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 …
Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues
In the IoT-based systems, the fog computing allows the fog nodes to offload and process
tasks requested from IoT-enabled devices in a distributed manner instead of the centralized …
tasks requested from IoT-enabled devices in a distributed manner instead of the centralized …
Edge-enabled two-stage scheduling based on deep reinforcement learning for internet of everything
Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic,
which is playing an increasingly indispensable role in modern intelligent applications. These …
which is playing an increasingly indispensable role in modern intelligent applications. These …
Artificial-intelligence-enabled intelligent 6G networks
With the rapid development of smart terminals and infrastructures, as well as diversified
applications (eg, virtual and augmented reality, remote surgery and holographic projection) …
applications (eg, virtual and augmented reality, remote surgery and holographic projection) …
Deep federated Q-learning-based network slicing for industrial IoT
Fifth generation and beyond networks are envisioned to support multi industrial Internet of
Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network …
Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network …
Artificial intelligence implication on energy sustainability in Internet of Things: A survey
The massive number of Internet of Things (IoT) devices connected to the Internet is
continuously increasing. The operations of these devices rely on consuming huge amounts …
continuously increasing. The operations of these devices rely on consuming huge amounts …
Reinforcement learning based capacity management in multi-layer satellite networks
C Jiang, X Zhu - IEEE Transactions on Wireless …, 2020 - ieeexplore.ieee.org
The development of satellite networks is drawing much more attention in recent years due to
the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit …
the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit …
A survey of scheduling in 5g urllc and outlook for emerging 6g systems
Future wireless communication is expected to be a paradigm shift from three basic service
requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra …
requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra …
Deep reinforcement learning for adaptive network slicing in 5G for intelligent vehicular systems and smart cities
Intelligent vehicular systems and smart city applications are the fastest growing Internet-of-
Things (IoT) implementations at a compound annual growth rate of 30%. In view of the …
Things (IoT) implementations at a compound annual growth rate of 30%. In view of the …
Home energy recommendation system (hers): A deep reinforcement learning method based on residents' feedback and activity
Smart home appliances can take command and act intelligently, making them suitable for
implementing optimization techniques. Artificial intelligence (AI) based control of these smart …
implementing optimization techniques. Artificial intelligence (AI) based control of these smart …