In this project, we analyze the tradeoff between the channel state information (CSI) feedback overhead and the performance achieved by the users in frequency domain duplexing (FDD) MIMO systems in terms of achievable rate. The final goal of the proposed system is to determine the beamforming information (i.e., precoding) from channel realizations. We employ a deep learning-based approach to design the end-to-end precoding-oriented feedback architecture, that includes learned pilots, users’ compressors, and base station processing.
Featured Group Publications
- F. Carpi, S. Venkatesan, J. Du, H. Viswanathan, S. Garg and E. Erkip, “Precoding-oriented Massive MIMO CSI Feedback Design,” ICC 2023 – IEEE International Conference on Communications, Rome, Italy, 2023, pp. 4973-4978, doi: 10.1109/ICC45041.2023.10278955.