Modern deep reinforcement learning algorithms
S Ivanov, A D'yakonov - arXiv preprint arXiv:1906.10025, 2019 - arxiv.org
Recent advances in Reinforcement Learning, grounded on combining classical theoretical
results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence …
results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence …
Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey
Nowadays, many research studies and industrial investigations have allowed the integration
of the Internet of Things (IoT) in current and future networking applications by deploying a …
of the Internet of Things (IoT) in current and future networking applications by deploying a …
[HTML][HTML] Adaptive wireless network management with multi-agent reinforcement learning
Wireless networks are trending towards large scale systems, containing thousands of nodes,
with multiple co-existing applications. Congestion is an inevitable consequence of this scale …
with multiple co-existing applications. Congestion is an inevitable consequence of this scale …
Multi-Tier Deep Reinforcement Learning for Non-Terrestrial Networks
To provide global coverage and ubiquitous wireless services, non-terrestrial networks
(NTNs) composed of space-tier, air-tier, and ground-tier stations, have been regarded as a …
(NTNs) composed of space-tier, air-tier, and ground-tier stations, have been regarded as a …
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 …
ReCARL: Resource allocation in cloud RANs with deep reinforcement learning
Cloud radio access networks (CRANs) have become a key enabling technique for the next
generation wireless communications. Resource allocation in CRANs still needs to be further …
generation wireless communications. Resource allocation in CRANs still needs to be further …
Dynamic power allocation in cellular network based on multi-agent double deep reinforcement learning
Y Yang, F Li, X Zhang, Z Liu, KY Chan - Computer Networks, 2022 - Elsevier
With the massively growing wireless data traffic, the dense cellular network has become a
significant mode for the fifth generation (5G) network. To fully utilize the benefit of the cellular …
significant mode for the fifth generation (5G) network. To fully utilize the benefit of the cellular …
Deep reinforcement learning-assisted energy harvesting wireless networks
J Ye, H Gharavi - IEEE transactions on green communications …, 2020 - ieeexplore.ieee.org
Heterogeneous ultra-dense networking (HUDN) with energy harvesting technology is a
promising approach to deal with the ever-growing traffic that can severely impact the power …
promising approach to deal with the ever-growing traffic that can severely impact the power …
Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review
M Khani, S Jamali, MK Sohrabi… - Transactions on …, 2024 - Wiley Online Library
This paper reviews recent research on resource allocation in 5G cloud‐based radio access
networks (C‐RAN) using deep reinforcement learning (DRL) algorithms. It explores the …
networks (C‐RAN) using deep reinforcement learning (DRL) algorithms. It explores the …
Optimizing wireless systems using unsupervised and reinforced-unsupervised deep learning
Resource allocation and transceivers in wireless networks are usually designed by solving
optimization problems subject to specific constraints, which can be formulated as variable or …
optimization problems subject to specific constraints, which can be formulated as variable or …