Leveraging UAVs for coverage in cell-free vehicular networks: A deep reinforcement learning approach
The success in transitioning towards smart cities relies on the availability of information and
communication technologies that meet the demands of this transformation. The terrestrial …
communication technologies that meet the demands of this transformation. The terrestrial …
Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach
The developments of connected vehicles are heavily influenced by information and
communications technologies, which have fueled a plethora of innovations in various areas …
communications technologies, which have fueled a plethora of innovations in various areas …
Meta-hierarchical reinforcement learning (MHRL)-based dynamic resource allocation for dynamic vehicular networks
With the rapid development of vehicular networks, there is an increasing demand for
extensive networking, computting, and caching resources. How to allocate multiple …
extensive networking, computting, and caching resources. How to allocate multiple …
[HTML][HTML] Role of machine learning in resource allocation strategy over vehicular networks: a survey
I Nurcahyani, JW Lee - Sensors, 2021 - mdpi.com
The increasing demand for smart vehicles with many sensing capabilities will escalate data
traffic in vehicular networks. Meanwhile, available network resources are limited. The …
traffic in vehicular networks. Meanwhile, available network resources are limited. The …
[HTML][HTML] An improved deep reinforcement learning routing technique for collision-free VANET
P Upadhyay, V Marriboina, SJ Goyal, S Kumar… - Scientific Reports, 2023 - nature.com
Abstract Vehicular Adhoc Networks (VANETs) is an emerging field that employs a wireless
local area network (WLAN) characterized by an ad-hoc topology. Vehicular Ad Hoc …
local area network (WLAN) characterized by an ad-hoc topology. Vehicular Ad Hoc …
Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges
S Khatri, H Vachhani, S Shah, J Bhatia… - Peer-to-Peer Networking …, 2021 - Springer
Low latency in communication among the vehicles and RSUs, smooth traffic flow, and road
safety are the major concerns of the Intelligent Transportation Systems. Vehicular Ad hoc …
safety are the major concerns of the Intelligent Transportation Systems. Vehicular Ad hoc …
Machine learning for next‐generation intelligent transportation systems: A survey
T Yuan, W da Rocha Neto… - Transactions on …, 2022 - Wiley Online Library
Intelligent transportation systems, or ITS for short, includes a variety of services and
applications such as road traffic management, traveler information systems, public transit …
applications such as road traffic management, traveler information systems, public transit …
Unified automatic control of vehicular systems with reinforcement learning
Emerging vehicular systems with increasing proportions of automated components present
opportunities for optimal control to mitigate congestion and increase efficiency. There has …
opportunities for optimal control to mitigate congestion and increase efficiency. There has …
Multi-agent deep reinforcement learning-empowered channel allocation in vehicular networks
AS Kumar, L Zhao, X Fernando - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Channel allocation has a direct and profound impact on the performance of vehicle-to-
everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is …
everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is …
Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges
Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …
mobile data traffic and support an increasingly high density of mobile users involving a …