Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems
<|reference_start|>Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems: This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework while reducing its computational complexity based on shape-gain vector quantization. In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized using a trainable Grassmannian codebook. A multi-rate codebook design strategy is also developed by introducing a codeword selection rule for a nested codebook along with the design of a loss function. Simulation results demonstrate that the proposed method reduces the computational complexity associated with VQ-VAE while improving CSI reconstruction performance under a given feedback overhead.<|reference_end|>
@article{shin2024vector,
    title={Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO
  Systems},
    author={Junyong Shin, Yujin Kang, Yo-Seb Jeon},
    journal={arXiv preprint arXiv:2403.07355},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07355},
    primaryClass={eess.SP cs.AI cs.CV}
}