[HTML][HTML] Value is king: the mecforge deep reinforcement learning solution for resource management in 5g and beyond
F Poltronieri, C Stefanelli, N Suri… - Journal of Network and …, 2022 - Springer
Multi-access edge computing (MEC) is a key enabler to fulfill the promises of a new
generation of immersive and low-latency services in 5G and Beyond networks. MEC …
generation of immersive and low-latency services in 5G and Beyond networks. MEC …
The Cost of Learning: Efficiency vs. Efficacy of Learning-Based RRM for 6G
In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution
to automatically learn efficient resource management strategies in complex networks. In …
to automatically learn efficient resource management strategies in complex networks. In …
AI-based resource provisioning of IoE services in 6G: A deep reinforcement learning approach
Currently, researchers have motivated a vision of 6G for empowering the new generation of
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the
central network, incorporating edge nodes close to end devices. This expansion facilitates …
central network, incorporating edge nodes close to end devices. This expansion facilitates …
[HTML][HTML] Multi-user edge service orchestration based on Deep Reinforcement Learning
The fifth generation (5G) of mobile network offers a remarkable degree of flexibility to mobile
operators, enabling them to provide users with effective and tailored network services …
operators, enabling them to provide users with effective and tailored network services …
[HTML][HTML] Deep Reinforcement Learning for QoS provisioning at the MAC layer: A Survey
Abstract Quality of Service (QoS) provisioning is based on various network management
techniques including resource management and medium access control (MAC). Various …
techniques including resource management and medium access control (MAC). Various …
Deep reinforcement learning‐based resource allocation in multi‐access edge computing
Network architects and engineers face challenges in meeting the increasing complexity and
low‐latency requirements of various services. To tackle these challenges, multi‐access …
low‐latency requirements of various services. To tackle these challenges, multi‐access …
Towards Massive Distribution of Intelligence for 6G Network Management using Double Deep Q-Networks
S Majumdar, S Schwarzmann… - … on Network and …, 2023 - ieeexplore.ieee.org
In future 6G networks, the deployment of network elements is expected to be highly
distributed, going beyond the level of distribution of existing 5G deployments. To fully exploit …
distributed, going beyond the level of distribution of existing 5G deployments. To fully exploit …
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 …
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 …