arXiv subjects

Peigen Ye

Publications and source records attributed to Peigen Ye.

4 recordsLinked to original sources

arXiv2025

User-Intent-Driven Semantic Communication via Adaptive Deep Understanding

Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting key semantics, they still fail to deeply understand and generalize users' real intentions. To overcome this, we propose a user-intention-driven semantic communication system that interprets diverse abstract intents. First, we integrate a multi-modal large model as semantic knowledge base to generate user-intention prior. Next, a mask-guided attention module is proposed to effectively highlight critical semantic regions. Further, a channel state awareness module ensures adaptive, robust transmission across varying channel conditions. Extensive experiments demonstrate that our system achieves deep intent understanding and outperforms DeepJSCC, e.g., under a Rayleigh channel at an SNR of 5 dB, it achieves improvements of 8%, 6%, and 19% in PSNR, SSIM, and LPIPS, respectively.

cs.IT↗

arXiv2024

Segmented Private Data Aggregation in the Multi-message Shuffle Model

The shuffle model of differential privacy (DP) offers compelling privacy-utility trade-offs in decentralized settings (e.g., internet of things, mobile edge networks). Particularly, the multi-message shuffle model, where each user may contribute multiple messages, has shown that accuracy can approach that of the central model of DP. However, existing studies typically assume a uniform privacy protection level for all users, which may deter conservative users from participating and prevent liberal users from contributing more information, thereby reducing the overall data utility, such as the accuracy of aggregated statistics. In this work, we pioneer the study of segmented private data aggregation within the multi-message shuffle model of DP, introducing flexible privacy protection for users and enhanced utility for the aggregation server. Our framework not only protects users' data but also anonymizes their privacy level choices to prevent potential data leakage from these choices. To optimize the privacy-utility-communication trade-offs, we explore approximately optimal configurations for the number of blanket messages and conduct almost tight privacy amplification analyses within the shuffle model. Through extensive experiments, we demonstrate that our segmented multi-message shuffle framework achieves a reduction of about 50\% in estimation error compared to existing approaches, significantly enhancing both privacy and utility.

cs.CR↗

arXiv2024

Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer

Codebook-based generative semantic communication attracts increasing attention, since only indices are required to be transmitted when the codebook is shared between transmitter and receiver. However, due to the fact that the semantic relations among code vectors are not necessarily related to the distance of the corresponding code indices, the performance of the codebook-enabled semantic communication system is susceptible to the channel noise. Thus, how to improve the system robustness against the noise requires careful design. This paper proposes a robust codebook-assisted image semantic communication system, where semantic codec and codebook are first jointly constructed, and then vector-to-index transformer is designed guided by the codebook to eliminate the effects of channel noise, and achieve image generation. Thanks to the assistance of the high-quality codebook to the Transformer, the generated images at the receiver outperform those of the compared methods in terms of visual perception. In the end, numerical results and generated images demonstrate the advantages of the generative semantic communication method over JPEG+LDPC and traditional joint source channel coding (JSCC) methods.

cs.IT↗

arXiv2023

A Token-level Contrastive Framework for Sign Language Translation

Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people. Recently, researchers have adopted Neural Machine Translation (NMT) methods, which usually require large-scale corpus for training, to achieve SLT. However, the publicly available SLT corpus is very limited, which causes the collapse of the token representations and the inaccuracy of the generated tokens. To alleviate this issue, we propose ConSLT, a novel token-level \textbf{Con}trastive learning framework for \textbf{S}ign \textbf{L}anguage \textbf{T}ranslation , which learns effective token representations by incorporating token-level contrastive learning into the SLT decoding process. Concretely, ConSLT treats each token and its counterpart generated by different dropout masks as positive pairs during decoding, and then randomly samples $K$ tokens in the vocabulary that are not in the current sentence to construct negative examples. We conduct comprehensive experiments on two benchmarks (PHOENIX14T and CSL-Daily) for both end-to-end and cascaded settings. The experimental results demonstrate that ConSLT can achieve better translation quality than the strong baselines.

cs.CL↗