3 research outputs found
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches
As cellular networks evolve towards the 6th generation, machine learning is
seen as a key enabling technology to improve the capabilities of the network.
Machine learning provides a methodology for predictive systems, which can make
networks become proactive. This proactive behavior of the network can be
leveraged to sustain, for example, a specific quality of service requirement.
With predictive quality of service, a wide variety of new use cases, both
safety- and entertainment-related, are emerging, especially in the automotive
sector. Therefore, in this work, we consider maximum throughput prediction
enhancing, for example, streaming or high-definition mapping applications. We
discuss the entire machine learning workflow highlighting less regarded aspects
such as the detailed sampling procedures, the in-depth analysis of the dataset
characteristics, the effects of splits in the provided results, and the data
availability. Reliable machine learning models need to face a lot of challenges
during their lifecycle. We highlight how confidence can be built on machine
learning technologies by better understanding the underlying characteristics of
the collected data. We discuss feature engineering and the effects of different
splits for the training processes, showcasing that random splits might
overestimate performance by more than twofold. Moreover, we investigate diverse
sets of input features, where network information proved to be most effective,
cutting the error by half. Part of our contribution is the validation of
multiple machine learning models within diverse scenarios. We also use
explainable AI to show that machine learning can learn underlying principles of
wireless networks without being explicitly programmed. Our data is collected
from a deployed network that was under full control of the measurement team and
covered different vehicular scenarios and radio environments.Comment: 18 pages, 12 Figures. Accepted on IEEE Acces
Enabling Mobility-Oriented JCAS in 6G Networks: An Architecture Proposal
Sensing plays a crucial role in autonomous and assisted vehicular driving, as
well as in the operation of autonomous drones. The traditional segregation of
communication and onboard sensing systems in mobility applications is due to be
merged using Joint Communication and Sensing (JCAS) in the development of the
6G mobile radio standard. The integration of JCAS functions into the future
road traffic landscape introduces novel challenges for the design of the 6G
system architecture. Special emphasis will be placed on facilitating direct
communication between road users and aerial drones. In various mobility
scenarios, diverse levels of integration will be explored, ranging from
leveraging communication capabilities to coordinate different radars to
achieving deep integration through a unified waveform. In this paper, we have
identified use cases and derive five higher-level Tech Cases (TCs). Technical
and functional requirements for the 6G system architecture for a
device-oriented JCAS approach will be extracted from the TCs and used to
conceptualize the architectural views.Comment: 6 pages, 3 figures, 4th IEEE Symposium on Joint Communication and
Sensin
Unsupervised Domain Adaptation across FMCW Radar Configurations Using Margin Disparity Discrepancy
Commercial radar sensing is gaining relevance and machine learning algorithms
constitute one of the key components that are enabling the spread of this radio
technology into areas like surveillance or healthcare. However, radar datasets
are still scarce and generalization cannot be yet achieved for all radar
systems, environment conditions or design parameters. A certain degree of fine
tuning is, therefore, usually required to deploy machine-learning-enabled radar
applications. In this work, we consider the problem of unsupervised domain
adaptation across radar configurations in the context of deep-learning human
activity classification using frequency-modulated continuous-wave. For that, we
focus on the theory-inspired technique of Margin Disparity Discrepancy, which
has already been proved successful in the area of computer vision. Our
experiments extend this technique to radar data, achieving a comparable
accuracy to fewshot supervised approaches for the same classification problem.Comment: 5 pages, 2 figures, accepted as a conference paper for EUSIPCO 202