no code implementations • 23 Jan 2024 • Yue Hu, Xianghe Pang, Xiaoqi Qin, Yonina C. Eldar, Siheng Chen, Ping Zhang, Wenjun Zhang
Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration.
no code implementations • 2 Jan 2024 • Dongxu Li, Jianhao Huang, Chuan Huang, Xiaoqi Qin, Han Zhang, Ping Zhang
For the case with unknown semantic source distribution, while only a set of the source samples is available, we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution.
no code implementations • 18 Dec 2023 • Keyi Ju, Xiaoqi Qin, Hui Zhong, Xinyue Zhang, Miao Pan, Baoling Liu
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process.
no code implementations • 10 Nov 2023 • Xianxin Song, Xinmin Li, Xiaoqi Qin, Jie Xu, Tony Xiao Han, Derrick Wing Kwan Ng
Accordingly, we analyze the sensing signal-to-noise ratio (SNR) performance for a target detection scenario and the estimation Cram\'er-Rao bound (CRB) performance for a target's direction-of-arrival (DoA) estimation scenario, in cases where the transmit beamforming at the BS and the reflective beamforming at the IRS are jointly optimized.
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 • 10 Aug 2023 • Xianxin Song, Xinmin Li, Xiaoqi Qin, Jie Xu
This paper compares the signal-to-noise ratio (SNR) performance between the fully-passive intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing versus its semi-passive counterpart.
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 • 30 Jun 2023 • Xianxin Song, Xiaoqi Qin, Jie Xu, Rui Zhang
Accordingly, we model two types of CU receivers, namely Type-I and Type-II CU receivers, which do not have and have the capability of canceling the interference from the sensing signals, respectively.
no code implementations • 18 Apr 2023 • Ran Li, Chuan Huang, Xiaoqi Qin, Lei Yang
Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge.
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 • 27 Feb 2023 • Jianhao Huang, Dongxu Li, Chuan Huang, Xiaoqi Qin, Wei zhang
This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion.
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.
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 • 19 May 2022 • Rui Chen, Dian Shi, Xiaoqi Qin, Dongjie Liu, Miao Pan, Shuguang Cui
In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices.
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.
no code implementations • 7 Jan 2019 • Jiahao Ding, Xiaoqi Qin, Wenjun Xu, Yanmin Gong, Chi Zhang, Miao Pan
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests.