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Found 10 papers, 4 papers with code
QoS prediction in radio vehicular environments via prior user information
Moreover, we are extending prior art by showing how longer prediction horizons can be supported.
From Empirical Measurements to Augmented Data Rates: A Machine Learning Approach for MCS Adaptation in Sidelink Communication
In this paper, we propose a machine learning approach to predict suitable MCS levels.
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches
This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement.
Explainable Artificial Intelligence (XAI)
Feature Engineering
Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies
The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities.
Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets
This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets.
Machine Learning-based Methods for Reconfigurable Antenna Mode Selection in MIMO Systems
Due to the non-convexity of this problem, we propose machine learning-based methods for RA antenna mode selection in both dynamic and static scenarios.
GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems
In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform.
Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Maps
Pure data-driven methods can achieve good performance without assuming any physical model, but their complexity and their lack of robustness is not acceptable for many applications.
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication
This paper addresses the problem of decentralized spectrum sharing in vehicle-to-everything (V2X) communication networks.
Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information
In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks.
