AI models for green communications towards 6G
Green communications have always been a target for the information industry to alleviate
energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is …
energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is …
Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing
Mobile-edge computing (MEC) has emerged as a promising computing paradigm in the 5G
architecture, which can empower user equipments (UEs) with computation and energy …
architecture, which can empower user equipments (UEs) with computation and energy …
Lyapunov-guided deep reinforcement learning for stable online computation offloading in mobile-edge computing networks
Opportunistic computation offloading is an effective method to improve the computation
performance of mobile-edge computing (MEC) networks under dynamic edge environment …
performance of mobile-edge computing (MEC) networks under dynamic edge environment …
Energy-efficient joint task offloading and resource allocation in OFDMA-based collaborative edge computing
Mobile edge computing (MEC) is an emergent architecture, which brings computation and
storage resources to the edge of mobile network and provides rich services and applications …
storage resources to the edge of mobile network and provides rich services and applications …
Edge intelligence: A computational task offloading scheme for dependent IoT application
Computational offloading, as an effective way to extend the capability of resource-limited
edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for …
edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for …
Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
Reinforcement learning methods for computation offloading: a systematic review
Z Zabihi, AM Eftekhari Moghadam… - ACM Computing …, 2023 - dl.acm.org
Today, cloud computation offloading may not be an appropriate solution for delay-sensitive
applications due to the long distance between end-devices and remote datacenters. In …
applications due to the long distance between end-devices and remote datacenters. In …
Deep reinforcement learning-based task scheduling in iot edge computing
Edge computing (EC) has recently emerged as a promising paradigm that supports resource-
hungry Internet of Things (IoT) applications with low latency services at the network edge …
hungry Internet of Things (IoT) applications with low latency services at the network edge …
Multitask offloading strategy optimization based on directed acyclic graphs for edge computing
With the advancement of the user application service demands, the IoT system tends to
offload the tasks to the edge server for execution. Most of the current studies on edge …
offload the tasks to the edge server for execution. Most of the current studies on edge …