Deep reinforcement learning for computation and communication resource allocation in multiaccess MEC assisted railway IoT networks
J Xu, B Ai, L Chen, Y Cui… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to
support compute-intensive and delay-sensitive applications in railway Internet of Things …
support compute-intensive and delay-sensitive applications in railway Internet of Things …
Applications of multi-agent reinforcement learning in future internet: A comprehensive survey
Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
Computation-Efficient Offloading and Power Control for MEC in IoT Networks by Meta Reinforcement Learning
Due to the proliferation of devices and the availability of computing servers, mobile edge
computing (MEC) has gained popularity in executing various computational tasks. MEC …
computing (MEC) has gained popularity in executing various computational tasks. MEC …
A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN
Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for
realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in …
realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in …
Stacked autoencoder-based deep reinforcement learning for online resource scheduling in large-scale MEC networks
An online resource scheduling framework is proposed for minimizing the sum of weighted
task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision …
task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision …
Multi-agent deep reinforcement learning based resource allocation for ultra-reliable low-latency internet of controllable things
Y Xiao, Y Song, J Liu - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
As a promising technology in the 5G era, the artificial intelligence (AI) enabled Internet of
controllable things (IoCT) is expected to be an integral part of heterogeneous networks …
controllable things (IoCT) is expected to be an integral part of heterogeneous networks …
Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach
A Qadeer, MJ Lee - IEEE Access, 2023 - ieeexplore.ieee.org
Edge Cloud (EC) empowers the beyond 5G (B5G) wireless networks to cope with large-
scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing …
scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing …
Congestion-aware routing in dynamic iot networks: A reinforcement learning approach
H Farag, Č Stefanovič - 2021 IEEE Global Communications …, 2021 - ieeexplore.ieee.org
The innovative services empowered by the Internet of Things (IoT) require a seamless and
reliable wireless infras-tructure that enables communications within heterogeneous and …
reliable wireless infras-tructure that enables communications within heterogeneous and …
A Comparative Analysis of Deep Reinforcement Learning-based xApps in O-RAN
The highly heterogeneous ecosystem of Next Generation (NextG) wireless communication
systems calls for novel networking paradigms where functionalities and operations can be …
systems calls for novel networking paradigms where functionalities and operations can be …
A deep reinforcement learning based offloading game in edge computing
Edge computing is a new paradigm to provide strong computing capability at the edge of
pervasive radio access networks close to users. A critical research challenge of edge …
pervasive radio access networks close to users. A critical research challenge of edge …