning Algorithms For Wireless Network Optimization (2024)0.00
- Deep Reinforcement Learning For Distributed And Uncoordinated Cognitive Radios Resource Allocation (2022)0.00
- Resource Management In Wireless Networks Via Multi-agent Deep Reinforcement Learning (2020)16.43
- Deep Reinforcement Learning For Joint Spectrum And Power Allocation In Cellular Networks (2020)0.00
- Leveraging Digital Cousins For Ensemble Q-learning In Large-scale Wireless Networks (2024)6.77
- Offline Reinforcement Learning For Wireless Network Optimization With Mixture Datasets (2023)9.59
- Small-scale-fading-aware Resource Allocation In Wireless Federated Learning (2025)0.00