Deep reinforcement learning for resource management on network slicing: A survey
JA Hurtado Sánchez, K Casilimas… - Sensors, 2022 - mdpi.com
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …
Federated reinforcement learning: Techniques, applications, and open challenges
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
Efficient federated DRL-based cooperative caching for mobile edge networks
A Tian, B Feng, H Zhou, Y Huang… - … on Network and …, 2022 - ieeexplore.ieee.org
Edge caching has been regarded as a promising technique for low-latency, high-rate data
delivery in future networks, and there is an increasing interest to leverage Machine Learning …
delivery in future networks, and there is an increasing interest to leverage Machine Learning …
Blockchain-based trusted traffic offloading in space-air-ground integrated networks (sagin): A federated reinforcement learning approach
F Tang, C Wen, L Luo, M Zhao… - IEEE Journal on Selected …, 2022 - ieeexplore.ieee.org
In the future era of intelligent networks, communication technology and network architecture
need to be further developed to provide users with high-quality services. The Space-Air …
need to be further developed to provide users with high-quality services. The Space-Air …
A joint reinforcement-learning enabled caching and cross-layer network code in F-RAN with D2D communications
MS Al-Abiad, MZ Hassan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this paper, we leverage reinforcement learning (RL) and cross-layer network coding
(CLNC) for efficiently pre-fetching requested contents to the local caches and delivering …
(CLNC) for efficiently pre-fetching requested contents to the local caches and delivering …
H-HOME: A learning framework of federated FANETs to provide edge computing to future delay-constrained IoT systems
In 6G systems, it will be mandatory that the network is able to support edge computing
powered by Artificial Intelligence (AI) to provide mobile devices with the opportunity of job …
powered by Artificial Intelligence (AI) to provide mobile devices with the opportunity of job …
Multi-domain resource scheduling for simultaneous wireless computing and power transfer in fog radio access network
Future 6G is deemed to provide Simultaneous Wireless cOmputing and Power Transfer
(SWOPT) services for addressing the shortage of local computation and that of energy at …
(SWOPT) services for addressing the shortage of local computation and that of energy at …
Multi-Agent Reinforcement Learning-Based Joint Caching and Routing in Heterogeneous Networks
In this paper, we explore the problem of minimizing transmission cost among cooperative
nodes by jointly optimizing caching and routing in a hybrid network with vital support of …
nodes by jointly optimizing caching and routing in a hybrid network with vital support of …
Cooperative edge caching via multi agent reinforcement learning in fog radio access networks
Q Chang, Y Jiang, FC Zheng… - ICC 2022-IEEE …, 2022 - ieeexplore.ieee.org
In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs)
is investigated. To minimize the content transmission delay, we formulate the cooperative …
is investigated. To minimize the content transmission delay, we formulate the cooperative …
Federated Online Learning Aided Multi-Objective Proactive Caching in Heterogeneous Edge Networks
To address the drastic increase in multimedia traffic volume, mobile edge caching (MEC)
has been exploited to reduce redundant data transmissions by equipping computation and …
has been exploited to reduce redundant data transmissions by equipping computation and …