Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …
led to multiple successes in solving sequential decision-making problems in various …
[HTML][HTML] 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 …
[HTML][HTML] From 5G to 6G technology: meets energy, internet-of-things and machine learning: a survey
Due to the rapid development of the fifth-generation (5G) applications, and increased
demand for even faster communication networks, we expected to witness the birth of a new …
demand for even faster communication networks, we expected to witness the birth of a new …
[HTML][HTML] An overview of reinforcement learning algorithms for handover management in 5G ultra-dense small cell networks
The fifth generation (5G) wireless technology emerged with marvelous effort to state, design,
deployment and standardize the upcoming wireless network generation. Artificial …
deployment and standardize the upcoming wireless network generation. Artificial …
Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges
Sixth generation (6G) internet of things (IoT) networks will modernize the applications and
satisfy user demands through implementing smart and automated systems. Intelligence …
satisfy user demands through implementing smart and automated systems. Intelligence …
[HTML][HTML] Machine learning-based zero-touch network and service management: A survey
J Gallego-Madrid, R Sanchez-Iborra, PM Ruiz… - Digital Communications …, 2022 - Elsevier
The exponential growth of mobile applications and services during the last years has
challenged the existing network infrastructures. Consequently, the arrival of multiple …
challenged the existing network infrastructures. Consequently, the arrival of multiple …
[HTML][HTML] Applicability of deep reinforcement learning for efficient federated learning in massive IoT communications
To build intelligent model learning in conventional architecture, the local data are required to
be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage …
be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
[HTML][HTML] The role of artificial intelligence driven 5G networks in COVID-19 outbreak: Opportunities, challenges, and future outlook
There is no doubt that the world is currently experiencing a global pandemic that is
reshaping our daily lives as well as the way business activities are being conducted. With …
reshaping our daily lives as well as the way business activities are being conducted. With …
[HTML][HTML] A Systematic Literature Review of reinforcement learning-based knowledge graph research
Abstract Knowledge graphs (KGs) model entities or concepts and their relations in a
structural manner. The incompleteness has turned out to be the main challenge that hinders …
structural manner. The incompleteness has turned out to be the main challenge that hinders …