When multiple agents learn to schedule: A distributed radio resource management framework

N Naderializadeh, J Sydir, M Simsek… - arXiv preprint arXiv …, 2019 - arxiv.org
Interference among concurrent transmissions in a wireless network is a key factor limiting
the system performance. One way to alleviate this problem is to manage the radio resources …

Resource management in wireless networks via multi-agent deep reinforcement learning

N Naderializadeh, JJ Sydir, M Simsek… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …

Smart scheduling based on deep reinforcement learning for cellular networks

J Wang, C Xu, R Li, Y Ge, J Wang - 2021 IEEE 32nd Annual …, 2021 - ieeexplore.ieee.org
To improve the system performance towards the Shannon limit, advanced radio resource
management mechanisms play a fundamental role. In particular, scheduling should receive …

MERGE: Meta Reinforcement Learning for Tunable RL Agents at the Edge

S Tripathi, CF Chiasserini - GLOBECOM 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
The efficient allocation of radio resources is an essential trait of 5G/6G radio access
networks (RANs), as they are called to meet diverse QoS requirements of highly demanding …

Distributed intelligence: A verification for multi-agent DRL-based multibeam satellite resource allocation

X Liao, X Hu, Z Liu, S Ma, L Xu, X Li… - IEEE …, 2020 - ieeexplore.ieee.org
Centralized radio resource management method puts all of the computational burdens in an
agent, which is unbearable with the increasing of data dimensionality. This letter focuses on …

Deep reinforcement learning based traffic-and channel-aware OFDMA resource allocation

R Balakrishnan, K Sankhe… - 2019 IEEE Global …, 2019 - ieeexplore.ieee.org
Efficient radio resource allocation is a fundamental optimization problem for wireless
networks, and has been widely studied in the past. However, wireless systems are evolving …

Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey

A Alwarafy, M Abdallah, BS Ciftler, A Al-Fuqaha… - arXiv preprint arXiv …, 2021 - arxiv.org
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …

Deep reinforcement learning for scheduling in cellular networks

J Wang, C Xu, Y Huangfu, R Li, Y Ge… - 2019 11th International …, 2019 - ieeexplore.ieee.org
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in
both industry and academia. A common solution is to replace partial or even all modules in …

Learning to schedule communication in multi-agent reinforcement learning

D Kim, S Moon, D Hostallero, WJ Kang, T Lee… - arXiv preprint arXiv …, 2019 - arxiv.org
Many real-world reinforcement learning tasks require multiple agents to make sequential
decisions under the agents' interaction, where well-coordinated actions among the agents …

Radio resource scheduling with deep pointer networks and reinforcement learning

F Al-Tam, A Mazayev, N Correia… - 2020 IEEE 25th …, 2020 - ieeexplore.ieee.org
This article presents an artificial intelligence (AI) adaptable solution to handle the radio
resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio …