Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems
The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …
solutions to replace the traditional model-driven approaches that proved to be not rich …
Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
6G and beyond: The future of wireless communications systems
6G and beyond will fulfill the requirements of a fully connected world and provide ubiquitous
wireless connectivity for all. Transformative solutions are expected to drive the surge for …
wireless connectivity for all. Transformative solutions are expected to drive the surge for …
Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks
Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to
the edge of Internet of Things (IoT) system. However, the static edge server deployment may …
the edge of Internet of Things (IoT) system. However, the static edge server deployment may …
Spectrum sharing in vehicular networks based on multi-agent reinforcement learning
This paper investigates the spectrum sharing problem in vehicular networks based on multi-
agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the …
agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the …
Deep reinforcement learning based resource allocation for V2V communications
In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-
to-vehicle (V2V) communications based on deep reinforcement learning, which can be …
to-vehicle (V2V) communications based on deep reinforcement learning, which can be …
Vehicular fog computing: Enabling real-time traffic management for smart cities
Fog computing extends the facility of cloud computing from the center to edge networks.
Although fog computing has the advantages of location awareness and low latency, the …
Although fog computing has the advantages of location awareness and low latency, the …
Offloading in internet of vehicles: A fog-enabled real-time traffic management system
Fog computing has been merged with Internet of Vehicle (IoV) systems to provide
computational resources for end users, by which low latency can be guaranteed. In this …
computational resources for end users, by which low latency can be guaranteed. In this …
A survey of collaborative machine learning using 5G vehicular communications
SV Balkus, H Wang, BD Cornet… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
By enabling autonomous vehicles (AVs) to share data while driving, 5G vehicular
communications allow AVs to collaborate on solving common autonomous driving tasks …
communications allow AVs to collaborate on solving common autonomous driving tasks …
A survey of 5G network systems: challenges and machine learning approaches
Abstract 5G cellular networks are expected to be the key infrastructure to deliver the
emerging services. These services bring new requirements and challenges that obstruct the …
emerging services. These services bring new requirements and challenges that obstruct the …