Deep Reinforcement Learning for Downlink Scheduling in 5G and Beyond Networks: A Review
M Seguin, A Omer, M Koosha… - 2023 IEEE 34th …, 2023 - ieeexplore.ieee.org
The coexistence of a wide variety of different applications with diverse Quality of Service
(QoS) and Quality of Experience (QoE) requirements calls for more sophisticated radio …
(QoS) and Quality of Experience (QoE) requirements calls for more sophisticated radio …
A comprehensive survey on radio resource management in 5G HetNets: Current solutions, future trends and open issues
The 5G network technologies are intended to accommodate innovative services with a large
influx of data traffic with lower energy consumption and increased quality of service and user …
influx of data traffic with lower energy consumption and increased quality of service and user …
Multi-agent deep reinforcement learning for resource allocation in the multi-objective HetNet
H Nie, S Li, Y Liu - 2021 International Wireless …, 2021 - ieeexplore.ieee.org
Resource allocation in a heterogeneous network is an NP-hard problem, especially in 5G
network scenarios. Multiobjective optimization in resource allocation is a challenging task …
network scenarios. Multiobjective optimization in resource allocation is a challenging task …
A deep-learning-based radio resource assignment technique for 5G ultra dense networks
Recently, deep learning has emerged as a state-of-the-art machine learning technique with
promising potential to drive significant breakthroughs in a wide range of research areas. The …
promising potential to drive significant breakthroughs in a wide range of research areas. The …
Deep reinforcement learning for edge computing and resource allocation in 5G beyond
Y Dai, D Xu, K Zhang, Y Lu… - 2019 IEEE 19th …, 2019 - ieeexplore.ieee.org
By extending computation capacity to the edge of wireless networks, edge computing has
the potential to enable computation-intensive and delay-sensitive applications in 5G and …
the potential to enable computation-intensive and delay-sensitive applications in 5G and …
Green deep reinforcement learning for radio resource management: Architecture, algorithm compression, and challenges
Artificial intelligence (AI) heralds a step-change in wireless networks but may also cause
irreversible environmental damage due to its high energy consumption. Here, we address …
irreversible environmental damage due to its high energy consumption. Here, we address …
Radio resource management in multi-numerology 5G new radio featuring network slicing
5G New Radio (NR) introduces several key features to support the new emerging vertical
industry use-cases, mainly:(1) Different numerology that gives more flexibility in managing …
industry use-cases, mainly:(1) Different numerology that gives more flexibility in managing …
Toward a smart resource allocation policy via artificial intelligence in 6G networks: Centralized or decentralized?
In this paper, we design a new smart softwaredefined radio access network (RAN)
architecture with important properties like flexibility and traffic awareness for sixth generation …
architecture with important properties like flexibility and traffic awareness for sixth generation …
Learn to schedule (LEASCH): A deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer
Network management tools are usually inherited from one generation to another. This was
successful since these tools have been kept in check and updated regularly to fit new …
successful since these tools have been kept in check and updated regularly to fit new …
[HTML][HTML] Energy-efficient joint resource allocation in 5G HetNet using Multi-Agent Parameterized Deep Reinforcement learning
Small cells are a promising technique to improve the capacity and throughput of future
wireless networks. However, user association and power allocation in heterogeneous …
wireless networks. However, user association and power allocation in heterogeneous …