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

Author:

Pasic Faruk1,Di Cicco Nicola2,Skocaj Marco3,Tornatore Massimo2,Schwarz Stefan1,Mecklenbräuker Christoph F.1,Oegli-Esposti Vittorio3

Affiliation:

1. Institute of Telecommunications,TU Wien,Austria

2. Politecnico di Milano,DEIB,Italy

3. University of Bologna, and WiLab, CNIT,DEI,Italy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications

Reference15 articles.

1. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks;gal;Advances in neural information processing systems,2016

2. Multi-Band Wireless Channel Measurements in High-Mobility Environment;pasic;IEEE Dataport,2022

3. Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks;radford;Proc 4th Int'l Conf Learning Representations ICLR 2016,2016

4. Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

5. Crest-Factor Study of MC-CDMA and OFDM;choi;Proc 50th Vehicular Technology Conf,0

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2. Stationarity of Multiband Channels for OTFS-Based Intelligent Transportation Systems;2024 18th European Conference on Antennas and Propagation (EuCAP);2024-03-17

3. Toward Better Generalization: Shape Feature-Enhanced Fastener Defect Detection With Diffusion Model;IEEE Transactions on Instrumentation and Measurement;2024

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