arXiv subjects

Jürgen Jasperneite

Publications and source records attributed to Jürgen Jasperneite.

3 recordsLinked to original sources

arXiv2026

Enhancing 6G Wireless Intelligence: Do LLMs Work for CSI Prediction?

In high-mobility 6G scenarios, rapidly time-varying channels lead to very short coherence times, which makes conventional pilot-based channel state information (CSI) estimation approaches prone to outdated information or excessive pilot overhead. Therefore, channel prediction becomes essential in such dynamic wireless systems. To address this challenge, large language models (LLMs) are emerging learning frameworks that have recently attracted attention for CSI prediction due to their strong sequence modeling capability and ability to generalize across different environments. This paper proposes an LLM-based framework for channel prediction in high-mobility orthogonal time frequency space (OTFS) communication systems. In this work, we develop a physics-aware LLM-based predictor that learns the temporal evolution of OTFS channel coefficients from historical channel observations while incorporating mobility-related physical descriptors (e.g., maximum Doppler frequency) to achieve accurate prediction of future channel states in rapidly time-varying environments. The effectiveness of the proposed framework is evaluated through extensive simulations under user velocities ranging from 100 to 500 km/h. Numerical results show that the proposed method consistently achieves lower normalized mean square error (NMSE) compared with both classical deep learning predictors and LLM-based predictors without physical channel descriptors. These results demonstrate the advantage of integrating mobility-related channel knowledge with LLM-based sequence modeling for channel prediction in highly dynamic OTFS systems.

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arXiv2026

A Conditional Variational Framework for Channel Prediction in High-Mobility 6G OTFS Networks

This paper proposes a machine learning (ML) based method for channel prediction in high mobility orthogonal time frequency space (OTFS) channels. In these scenarios, rapid variations caused by Doppler spread and time varying multipath propagation lead to fast channel decorrelation, making conventional pilot based channel estimation methods prone to outdated channel state information (CSI) and excessive overhead. Therefore, reliable channel prediction methods become essential to support robust detection and decoding in OTFS systems. In this paper, we propose conditional variational autoencoder for channel prediction (CVAE4CP) method, which learns the conditional distribution of OTFS delay Doppler channel coefficients given physical system and mobility parameters. By incorporating these parameters as conditioning information, the proposed method enables the prediction of future channel coefficients before their actual realization, while accounting for inherent channel uncertainty through a low dimensional latent representation. The proposed framework is evaluated through extensive simulations under high mobility conditions. Numerical results demonstrate that CVAE4CP consistently outperforms a competing learning based baseline in terms of normalized mean squared error (NMSE), particularly at high Doppler frequencies and extended prediction horizons. These results confirm the effectiveness and robustness of the proposed approach for channel prediction in rapidly time varying OTFS systems.

eess.SP↗

arXiv2022

Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control

Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforcement learning (RL) for traffic signal control is a promising approach to design better control policies and has attracted considerable research interest in recent years. However, most work done in this area used simplified simulation environments of traffic scenarios to train RL-based TSC. To deploy RL in real-world traffic systems, the gap between simplified simulation environments and real-world applications has to be closed. Therefore, we propose LemgoRL, a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany. In addition to the realistic simulation model, LemgoRL encompasses a traffic signal logic unit that ensures compliance with all regulatory and safety requirements. LemgoRL offers the same interface as the wellknown OpenAI gym toolkit to enable easy deployment in existing research work. To demonstrate the functionality and applicability of LemgoRL, we train a state-of-the-art Deep RL algorithm on a CPU cluster utilizing a framework for distributed and parallel RL and compare its performance with other methods. Our benchmark tool drives the development of RL algorithms towards real-world applications.

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