@inproceedings{
  author = {A. Danaee and S. Hassanpour and D. W\"{u}bben and A. Dekorsy},
  year = {2025},
  month = {Mar},
  title = {Relevance-Based Multi-User Data Compression for Fronthaul Rate Reduction in Cell-Free Massive MIMO Systems},
  URL = {https://scc2025.net/},
  address={Karlsruhe, Germany},
  abstract={In the Cell-Free massive Multiple-Input Multiple-Output (CF-mMIMO) systems, a large number of distributed users are simultaneously served by multiple Radio Access Points (RAPs). In the uplink, each RAP receives noisy observations from several users and must locally compress these signals before forwarding them to the corresponding Central Processing Unit (CPU) via multiple fronthaul channels, each subject to a rate limitation. The challenge is to design the compressed signals at the RAPs such that the received signals at the CPU retain as much information as possible about the users (to be retrieved). To address this, we adopt compression techniques based on the Information Bottleneck (IB) principle to design local quantizers at the RAPs by ensuring an efficient balance between the informativity and compactness of the compressed signals. We discuss here two different compression schemes: one that processes the signals independently across fronthaul links and another that leverages the side information from previously retrieved signals at the CPU. By using side information, the latter generally provides a better trade-off between the compression efficiency and performance, albeit with an increased complexity. Through numerical simulations, we demonstrate the effectiveness of both IB-based schemes compared to the conventional compression methods, showing their potential for improving fronthaul rate efficiency and overall system performance in typical digital transmission scenarios.},
  booktitle={International ITG Conference on Systems, Communications and Coding (SCC 2025)}
}