no code implementations • 24 Apr 2024 • Jincheng Dai, Zhuowei Huang, Haiyun Jiang, Chen Chen, Deng Cai, Wei Bi, Shuming Shi
Large Language Models (LLMs), despite their impressive performance on a wide range of tasks, require significant GPU memory and consume substantial computational resources.
1 code implementation • 18 Aug 2023 • Ke Yang, Sixian Wang, Jincheng Dai, Xiaoqi Qin, Kai Niu, Ping Zhang
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.
no code implementations • 30 Jun 2023 • Ping Zhang, Heng Yang, Zhiyong Feng, Yanpeng Cui, Jincheng Dai, Xiaoqi Qin, Jinglin Li, Qixun Zhang
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 shifting the paradigm of wireless communication design from naive maximalist approaches to intelligent value-based approaches.
no code implementations • 26 Mar 2023 • Sixian Wang, Jincheng Dai, Xiaoqi Qin, Zhongwei Si, Kai Niu, Ping Zhang
First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain.
no code implementations • 26 Mar 2023 • Sixian Wang, Jincheng Dai, Xiaoqi Qin, Kai Niu, Ping Zhang
We first focus on those two paradigms of NeurJSCC by identifying their common and different components in building end-to-end communication systems.
no code implementations • 8 Nov 2022 • Jincheng Dai, Sixian Wang, Ke Yang, Kailin Tan, Xiaoqi Qin, Zhongwei Si, Kai Niu, Ping Zhang
Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain.
2 code implementations • 2 Nov 2022 • Ke Yang, Sixian Wang, Jincheng Dai, Kailin Tan, Kai Niu, Ping Zhang
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT).
no code implementations • 4 Aug 2022 • Jincheng Dai, Ping Zhang, Kai Niu, Sixian Wang, Zhongwei Si, Xiaoqi Qin
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 communications.
no code implementations • 26 May 2022 • Sixian Wang, Jincheng Dai, Zijian Liang, Kai Niu, Zhongwei Si, Chao Dong, Xiaoqi Qin, Ping Zhang
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.
no code implementations • 26 May 2022 • Jun Wang, Sixian Wang, Jincheng Dai, Zhongwei Si, Dekun Zhou, Kai Niu
However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM).
no code implementations • 25 Jan 2022 • Sixian Wang, Ke Yang, Jincheng Dai, Kai Niu
In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view transmitted over wireless channels and reconstructed in the center node.
1 code implementation • 21 Dec 2021 • Jincheng Dai, Sixian Wang, Kailin Tan, Zhongwei Si, Xiaoqi Qin, Kai Niu, Ping Zhang
In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding.