Wenyi Zhang’s research while affiliated with University of Electronic Science and Technology of China and other places

A composite source model consists of an intrinsic state and an extrinsic observation. The fundamental performance limit of reproducing the intrinsic state is characterized by the indirect rate–distortion function. In a remote classification application, a source encoder encodes the extrinsic observation (e.g., image) into bits, and a source decoder plays the role of a classifier that reproduces the intrinsic state (e.g., label of image). In this work, we characterize the general structure of the optimal transition probability distribution, achieving the indirect rate–distortion function. This optimal solution can be interpreted as a “soft classifier”, which generalizes the conventionally adopted “classify-then-compress” scheme. We then apply the soft classification to aid the lossy compression of the extrinsic observation of a composite source. This leads to a coding scheme that exploits the soft classifier to guide reproduction, outperforming existing coding schemes without classification or with hard classification.

We investigate information-theoretic limits and design of communication under receiver quantization. Unlike most existing studies, this work is more focused on the impact of resolution reduction from high to low. We consider a standard transceiver architecture, which includes i.i.d. complex Gaussian codebook at the transmitter, and a symmetric quantizer cascaded with a nearest neighbor decoder at the receiver. Employing the generalized mutual information (GMI), an achievable rate under general quantization rules is obtained in an analytical form, which shows that the rate loss due to quantization is $\log\left(1+\gamma\mathsf{SNR}\right)$, where $\gamma$ is determined by thresholds and levels of the quantizer. Based on this result, the performance under uniform receiver quantization is analyzed comprehensively. We show that the front-end gain control, which determines the loading factor of quantization, has an increasing impact on performance as the resolution decreases. In particular, we prove that the unique loading factor that minimizes the MSE also maximizes the GMI, and the corresponding irreducible rate loss is given by $\log\left(1+\mathsf {mmse}\cdot\mathsf{SNR}\right)$, where mmse is the minimum MSE normalized by the variance of quantizer input, and is equal to the minimum of $\gamma$. A geometrical interpretation for the optimal uniform quantization at the receiver is further established. Moreover, by asymptotic analysis, we characterize the impact of biased gain control, including how small rate losses decay to zero and achievable rate approximations under large bias. From asymptotic expressions of the optimal loading factor and mmse, approximations and several per-bit rules for performance are also provided. Finally we discuss more types of receiver quantization and show that the consistency between achievable rate maximization and MSE minimization does not hold in general.

Diffusion models have demonstrated remarkable success in generative tasks, yet their iterative denoising process results in slow inference, limiting their practicality. While existing acceleration methods exploit the well-known U-shaped similarity pattern between adjacent steps through caching mechanisms, they lack theoretical foundation and rely on simplistic computation reuse, often leading to performance degradation. In this work, we provide a theoretical understanding by analyzing the denoising process through the second-order Adams-Bashforth method, revealing a linear relationship between the outputs of consecutive steps. This analysis explains why the outputs of adjacent steps exhibit a U-shaped pattern. Furthermore, extending Adams-Bashforth method to higher order, we propose a novel caching-based acceleration approach for diffusion models, instead of directly reusing cached results, with a truncation error bound of only $O(h^k)$ where h is the step size. Extensive validation across diverse image and video diffusion models (including HunyuanVideo and FLUX.1-dev) with various schedulers demonstrates our method's effectiveness in achieving nearly $3\times$ speedup while maintaining original performance levels, offering a practical real-time solution without compromising generation quality.

We investigate performance limits and design of communication in the presence of uniform output quantization with moderate to high resolution. Under independent and identically distributed (i.i.d.) complex Gaussian codebook and nearest neighbor decoding rule, an achievable rate is derived in an analytical form by the generalized mutual information (GMI). The gain control before quantization is shown to be increasingly important as the resolution decreases, due to the fact that the loading factor (normalized one-sided quantization range) has increasing impact on performance. The impact of imperfect gain control in the high-resolution regime is characterized by two asymptotic results: 1) the rate loss due to overload distortion decays exponentially as the loading factor increases, and 2) the rate loss due to granular distortion decays quadratically as the step size vanishes. For a 2K-level uniform quantizer, we prove that the optimal loading factor that maximizes the achievable rate scales like $2\sqrt{\ln 2K}$ as the resolution increases. An asymptotically tight estimate of the optimal loading factor is further given, which is also highly accurate for finite resolutions.

In this work, generalized nearest neighbor decoding (GNND), a recently proposed receiver architecture, is studied for channels under general input constellations, and multiuser uplink interference suppression is employed as a case study for demonstrating its potential. In essence, GNND generalizes the well-known nearest neighbor decoding, by introducing a symbol-level memoryless processing step, which can be rendered seamlessly compatible with Gaussian channel-based decoders. First, criteria of the optimal GNND are derived for general input constellations, expressed in the form of conditional moments matching, thereby generalizing the prior work which has been confined to Gaussian input. Then, the optimal GNND is applied to the use case of multiuser uplink, for which the optimal GNND is shown to be capable of achieving information rates nearly identical to the channel mutual information. By contrast, the commonly used channel linearization (CL) approach incurs a noticeable rate loss. A coded modulation scheme is subsequently developed, aiming at implementing GNND using off-the-shelf channel codes, without requiring iterative message passing between demodulator and decoder. Through numerical experiments it is validated that the developed scheme significantly outperforms the CL-based scheme.

This paper introduces a new stochastic geometry-based model to analyze the Request-to-Send/Clear-to-Send (RTS/CTS) handshake mechanism in wireless local area networks (WLANs). We develop an advanced hard-core point process model, termed the dual-zone hard-core process (DZHCP), which extends traditional hard-core models to capture the spatial interactions and exclusion effects introduced by the RTS/CTS mechanism. This model integrates key parameters accounting for the thinning effects imposed by RTS/CTS, enabling a refined characterization of active transmitters in the network. Analytical expressions are derived for the intensity of the DZHCP, the mean interference, and an approximation of the success probability, providing insight into how network performance depends on critical design parameters. Our results demonstrate that the Type II RTS/CTS mechanism significantly reduces mean interference by introducing additional protection regions, effectively mitigating the impact of nearby interferers. Compared to CSMA-based schemes, it achieves a lower mean interference level while maintaining a comparable or higher active node density.

A composite source, consisting of multiple subsources and a memoryless switch, outputs one symbol at a time from the subsource selected by the switch. If some data should be encoded more accurately than other data from an information source, the composite source model is suitable because in this model different distortion constraints can be put on the subsources. In this context, we propose subsource-dependent fidelity criteria for composite sources and use them to formulate a rate-distortion problem. We solve the problem and obtain a single-letter expression for the rate-distortion function. Further rate-distortion analysis characterizes the performance of classify-then-compress (CTC) coding, which is frequently used in practice when subsource-dependent fidelity criteria are considered. Our analysis shows that CTC coding generally has performance loss relative to optimal coding, even if the classification is perfect. We also identify the cause of the performance loss, that is, class labels have to be reproduced in CTC coding. Last but not least, we show that the performance loss is negligible for asymptotically small distortion if CTC coding is appropriately designed and some mild conditions are satisfied.

This paper introduces a new stochastic geometry-based model to analyze the Request-to-Send/Clear-to-Send (RTS/CTS) handshake mechanism in wireless local area networks (WLANs). We develop an advanced hard-core point process model, termed the dual-zone hard-core process (DZHCP), which extends traditional hard-core models to capture the spatial interactions and exclusion effects introduced by the RTS/CTS mechanism. This model integrates key parameters accounting for the thinning effects imposed by RTS/CTS, enabling a refined characterization of active transmitters in the network. Analytical expressions are derived for the intensity of the DZHCP, the mean interference, and an approximation of the success probability, providing insight into how network performance depends on critical design parameters. Our results provide better estimates of interference levels and success probability, which could inform strategies for better interference management and improved performance in future WLAN designs.