Literature Database Entry

min2026wireless

Yura Min, "Wireless Channel Prediction based on Machine Learning," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), May 2026. (Advisor: Anatolij Zubow; Referees: Falko Dressler and Odej Kao)

Abstract

In this work, a machine-learning-based data-driven framework is developed to predict future wireless channel frequency responses (CFRs) from Sionna RT-generated channel traces. Two propagation scenarios are considered: an outdoor Munich urban environment and an indoor apartment environment. The generated CFR traces are transformed into magnitude/phase-based real-valued representations and arranged into supervised sequence datasets with additional motion-related information. Four neural-network variants are evaluated: an LSTM-based model, a causal Conv1D–LSTM model, a stacked LSTM model, and a Conv1D–stacked LSTM model. The experiments analyze how CFR prediction performance changes with respect to model architecture, temporal context length, and receiver speed. Performance is evaluated using complex NMSE and magnitude-only NMSE in order to distinguish full complex-valued prediction quality from amplitude-tracking accuracy. The results show that the indoor and outdoor scenarios exhibit different channel characteristics and lead to different prediction behavior. In the speed-sweep experiments, prediction performance generally degrades as receiver speed increases, although the most robust model depends on the scenario. In the lag-sweep experiments, increasing the temporal context length does not always improve prediction accuracy, indicating a trade-off between additional channel history and increased learning complexity. The best tested configurations achieve clearly negative magnitude-only NMSE values, while complex NMSE values are generally higher due to phase prediction errors. Overall, the results demonstrate that data-driven CFR prediction performance depends jointly on the propagation environment, model architecture, lag length, receiver speed, and the distinction between magnitude-only and complex-valued evaluation.

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Yura Min

BibTeX reference

@phdthesis{min2026wireless,
    author = {Min, Yura},
    title = {{Wireless Channel Prediction based on Machine Learning}},
    advisor = {Zubow, Anatolij},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {5},
    referee = {Dressler, Falko and Kao, Odej},
    school = {TU Berlin (TUB)},
    type = {Bachelor Thesis},
    year = {2026},
   }
   
   

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Last modified: 2026-09-12