19 citations · 49 across the 22 of their papers we have counts for
24 papers
Adaptive Source-Channel Coding for Semantic Communications
Dongxu Li, Kai Yuan, Jianhao Huang +4
Semantic communications (SemComs) have emerged as a promising paradigm for joint data and task-oriented transmissions, combining the demands for both the bit-accurate delivery and…
Neural Coding Is Not Always Semantic: Toward the Standardized Coding Workflow in Semantic Communications
Hai-Long Qin, Jincheng Dai, Sixian Wang +5
Semantic communication, leveraging advanced deep learning techniques, emerges as a new paradigm that meets the requirements of next-generation wireless networks. However, current s…
ResiComp: Loss-Resilient Image Compression via Dual-Functional Masked Visual Token Modeling
Sixian Wang, Jincheng Dai, Xiaoqi Qin +3
Recent advancements in neural image codecs (NICs) are of significant compression performance, but limited attention has been paid to their error resilience. These resulting NICs te…
NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission
Weijie Yue, Zhongwei Si, Bolin Wu +5
We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decom…
SoundSpring: Loss-Resilient Audio Transceiver with Dual-Functional Masked Language Modeling
Shengshi Yao, Jincheng Dai, Xiaoqi Qin +4
In this paper, we propose "SoundSpring", a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also bein…
Rate-Distortion-Perception Controllable Joint Source-Channel Coding for High-Fidelity Generative Communications
Kailin Tan, Jincheng Dai, Zhenyu Liu +5
End-to-end image transmission has recently become a crucial trend in intelligent wireless communications, driven by the increasing demand for high bandwidth efficiency. However, ex…
Deep Generative Modeling Reshapes Compression and Transmission: From Efficiency to Resiliency
Jincheng Dai, Xiaoqi Qin, Sixian Wang +3
Information theory and machine learning are inextricably linked and have even been referred to as "two sides of the same coin". One particularly elegant connection is the essential…
DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling
Sixian Wang, Jincheng Dai, Kailin Tan +3
End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical cond…
Pragmatic Communication in Multi-Agent Collaborative Perception
Yue Hu, Xianghe Pang, Xiaoqi Qin +4
Collaborative perception allows each agent to enhance its perceptual abilities by exchanging messages with others. It inherently results in a trade-off between perception ability a…
Fundamental Limitation of Semantic Communications: Neural Estimation for Rate-Distortion
Dongxu Li, Jianhao Huang, Chuan Huang +3
This paper studies the fundamental limit of semantic communications over the discrete memoryless channel. We consider the scenario to send a semantic source consisting of an observ…
SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding
Ke Yang, Sixian Wang, Jincheng Dai +3
As one of the key techniques to realize semantic communications, end-to-end optimized neural joint source-channel coding (JSCC) has made great progress over the past few years. A g…
Toward Intelligent and Efficient 6G Networks: JCSC Enabled On-Purpose Machine Communications
Ping Zhang, Heng Yang, Zhiyong Feng +5
Driven by the vision of "intelligent connection of everything" toward 6G, the collective intelligence of networked machines can be fully exploited to improve system efficiency by s…
NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials
Sixian Wang, Jincheng Dai, Xiaoqi Qin +2
Recent advances in deep learning have led to increased interest in solving high-efficiency end-to-end transmission problems using methods that employ the nonlinear property of neur…
Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications
Sixian Wang, Jincheng Dai, Xiaoqi Qin +3
Recent deep learning methods have led to increased interest in solving high-efficiency end-to-end transmission problems. These methods, we call nonlinear transform source-channel c…
Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme
Jianhao Huang, Dongxu Li, Chuan Huang +2
Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneousl…
Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding
Jincheng Dai, Sixian Wang, Ke Yang +5
The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned…
Timeliness of Information for Computation-intensive Status Updates in Task-oriented Communications
Xiaoqi Qin, Yanlin Li, Xianxin Song +3
Moving beyond just interconnected devices, the increasing interplay between communication and computation has fed the vision of real-time networked control systems. To obtain timel…
Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding
Jincheng Dai, Ping Zhang, Kai Niu +3
Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable co…
Energy-efficient Caching and Task offloading for Timely Status Updates in UAV-assisted VANETs
Nan Hu, Xiaoqi Qin, Nan Ma +3
Intelligent edge network is maturing to enable smart and efficient transportation systems. In this letter, we consider unmanned aerial vehicle (UAV)-assisted vehicular networks whe…
Wireless Deep Video Semantic Transmission
Sixian Wang, Jincheng Dai, Zijian Liang +5
In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed metho…
Towards Semantic Communications: A Paradigm Shift
Kai Niu, Jincheng Dai, Shengshi Yao +4
The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficient practical systems. Classical communication system…
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning
Y. Li, X. Qin, H. Chen +2
Federated Learning (FL) over wireless network enables data-conscious services by leveraging the ubiquitous intelligence at network edge for privacy-preserving model training. As th…
Nonlinear Transform Source-Channel Coding for Semantic Communications
Jincheng Dai, Sixian Wang, Kailin Tan +4
In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it ca…
Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding
Jincheng Dai, Ping Zhang, Kai Niu +3
Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable co…