When multiple agents learn to schedule: A distributed radio resource management framework
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 …
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
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
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 …
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 …
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 …
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 …
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
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 …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Deep reinforcement learning for scheduling in cellular networks
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 …
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
Many real-world reinforcement learning tasks require multiple agents to make sequential
decisions under the agents' interaction, where well-coordinated actions among the agents …
decisions under the agents' interaction, where well-coordinated actions among the agents …
Radio resource scheduling with deep pointer networks and reinforcement learning
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 …
resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio …