March 14, 2024, 4:47 a.m. | Junyong Shin, Yujin Kang, Yo-Seb Jeon

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.07355v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv autoencoder complexity computational cs.ai cs.cv eess.sp feedback framework information massive multiple paper quantization rate representation state systems type vae vector

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