22 papers
On the Gaussian-Quadratic Rate-Distortion Function for Vector Sources with Individual Distortion Constraints
Shuao Chen, Junyuan Gao, Yuxuan Shi +4
This paper investigates the Gaussian-quadratic lossy compression with arbitrary source length under individual distortion constraints. The rate-distortion function (RDF) is lower-b…
ConsisFormer: Compute-Efficient Transformer for Wireless Foundation Models Based on Channel Consistency
Yuwei Wang, Li Sun, Tingting Yang +4
Wireless foundation models (WFMs) have recently emerged as a promising paradigm for AI-native 6G networks, enabling universal channel representations adaptable to diverse communica…
Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models
Yuxuan Shi, Tingting Yang, Li Sun +4
Mobile systems increasingly rely on heterogeneous learning-enabled wireless functions, for which separate taskspecific models incur redundant training and model-management overhead…
SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction
Liwen Jing, Yisha Lu, Tingting Yang +5
This paper proposes SpikeWFM, a novel hybrid architecture that integrates spiking neural networks (SNNs) with conventional artificial neural network (ANN)-based transformers for wi…
Contextual Wireless Video Semantic Communication in MIMO-OFDM Systems
Bingyan Xie, Cong Zhou, Yuxuan Shi +3
This paper proposes a MIMO-OFDM-based context video semantic transmission framework, namely M-CVST, for robust video communication over multi-path multiple-input multiple-output (M…
SpecFed: Accelerating Federated LLM Inference with Speculative Decoding and Compressed Transmission
Ce Zheng, Xinghan Wang, Jiahong Ning +3
Federated inference enhances LLM performance in edge computing through weighted averaging of distributed model predictions. However, autoregressive LLM inference requires frequent…
FDD CSI Feedback under Finite Downlink Training: A Rate-Distortion Perspective
Shuao Chen, Junyuan Gao, Yuxuan Shi +4
This paper establishes the theoretical limits of channel state information (CSI) feedback in frequency-division duplexing (FDD) multi-antenna orthogonal frequency-division multiple…
Joint Lossy Compression for a Vector Gaussian Source under Individual Distortion Criteria
Shuao Chen, Junyuan Gao, Yuxuan Shi +4
This paper investigates the joint compression problem of a vector Gaussian source, where an individual distortion constraint is imposed on each source component. It is known that t…
Wireless Video Semantic Communication with Decoupled Diffusion Multi-frame Compensation
Bingyan Xie, Yongpeng Wu, Yuxuan Shi +4
Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wir…
Filter-and-Attend: Wireless Channel Foundation Model with Noise-Plus-Interference Suppression Structure
Yuwei Wang, Li Sun, Tingting Yang +3
Wireless channel foundation model (WCFM) is a task-agnostic AI model that is pre-trained to learn a universal channel representation for a wide range of communications and sensing…
Semantic-aided Parallel Image Transmission Compatible with Practical System
Mingkai Xu, Yongpeng Wu, Yuxuan Shi +4
In this paper, we propose a novel semantic-aided image communication framework for supporting the compatibility with practical separation-based coding architectures. Particularly,…
WVSC: Wireless Video Semantic Communication with Multi-frame Compensation
Bingyan Xie, Yongpeng Wu, Yuxuan Shi +4
Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wir…
WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication
Tingting Yang, Ping Zhang, Mengfan Zheng +4
This paper introduces WirelessGPT, a pioneering foundation model specifically designed for multi-task learning in wireless communication and sensing. Specifically, WirelessGPT leve…
RWZC: A Model-Driven Approach for Learning-based Robust Wyner-Ziv Coding
Yuxuan Shi, Shuo Shao, Yongpeng Wu +2
In this paper, a novel learning-based Wyner-Ziv coding framework is considered under a distributed image transmission scenario, where the correlated source is only available at the…
Learnable Residual-Based Latent Denoising in Semantic Communication
Mingkai Xu, Yongpeng Wu, Yuxuan Shi +3
A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the re…
SCSC: A Novel Standards-Compatible Semantic Communication Framework for Image Transmission
Xue Han, Yongpeng Wu, Zhen Gao +4
Joint source-channel coding (JSCC) is a promising paradigm for next-generation communication systems, particularly in challenging transmission environments. In this paper, we propo…
Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO Fading Channels
Bingyan Xie, Yongpeng Wu, Yuxuan Shi +3
Though achieving marvelous progress in various scenarios, existing semantic communication frameworks mainly consider single-input single-output Gaussian channels or Rayleigh fading…
Joint Data and Semantics Lossy Compression: Nonasymptotic Converse Bounds and Second-Order Asymptotics
Huiyuan Yang, Yuxuan Shi, Shuo Shao +1
This paper studies the joint data and semantics lossy compression problem, i.e., an extension of the hidden lossy source coding problem that entails recovering both the hidden and…
Indirect Lossy Source Coding with Observed Source Reconstruction: Nonasymptotic Bounds and Second-Order Asymptotics
Huiyuan Yang, Yuxuan Shi, Shuo Shao +1
This paper considers the joint compression of a pair of correlated sources, where the encoder is allowed to access only one of the sources. The objective is to recover both sources…
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission
Bingyan Xie, Yongpeng Wu, Yuxuan Shi +2
Multi-node communication, which refers to the interaction among multiple devices, has attracted lots of attention in many Internet-of-Things (IoT) scenarios. However, its huge amou…
Semantic-Aware Multi-Terminal Coding for Gaussian Mixture Sources
Yuxuan Shi, Shuo Shao, Yongpeng Wu +2
A novel distributed source coding model which named semantic-aware multi-terminal (MT) source coding is proposed and investigated in the paper, where multiple agents independently…
Excess Distortion Exponent Analysis for Semantic-Aware MIMO Communication Systems
Yuxuan Shi, Shuo Shao, Yongpeng Wu +3
In this paper, the analysis of excess distortion exponent for joint source-channel coding (JSCC) in semantic-aware communication systems is presented. By introducing an unobservabl…