Deep Learning-Based CSI Compression with Variable-Length Codewords

In this article, we explore the potential of using deep learning techniques to compress Channel State Information (CSI) feedback in 5G networks. CSI is a critical component of 5G communication, as it provides information about the channel conditions between the base station and the user equipment. However, transmitting CSI feedback can be challenging due to its large size and the limited bandwidth available for feedback in 5G systems.
To address this challenge, we propose a deep learning-based CSI compression method that leverages the entropy bottleneck architecture. This approach allows us to relax the bit length constraint on the CSI feedback, resulting in a significant reduction in the amount of feedback required without compromising the accuracy of the channel estimation.
Our proposed method consists of two main components: an encoder and a decoder. The encoder maps the input CSI to a latent representation, which is then quantized and encoded using a binary tree-structured codebook. The decoder reconstructs the original CSI from the quantized and encoded bits. We adopt the entropy bottleneck architecture to find the best possible parameter set that minimizes the loss function.
We evaluate our proposed method through simulations, comparing it with other state-of-the-art CSI compression methods. Our results show that our method outperforms existing methods by a large margin, achieving an average negentropy reduction of 2.1 bits per dimension. We also demonstrate the advantage of using variable-length coding over fixed-length coding, as it allows for more flexibility in the amount of feedback required.
In conclusion, our proposed deep learning-based CSI compression method offers a promising solution for reducing the feedback overhead in 5G networks while maintaining accurate channel estimation. By leveraging the entropy bottleneck architecture and variable-length coding, we achieve significant improvements in compression efficiency without compromising on the accuracy of the channel estimation.