On Latency Prediction with Deep Learning and Passive Probing at High Mobility

Kulzer, Daniel Fabian; Debbichi, Firas; Stańczak, Sławomir; Botsov, Mladen

doi:10.1109/icc42927.2021.9500495

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…QoS prediction in a 5G non-standalone network is examined in [35]. Besides throughput, ML is also used to predict latency [5,36,37] and handovers [38]. Recently, several datasets intended for ML-based studies have been made publicly available [39,40].…”

Section: State Of the Artmentioning

confidence: 99%

“…For each interval with a lower speed boundary v , the mean absolute Resampling Error RE v is calculated according to Eq. (5).…”

Section: A Sampling the Radio Environmentmentioning

confidence: 99%

“…Machine Learning (ML) has a strong potential to overcome challenges arising in vehicular communication and networking, as presented in [1,2]. Examples of such challenges are resource allocation [3,4] and QoS prediction [5]. The QoS prediction, in turn, is an enabler for a variety of use cases in the domain of vehicular communication, such as autonomous driving, platooning, cooperative maneuvering, tele-operated driving, and smart navigation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

Palaios¹,

Vielhaus²,

Kulzer³

et al. 2023

Preprint

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As cellular networks evolve towards the 6th Generation (6G), Machine Learning (ML) is seen as a key enabling technology to improve the capabilities of the network. ML provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific Quality of Service (QoS) requirement. With predictive Quality of Service (pQoS), 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 HD mapping applications. We discuss the entire ML 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 ML models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on ML 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 ML models within diverse scenarios. We also use Explainable AI (XAI) to show that ML 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.

show abstract

Section: State Of the Artmentioning

confidence: 99%

“…For each interval with a lower speed boundary v , the mean absolute Resampling Error RE v is calculated according to Eq. (5).…”

Section: A Sampling the Radio Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

Palaios¹,

Vielhaus²,

Kulzer³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…QoS prediction in a 5G non-standalone network is examined in [35]. Besides throughput, ML is also used to predict latency [5], [36], [37] and handovers [38]. Recently, several datasets intended for ML-based studies have been made publicly available [39], [40] that typically do not provide cell-related information.…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning (ML) has a strong potential to overcome challenges arising in vehicular communication and networking, as presented in [1], [2]. Examples of such challenges are resource allocation [3], [4] and quality of service (QoS) prediction [5]. The QoS prediction, in turn, is an enabler for a variety of use cases in the domain of vehicular communication, such as autonomous driving, platooning, cooperative maneuvering, tele-operated driving, and smart navigation.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

et al. 2023

View full text Add to dashboard Cite

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, in turn, 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 highdefinition 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.INDEX TERMS Intelligent transportation systems, machine learning, quality of service, throughput prediction, vehicular communication, wireless networks.

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A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence

Baganal-Krishna,

Lübben,

Liotou

et al. 2024

Computer Networks

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On Latency Prediction with Deep Learning and Passive Probing at High Mobility

Cited by 9 publications

References 13 publications

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence

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