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.

This paper studies a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown hardware impairments, we develop a novel likelihood function estimation method based on data augmentation and boosting. In our method, we generate multiple augmented datasets by injecting noise with various distributions into seed data consisting of online received signals. We then estimate the likelihood function (LF) using each augmented dataset based on the expectationmaximization algorithm. We linearly combine the multiple LF estimates obtained from these datasets based on their reliability levels. Simulation results demonstrate that the ML detection combined with our LF estimation method outperforms existing methods, while also highlighting the effectiveness of our data augmentation approach.

This paper addresses a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown hardware impairments, we develop novel likelihood function (LF) estimation methods based on data augmentation and boosting. The core idea of our methods is to generate multiple augmented datasets by injecting noise with various distributions into seed data consisting of online received signals. We then estimate the LF using each augmented dataset based on either the expectation maximization (EM) algorithm or the kernel density estimation (KDE) method. Inspired by boosting, we further refine the estimated LF by linearly combining the multiple LF estimates obtained from the augmented datasets. To determine the weights for this linear combination, we develop methods that take different approaches to measure the reliability of the estimated LFs. Simulation results demonstrate that both the EM- and KDE-based LF estimation methods offer significant performance gains over existing LF estimation methods. Our results also show that the effectiveness of the proposed methods improves as the size of the augmented data increases.

To support higher throughput in next-generation Wi-Fi systems, efficient compression and feedback of channel state information (CSI) from a station (STA) to an access point (AP) is essential. This paper proposes a deep learning (DL)-based CSI feedback framework tailored for Wi-Fi systems. The framework employs encoder and decoder networks to compress and reconstruct CSI angle parameters, with a trainable vector quantization (VQ) module enabling efficient finite-bit representation through end-to-end training. To further enhance performance, we introduce an angle-difference feedback strategy that exploits the temporal correlation of the angle parameters by feeding back the difference between the current and previous values. This is complemented by preprocessing that handles the periodicity of angles and tailored VQ modules that compensate for residual quantization errors. Additionally, we present a DL-based CSI refinement module at the AP, which improves reconstruction by jointly using current and prior feedback. Simulation results show that the proposed framework outperforms both standard Wi-Fi feedback and existing DL-based feedback methods, with notable gains from both angle-difference feedback and CSI refinement.