@book{
  author = {F. Haddad and C. Bockelmann and A. Dekorsy},
  year = {2025},
  month = {Mar},
  title = { CSI Feedback Compression in MIMO Time-Varying Systems via Dynamic Mode Decomposition and Convolutional Autoencoder},
  URL = {https://scc2025.net/},
  address={Karlsruhe, Germany},
  abstract={In wireless communication, base stations rely on downlink Channel State Information (CSI) to perform precoding. Without channel reciprocity, the mobile station must transmit the estimated CSI back to the base station. Due to the time-varying nature of the environment, channel characteristics change constantly, requiring regular CSI feedback updates at intervals that depend on the rate of change. Thus, increasing the interval between the CSI updates, can reduce the average CSI feedback overhead. Additionally, In Multiple-Input Multiple-Output (MIMO) systems, the CSI feedback overhead grows with the number of antennas and bandwidth, leading to a potential performance bottleneck. To reduce the CSI feedback overhead and increase the intervals between CSI updates,  we propose a novel method that integrates Dynamic Mode Decomposition (DMD) and Convolutional Autoencoders (CAE) to model and compress channel dynamics. DMD decomposes the channel matrix into modes that can predict the future state of the channel, thereby extending CSI feedback intervals, while CAE captures the most relevant features of these modes for further compression. Simulation results demonstrate that this method effectively reduces feedback overhead and prolongs the intervals between CSI updates.},
  booktitle={International ITG Conference on Systems, Communications and Coding}
}