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

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. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2

Brady- and tachyarrhythmias detected by continuous rhythm monitoring in paroxysmal atrial fibrillation

Objective: Atrial fibrillation (AF) is associated with adverse events including conduction disturbances, ventricular arrhythmias and sudden death. The aim of this study was to examine brady- and tachyarrhythmias using continuous rhythm monitoring in patients with paroxysmal self-terminating AF (PAF). Methods: In this multicentre observational substudy to the Reappraisal of Atrial Fibrillation: interaction between hyperCoagulability, Electrical remodelling and Vascular destabilisation in the progression of AF (RACE V), we included 392 patients with PAF and at least 2 years of continuous rhythm monitoring. All patients received an implantable loop recorder, and all detected episodes of tachycardia ≥182 beats per minute (BPM), bradycardia ≤30 BPM or pauses ≥5 s were adjudicated by three physicians. Results: Over 1272 patient-years of continuous rhythm monitoring, we adjudicated 1940 episodes in 175 patients (45%): 106 (27%) patients experienced rapid AF or atrial flutter (AFL), pauses ≥5 s or bradycardias ≤30 BPM occurred in 47 (12%) patients and in 22 (6%) patients, we observed both episode types. No sustained ventricular tachycardias occurred. In the multivariable analysis, age &gt;70 years (HR 2.3, 95% CI 1.4 to 3.9), longer PR interval (HR 1.9, 1.1-3.1), CHA2DS2-VASc score ≥2 (HR 2.2, 1.1-4.5) and treatment with verapamil or diltiazem (HR 0.4, 0.2-1.0) were significantly associated with bradyarrhythmia episodes. Age &gt;70 years was associated with lower rates of tachyarrhythmias. Conclusions: In a cohort exclusive to patients with PAF, almost half experienced severe bradyarrhythmias or AF/AFL with rapid ventricular rates. Our data highlight a higher than anticipated bradyarrhythmia risk in PAF. Trial registration number: NCT02726698.</p

Brady- and tachyarrhythmias detected by continuous rhythm monitoring in paroxysmal atrial fibrillation

Objective: Atrial fibrillation (AF) is associated with adverse events including conduction disturbances, ventricular arrhythmias and sudden death. The aim of this study was to examine brady- and tachyarrhythmias using continuous rhythm monitoring in patients with paroxysmal self-terminating AF (PAF). Methods: In this multicentre observational substudy to the Reappraisal of Atrial Fibrillation: interaction between hyperCoagulability, Electrical remodelling and Vascular destabilisation in the progression of AF (RACE V), we included 392 patients with PAF and at least 2 years of continuous rhythm monitoring. All patients received an implantable loop recorder, and all detected episodes of tachycardia ≥182 beats per minute (BPM), bradycardia ≤30 BPM or pauses ≥5 s were adjudicated by three physicians. Results: Over 1272 patient-years of continuous rhythm monitoring, we adjudicated 1940 episodes in 175 patients (45%): 106 (27%) patients experienced rapid AF or atrial flutter (AFL), pauses ≥5 s or bradycardias ≤30 BPM occurred in 47 (12%) patients and in 22 (6%) patients, we observed both episode types. No sustained ventricular tachycardias occurred. In the multivariable analysis, age &gt;70 years (HR 2.3, 95% CI 1.4 to 3.9), longer PR interval (HR 1.9, 1.1-3.1), CHA2DS2-VASc score ≥2 (HR 2.2, 1.1-4.5) and treatment with verapamil or diltiazem (HR 0.4, 0.2-1.0) were significantly associated with bradyarrhythmia episodes. Age &gt;70 years was associated with lower rates of tachyarrhythmias. Conclusions: In a cohort exclusive to patients with PAF, almost half experienced severe bradyarrhythmias or AF/AFL with rapid ventricular rates. Our data highlight a higher than anticipated bradyarrhythmia risk in PAF. Trial registration number: NCT02726698.</p