Learning Representations for CSI Adaptive Quantization and Feedback
This work addresses a specific bottleneck in wireless communication systems, offering incremental improvements for CSI feedback efficiency.
The paper tackles the problem of inefficient quantization in channel state information (CSI) feedback for FDD systems, proposing two methods—post-training quantization and codebook training during autoencoder training—that achieve better reconstruction accuracy compared to standard techniques.
In this work, we propose an efficient method for channel state information (CSI) adaptive quantization and feedback in frequency division duplexing (FDD) systems. Existing works mainly focus on the implementation of autoencoder (AE) neural networks (NNs) for CSI compression, and consider straightforward quantization methods, e.g., uniform quantization, which are generally not optimal. With this strategy, it is hard to achieve a low reconstruction error, especially, when the available number of bits reserved for the latent space quantization is small. To address this issue, we recommend two different methods: one based on a post training quantization and the second one in which the codebook is found during the training of the AE. Both strategies achieve better reconstruction accuracy compared to standard quantization techniques.