Resource allocation in V2X communication: State-of-the-art and research challenges
A Nair, S Tanwar - Physical Communication, 2024 - Elsevier
This paper explores the multifaceted domain of resource allocation (RA) in vehicle-to-
everything (V2X) communication, emphasizing its pivotal role in advancing intelligent …
everything (V2X) communication, emphasizing its pivotal role in advancing intelligent …
Mutual Interference-Aware Throughput Enhancement in Massive IoT: A Graph Reinforcement Learning Framework
F Yang, C Yang, J Huang, O Alfarraj… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
As the number of devices increases dramatically in the Internet of Things (IoT), features of
dense deployment of massive devices generate mutual interference in communication …
dense deployment of massive devices generate mutual interference in communication …
Reinforcement learning-driven dynamic obstacle avoidance for mobile robot trajectory tracking
In this work, we propose a trajectory tracking method based on optimized Q-Learning (QL),
which has real-time obstacle avoidance capability, for controlling wheeled mobile robots in …
which has real-time obstacle avoidance capability, for controlling wheeled mobile robots in …
A Scalable Mean-Field MARL Framework for Multi-Objective V2X Resource Allocation
Resource allocation in dense vehicle-to-everything (V2X) communication networks poses
intricate challenges due to scalability issues, dynamic environments, and diverse quality of …
intricate challenges due to scalability issues, dynamic environments, and diverse quality of …
Unsupervised Power Allocation Based on Combination of Edge Aggregated Graph Attention Network with Deep Unfolded WMMSE
To address the challenge of optimizing power distribution for transmitter-receiver pairs within
ad hoc wireless networks, deep learning techniques have been employed to navigate the …
ad hoc wireless networks, deep learning techniques have been employed to navigate the …
Relay Selection and Resource Allocation for Ad Hoc Networks-Assisted Train-to-Train Communications: A Federated Soft Actor-Critic Approach
With the growing demand for various applications in intelligent rail transit, the burden of
information transmission is aggravated. Meanwhile, high-mobility trains, time-varying …
information transmission is aggravated. Meanwhile, high-mobility trains, time-varying …
Deep Deterministic Policy Gradient-Based Intelligent Task Offloading for Vehicular Computing With Priority Experience Playback
With the development of Internet of Vehicles (IoV) technology, users' demand for low latency
and high-quality network services has increased. However, executing large computing tasks …
and high-quality network services has increased. However, executing large computing tasks …
MEC-enabled resource allocation in Internet of Vehicles
Y Xiao, J Zhao, Q Zhang, Y Huang, H Quan… - Physical Communication, 2024 - Elsevier
Mobile edge computing (MEC), a promising technology, is widely used in the context of the
Internet of Vehicles (IoV) owing to its robust computing capabilities and proximity to mobile …
Internet of Vehicles (IoV) owing to its robust computing capabilities and proximity to mobile …
[HTML][HTML] Distributed Resources Allocation Method for Space–Ground Integrated Mobile Communication System
T Zhao, Z Li - Sensors, 2024 - mdpi.com
This paper presents an innovative approach towards space–ground integrated
communication systems by combining terrestrial cellular networks, UAV networks, and …
communication systems by combining terrestrial cellular networks, UAV networks, and …
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning
MS Islam, S Das, SK Gottipati, W Duguay… - arXiv preprint arXiv …, 2023 - arxiv.org
Recent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning
have made human-AI collaboration easier for humans to team with AI agents. Leveraging …
have made human-AI collaboration easier for humans to team with AI agents. Leveraging …