Performance Evaluation over DL-Based Channel Prediction Algorithm on Realistic CSI

Qiuheng Zhou, Wei Jiang, Donglin Wang, Hans Dieter Schotten (editor)

IEEE Vehicular Technology Conference (VTC-2022) September 26-29 London United Kingdom IEEE 2022.


With the development of smart connected automated guided vehicles (AGVs) and robots, many new services and applications occur, which require flexible wireless end-to-end communication, high data transmission, and intensive computation. To achieve such a highly demanding communication system, it is very important to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance, such as throughput and transmission efficiency. In this paper, we present our efforts towards proposing a deep learning-based channel prediction algorithm, which is then evaluated on the data set measured with different system state report frequencies from our implemented software-defined radio platform in different indoor environments. Results showed that the proposed channel predictor has a convincing ability in real-world channel prediction.


Performance_Evaluation_over_DL-Based_Channel_Prediction_Algorithm_on_Realistic_CSI.pdf (pdf, 889 KB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz