Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems
This work addresses efficiency and performance issues in CSI feedback for massive MIMO systems, which is incremental as it builds on existing VQ-VAE frameworks.
The paper tackles the problem of high computational complexity in deep-learning-based channel state information (CSI) feedback for massive MIMO systems by proposing a vector-quantized variational autoencoder method with shape-gain quantization, resulting in improved CSI reconstruction performance under a given feedback overhead.
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.