High-Mobility Wireless Channel Measurements at 5.9 GHz in an Urban Environment

Pasic, Faruk; Pratschner, Stefan; Langwieser, Robert; Mecklenbräuker, Christoph F.

doi:10.1109/balkancom55633.2022.9900633

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset is accessible via IEEE DataPort [1], and consists of doubly-selective channel transfer functions classified in four categories depending on the measurement scenarios in Tab. I.…”

Section: B Dataset Descriptionmentioning

confidence: 99%

“…Whenever link adaptation is based on the assumption of instantaneous CSIT, outdated F. Pasic and N. Di Cicco are co-first authors of this paper. 1 Link to IEEE DataPort dataset: [1] https://dx.doi.org/10.21227/3tpp-j394 2 Link to CodeOcean: https://codeocean.com/capsule/9619772/tree CSIT can cause significant performance degradation [4]. For example, feedback delay and processing delay in frequencydivision duplex (FDD) and time-division duplex (TDD) systems, respectively, are causes of outdated CSIT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Band Measurements for Deep Learning-Based Dynamic Channel Prediction and Simulation

Pasic,

Di Cicco,

Skocaj

et al. 2023

IEEE Commun. Mag.

View full text Add to dashboard Cite

Next-generation mobile communication systems are planned to support millimeter Wave (mmWave) transmission in scenarios with high-mobility, such as in private industrial networks. To cope with propagation environments with unprecedented challenges, data-driven methodologies such as Machine Learning (ML) are expected to act as a fundamental tool for decision support in future mobile systems. However, highquality measurement datasets need to be made available to the research community in order to develop and benchmark MLbased methodologies for next-generation wireless networks. We present a reliable testbed for collecting channel measurements at sub-6 GHz and mmWave frequencies. Further, we describe a rich dataset collected using the presented testbed. Our public dataset enables the development and testing of innovative ML-based channel simulators for both sub-6 GHz and mmWave bands on real-world data. We conclude this paper by discussing promising experimental results on two illustrative ML tasks leveraging on our dataset, namely, channel impulse response forecasting and synthetic channel transfer function generation, upon which we propose future exploratory research directions. The original dataset employed in this work is available on IEEE DataPort 1 , and the code utilized in our numerical experiments is publicly accessible via CodeOcean 2 Index Terms-machine learning, deep learning, radio channel prediction, radio channel simulation, vehicular communications, channel sounding, channel measurements.

show abstract

Section: B Dataset Descriptionmentioning

confidence: 99%