Thirty years of machine learning: The road to Pareto-optimal wireless networks
Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …
complex compelling applications both in military and civilian fields, where the users are able …
Multi-RAT access based on multi-agent reinforcement learning
The integration of multiple Radio Access Technologies (RATs) of licensed or unlicensed
bands is considered as a cost-efficient way to greatly increase network capacity of mobile …
bands is considered as a cost-efficient way to greatly increase network capacity of mobile …
Learning-assisted clustered access of 5G/B5G networks to unlicensed spectrum
License-assisted access (LAA) to unlicensed spectrum is a potential solution to improve the
resource availability and system scalability of 5G/B5G networks. Challenges arise from …
resource availability and system scalability of 5G/B5G networks. Challenges arise from …
Marconi-rosenblatt framework for intelligent networks (mr-inet gym): For rapid design and implementation of distributed multi-agent reinforcement learning solutions for …
Abstract We present the Marconi-Rosenblatt Framework for Intelligent Networks (MR-iNet
Gym) an open-source architecture designed for accelerating research and development of …
Gym) an open-source architecture designed for accelerating research and development of …
FAQ: A fuzzy-logic-assisted Q learning model for resource allocation in 6G V2X
This research proposes a dynamic resource allocation method for vehicle-to-everything
(V2X) communications in the sixth generation (6G) cellular networks. Cellular V2X (C-V2X) …
(V2X) communications in the sixth generation (6G) cellular networks. Cellular V2X (C-V2X) …
AI-enabled radio resource allocation in 5G for URLLC and eMBB users
M Elsayed, M Erol-Kantarci - 2019 IEEE 2nd 5G World Forum …, 2019 - ieeexplore.ieee.org
The fifth generation (5G) network is expected to accommodate heterogeneous traffic with
diverse QoS demands. In this paper, we address the coexistence of Ultra-Reliable Low …
diverse QoS demands. In this paper, we address the coexistence of Ultra-Reliable Low …
Federated reinforcement learning in iot: Applications, opportunities and open challenges
The internet of things (IoT) represents a disruptive concept that has been changing society in
several ways. There have been several successful applications of IoT in the industry. For …
several ways. There have been several successful applications of IoT in the industry. For …
AI-based radio resource allocation in support of the massive heterogeneity of 6G networks
There is a consensus in industry and academia that 6G wireless networks will incorporate
massive heterogeneous radio access technologies (RATs) in order to cater to the high …
massive heterogeneous radio access technologies (RATs) in order to cater to the high …
An overview of data-importance aware radio resource management for edge machine learning
The 5G network connecting billions of Internet of things (IoT) devices will make it possible to
harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization …
harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization …
Enhancing federated learning with spectrum allocation optimization and device selection
Machine learning (ML) is a widely accepted means for supporting customized services for
mobile devices and applications. Federated Learning (FL), which is a promising approach to …
mobile devices and applications. Federated Learning (FL), which is a promising approach to …