[HTML][HTML] Resource allocation in wireless networks with federated learning: Network adaptability and learning acceleration
HS Lee, DE Lee - ICT Express, 2022 - Elsevier
Deep reinforcement learning can effectively address resource allocation in wireless
networks. However, its learning speed may be slower in more complex networks and a new …
networks. However, its learning speed may be slower in more complex networks and a new …
Deep reinforcement learning based wireless network optimization: A comparative study
There is a growing interest in applying deep reinforcement learning (DRL) methods to
optimizing the operation of wireless networks. In this paper, we compare three state of the art …
optimizing the operation of wireless networks. In this paper, we compare three state of the art …
Survey on reinforcement learning applications in communication networks
Y Qian, J Wu, R Wang, F Zhu… - … of Communications and …, 2019 - ieeexplore.ieee.org
In recent years, intelligent communication has drawn huge research efforts in both academia
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …
Marconi-rosenblatt framework for intelligent networks (mr-inet gym): For rapid design and implementation of distributed multi-agent reinforcement learning solutions for …
Abstract We present the Marconi-Rosenblatt Framework for Intelligent Networks (MR-iNet
Gym) an open-source architecture designed for accelerating research and development of …
Gym) an open-source architecture designed for accelerating research and development of …
Offline reinforcement learning for wireless network optimization with mixture datasets
The recent development of reinforcement learning (RL) has boosted the adoption of online
RL for wireless radio resource management (RRM). However, online RL algorithms require …
RL for wireless radio resource management (RRM). However, online RL algorithms require …
Applications of multi-agent reinforcement learning in future internet: A comprehensive survey
Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
Enabling robust DRL-driven networking systems via teacher-student learning
Y Zheng, L Lin, T Zhang, H Chen… - IEEE Journal on …, 2021 - ieeexplore.ieee.org
The past few years have witnessed a surge of interest towards deep reinforcement learning
(DRL) in computer networks. With extraordinary ability of feature extraction, DRL has the …
(DRL) in computer networks. With extraordinary ability of feature extraction, DRL has the …
Deep reinforcement learning paradigm for dense wireless networks in smart cities
Wireless local area networks (WLANs) are widely deployed for Internet-centric data
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …
Resource allocation in wireless networks with deep reinforcement learning: A circumstance-independent approach
In the conventional approaches using reinforcement learning (RL) for resource allocation in
wireless networks, the structure of the policy depends on network circumstances such as the …
wireless networks, the structure of the policy depends on network circumstances such as the …
Coordinated reinforcement learning for optimizing mobile networks
Mobile networks are composed of many base stations and for each of them many
parameters must be optimized to provide good services. Automatically and dynamically …
parameters must be optimized to provide good services. Automatically and dynamically …