Deep Reinforcement Learning for Downlink Power Control in Dense 5G Networks
S Saeidian - 2019 - diva-portal.org
This thesis examines the problem of downlink power allocation in dense 5G networks, and
attempts to develop a data-driven solution by employing deep reinforcement learning. We …
attempts to develop a data-driven solution by employing deep reinforcement learning. We …
A novel duplex deep reinforcement learning based RRM framework for next-generation V2X communication networks
Resource management in the next-generation vehicle-to-everything (V2X) communication
networks is a demanding research problem. It is difficult to achieve the best results if the …
networks is a demanding research problem. It is difficult to achieve the best results if the …
Joint task offloading and resource allocation for multi-user and multi-server MEC networks: A deep reinforcement learning approach with multi-branch architecture
Y Sun, Q He - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Abstract Mobile Edge Computing (MEC) is a promising computing paradigm in the context of
5G networks, as it enables the migration of workloads from User Equipments (UEs) to …
5G networks, as it enables the migration of workloads from User Equipments (UEs) to …
Learning based e2e energy efficient in joint radio and nfv resource allocation for 5g and beyond networks
N Gholipoor, A Nouruzi, S Salarhosseini… - arXiv preprint arXiv …, 2021 - arxiv.org
In this paper, we propose a joint radio and core resource allocation framework for NFV-
enabled networks. In the proposed system model, the goal is to maximize energy efficiency …
enabled networks. In the proposed system model, the goal is to maximize energy efficiency …
-OMC: Cost-Aware Deep Learning for Mobile Network Resource Orchestration
Orchestrating resources in 5G and beyond-5G systems will be substantially more complex
than it used to be in previous generations of mobile networks. In order to take full advantage …
than it used to be in previous generations of mobile networks. In order to take full advantage …
Evolution toward 6G wireless networks: A resource management perspective
In this article, we first present the vision, key performance indicators, key enabling
techniques (KETs), and services of 6G wireless networks. Then, we highlight a series of …
techniques (KETs), and services of 6G wireless networks. Then, we highlight a series of …
Resource management in mobile edge computing: a comprehensive survey
With the evolution of 5G and Internet of Things technologies, Mobile Edge Computing (MEC)
has emerged as a major computing paradigm. Compared to cloud computing, MEC …
has emerged as a major computing paradigm. Compared to cloud computing, MEC …
Resource allocation based on deep reinforcement learning in IoT edge computing
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
[HTML][HTML] Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum
Modern computing environments, thanks to the advent of enabling technologies such as
Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary …
Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary …
Multi-dimensional resource orchestration toward edge intelligence in 6g networks
The sixth generation (6G) networks will support a range of intelligent applications such as
face recognition and next-word prediction. However, relying on remote central clouds to …
face recognition and next-word prediction. However, relying on remote central clouds to …