no code implementations • 20 Apr 2024 • Lanxin He, Zheng Wang, Yongming Huang
The Langevin sampling method relies on an accurate score matching while the existing massive multiple-input multiple output (MIMO) Langevin detection involves an inevitable singular value decomposition (SVD) to calculate the posterior score.
no code implementations • 16 Apr 2024 • Yongming Huang, Xiaohu You, Hang Zhan, Shiwen He, Ningning Fu, Wei Xu
In this paper, we propose a solution, termed the pervasive multi-level (PML) native AI architecture, which integrates the concept of knowledge graph (KG) into the intelligent operational manipulations of mobile networks, resulting in the establishment of a wireless data KG.
no code implementations • 8 Apr 2024 • Taotao Ji, Meng Hua, ChunGuo Li, Yongming Huang, Luxi Yang
To reap the active and passive beamforming gain in an intelligent reflecting surface (IRS)-aided wireless network, a typical way is to first acquire the channel state information (CSI) relying on the pilot signal, and then perform the joint beamforming design.
no code implementations • 20 Feb 2024 • Chunmei Xu, Shengheng Liu, Yongming Huang, Bjorn Ottersten, Dusit Niyato
Extensive simulation results are presented to demonstrate the effectiveness of the proposed random aggregate beamforming-based scheme as well as the refined method.
no code implementations • 29 Nov 2023 • Zhenyu Tao, Wei Xu, Yongming Huang, XiaoYun Wang, Xiaohu You
Digital twin, which enables emulation, evaluation, and optimization of physical entities through synchronized digital replicas, has gained increasingly attention as a promising technology for intricate wireless networks.
no code implementations • 28 Nov 2023 • Zhengming Zhang, Yongming Huang, Cheng Zhang, Qingbi Zheng, Luxi Yang, Xiaohu You
In this paper, a framework consisting of a digital twin and reinforcement learning agents is present to handle the issue.
no code implementations • 27 Mar 2023 • Kunyang Sun, Wei Lin, Haoqin Shi, Zhengming Zhang, Yongming Huang, Horst Bischof
This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor.
no code implementations • 8 Mar 2023 • Mengguan Pan, Shengheng Liu, Peng Liu, Wangdong Qi, Yongming Huang, Wang Zheng, Qihui Wu, Markus Gardill
Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas.
no code implementations • 28 Feb 2023 • Cheng-Xiang Wang, Xiaohu You, Xiqi Gao, Xiuming Zhu, Zixin Li, Chuan Zhang, Haiming Wang, Yongming Huang, Yunfei Chen, Harald Haas, John S. Thompson, Erik G. Larsson, Marco Di Renzo, Wen Tong, Peiying Zhu, Xuemin, Shen, H. Vincent Poor, Lajos Hanzo
A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc.
no code implementations • 14 Feb 2023 • Zhenyu Tao, Yongliang Guo, Guanghui He, Yongming Huang, Xiaohu You
However, the intricate structure and diverse functionalities of the existing 5G core network, especially the control plane, present challenges in constructing core network digital twins.
no code implementations • 6 Feb 2023 • Wenzhe Fan, Shengheng Liu, ChunGuo Li, Yongming Huang
Next, we develop a deep ADMM unfolding network (DAUN) to learn the ADMM parameter settings from the training data and a position refinement algorithm is proposed for DAUN.
1 code implementation • 26 Dec 2022 • Xinghua Jia, Peng Liu, Wangdong Qi, Shengheng Liu, Yongming Huang, Wang Zheng, Mengguan Pan, Xiaohu You
Channel-state-information-based localization in 5G networks has been a promising way to obtain highly accurate positions compared to previous communication networks.
no code implementations • 22 Aug 2022 • Shiwen He, Yeyu Ou, Liangpeng Wang, Hang Zhan, Peng Ren, Yongming Huang
Finally, the results show that the classification accuracy of the proposed model is better than the existing unsupervised graph neural network models, such as VGAE and ARVGE.
no code implementations • 2 Jul 2022 • Chong Zheng, Shengheng Liu, Yongming Huang, Wei zhang, Luxi Yang
In this article, we propose an unsupervised and privacy-preserving popularity prediction framework for MEC-enabled IIoT.
no code implementations • 24 Jun 2022 • Jiao Zhang, Min Zhu, Bingchang Hua, Mingzheng Lei, Yuancheng Cai, Liang Tian, Yucong Zou, Like Ma, Yongming Huang, Jianjun Yu, Xiaohu You
We demonstrate the first practical real-time dual-channel fiber-THz-fiber 2 * 2 MIMO seamless integration system with a record net data rate of 2 * 103. 125 Gb/s at 385 GHz and 435 GHz over two spans of 20 km SSMF and 3 m wireless link.
no code implementations • 20 Jun 2022 • Mengguan Pan, Peng Liu, Shengheng Liu, Wangdong Qi, Yongming Huang, Xiaohu You, Xinghua Jia, XiaoDong Li
Secondly, based on the deployment reality that 5G picocell gNBs only have a small-scale antenna array but have a large signal bandwidth, the proposed scheme decouples the estimation of time-of-arrival (TOA) and direction-of-arrival (DOA) to reduce the huge complexity induced by two-dimensional joint processing.
no code implementations • 15 Apr 2022 • Zihuan Mao, Shengheng Liu, Yimin D. Zhang, Leixin Han, Yongming Huang
In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA).
no code implementations • 8 Mar 2022 • Wei Wang, Bincheng Zhu, Yongming Huang, Wei zhang
For the constellation design, we adopt the amplitude and phase-shift keying (APSK) constellation and optimize the parameters of APSK such as ring number, ring radius, and inter-ring phase difference.
no code implementations • 20 Oct 2021 • Shengheng Liu, Chong Zheng, Yongming Huang, Tony Q. S. Quek
In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks.
no code implementations • 2 Apr 2021 • Chong Zheng, Shengheng Liu, Yongming Huang, Luxi Yang
Virtual reality (VR) is promising to fundamentally transform a broad spectrum of industry sectors and the way humans interact with virtual content.
no code implementations • 29 Mar 2021 • Shengheng Liu, Zihuan Mao, Yimin D. Zhang, Yongming Huang
The atomic-norm representation of the measurements from the interpolated virtual array is considered, and the equivalent dual-variable rank minimization problem is formulated and solved using a cyclic optimization approach.
no code implementations • ICCV 2021 • Kunyang Sun, Haoqing Shi, Zhengming Zhang, Yongming Huang
Image-level weakly supervised semantic segmentation is a challenging task.
no code implementations • 26 Nov 2020 • Yongming Huang, Shengheng Liu, Cheng Zhang, Xiaohu You, Hequan Wu
Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile communications will shift from facilitating interpersonal communications to supporting Internet of Everything (IoE), where intelligent communications with full integration of big data and artificial intelligence (AI) will play an important role in improving network efficiency and providing high-quality service.
no code implementations • 19 May 2020 • Wei Huang, Yong Zeng, Yongming Huang
This paper investigates the achievable rate region of the multiple-input single-output (MISO) interference channel aided by intelligent reflecting surfaces (IRSs).
9 code implementations • CVPR 2020 • Hao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen, Yongming Huang, Youliang Yan
The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference.
Ranked #13 on Real-time Instance Segmentation on MSCOCO
3 code implementations • 18 Oct 2019 • Bin Zhang, Shenyao Jin, Yili Xia, Yongming Huang, Zixiang Xiong
Deep learning based image denoising methods have been extensively investigated.
no code implementations • 18 Jul 2018 • Yuan Liu, Yuancheng Wang, Nan Li, Xu Cheng, Yifeng Zhang, Yongming Huang, Guojun Lu
We propose an attention-based approach to give a discrimination between texture areas and smooth areas.