User-Driven Adaptive CSI Feedback With Ordered Vector Quantization

We propose a user-driven method for adaptive quantization and feedback of channel state information (CSI) in frequency division duplexing systems. The approach can be combined with existing autoencoder neural networks and requires only a single codebook shared between the users and the base station. Users can provide feedback with different numbers of bits depending on their data rates, without system changes or additional storage requirements. We optimize CSI feedback by combining the nested dropout layer with vector quantization, resulting in a variable-length feedback approach. Simulation results show the proposed method outperforms state-of-the-art schemes in dynamically adapting to different feedback rates.

Automated summary

We propose a user-driven method for adaptive quantization and feedback of channel state information (CSI) in frequency division duplexing systems. The approach can be combined with existing autoencoder neural networks and requires only a single codebook shared between the users and the base station. Users can provide feedback with different numbers of bits depending on their data rates, without system changes or additional storage requirements. Simulation results show the proposed method outperforms state-of-the-art schemes in dynamically adapting to different feedback rates.