Deep Learning-Based CSI Feedback for Wi-Fi Systems With Temporal Correlation

To achieve higher throughput in next-generation Wi-Fi systems, a station (STA) needs to efficiently compress channel state information (CSI) and feed it back to an access point (AP). In this paper, we propose a novel deep learning (DL)-based CSI feedback framework tailored for next-generation Wi-Fi systems. Our framework incorporates a pair of encoder and decoder neural networks to compress and reconstruct the angle parameters of the CSI. To enable an efficient finite-bit representation of the encoder output, we introduce a trainable vector quantization module, which is integrated after the encoder network and jointly trained with both the encoder and decoder networks in an end-to-end manner. Additionally, we further enhance our framework by leveraging the temporal correlation of the angle parameters. Specifically, we propose an angle-difference feedback strategy which transmits the difference between the current and previous angle parameters when the difference is sufficiently small. This strategy accounts for the periodicity of the angle parameters through proper preprocessing and mitigates error propagation effects using novel feedback methods. We also introduce a DL-based CSI refinement module for the AP, which improves the reconstruction accuracy of the angle parameters by simultaneously utilizing both the previous and current feedback information. Simulation results demonstrate that our framework outperforms the standard method employed in current Wi-Fi systems. Our results also demonstrate significant performance gains achieved by the angle-difference feedback strategy and the CSI refinement module.

ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector Quantization

This paper proposes a novel end-to-end digital semantic communication framework based on multi-codebook vector quantization (VQ), referred to as ESC-MVQ. Unlike prior approaches that rely on end-to-end training with a specific power or modulation scheme, often under a particular channel condition, ESC-MVQ models a channel transfer function as parallel binary symmetric channels (BSCs) with trainable bit-flip probabilities. Building on this model, ESC-MVQ jointly trains multiple VQ codebooks and their associated bit-flip probabilities with a single encoder-decoder pair. To maximize inference performance when deploying ESC-MVQ in digital communication systems, we devise an optimal communication strategy that jointly optimizes codebook assignment, adaptive modulation, and power allocation. To this end, we develop an iterative algorithm that selects the most suitable VQ codebook for semantic features and flexibly allocates power and modulation schemes across the transmitted symbols. Simulation results demonstrate that ESC-MVQ, using a single encoder-decoder pair, outperforms existing digital semantic communication methods in both performance and memory efficiency, offering a scalable and adaptive solution for realizing digital semantic communication in diverse channel conditions.

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.