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Your search keyword '"Shi, Zhihua"' showing total 318 results
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318 results on '"Shi, Zhihua"'

1. Interference Cancellation Based Neural Receiver for Superimposed Pilot in Multi-Layer Transmission

Author

Xiao, Han, Tian, Wenqiang, Jin, Shi, Liu, Wendong, Shen, Jia, Shi, Zhihua, and Zhang, Zhi

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

In this paper, an interference cancellation based neural receiver for superimposed pilot (SIP) in multi-layer transmission is proposed, where the data and pilot are non-orthogonally superimposed in the same time-frequency resource. Specifically, to deal with the intra-layer and inter-layer interference of SIP under multi-layer transmission, the interference cancellation with superimposed symbol aided channel estimation is leveraged in the neural receiver, accompanied by the pre-design of pilot code-division orthogonal mechanism at transmitter. In addition, to address the complexity issue for inter-vendor collaboration and the generalization problem in practical deployments, respectively, this paper also provides a fixed SIP (F-SIP) design based on constant pilot power ratio and scalable mechanisms for different modulation and coding schemes (MCSs) and transmission layers. Simulation results demonstrate the superiority of the proposed schemes on the performance of block error rate and throughput compared with existing counterparts.

Published
2024

4. Knowledge-driven Meta-learning for CSI Feedback

Author

Xiao, Han, Tian, Wenqiang, Liu, Wendong, Guo, Jiajia, Zhang, Zhi, Jin, Shi, Shi, Zhihua, Guo, Li, and Shen, Jia

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed., Comment: arXiv admin note: text overlap with arXiv:2301.13475

Published
2023

9. A Knowledge-Driven Meta-Learning Method for CSI Feedback

Author

Xiao, Han, Tian, Wenqiang, Liu, Wendong, Zhang, Zhi, Shi, Zhihua, Guo, Li, and Shen, Jia

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output (MIMO) systems. Recently, deep learning (DL) has been introduced to enhance CSI feedback in massive MIMO application, where the massive collected training data and lengthy training time are costly and impractical for realistic deployment. In this paper, a knowledge-driven meta-learning solution for CSI feedback is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during the target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of channel, so that the DL model initialized by meta training can rapidly fit into a new target scenario with higher performance using only a few actually collected data in the target retraining phase. The method greatly reduces the demand for the number of actual collected data, as well as the cost of training time for realistic deployment. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.

Published
2023

35. LncRNA NONHSAT227443.1 Confers Esophageal Squamous Cell Carcinoma Chemotherapy Resistance by Activating PI3K/AKT Signaling via Targeting MRTFB.

Author

Wang, Yuchen, Wang, Yingying, Zhang, Jinze, Shi, Zhihua, and Liu, Junfeng

Subjects
PI3K/AKT pathway, SQUAMOUS cell carcinoma, PACLITAXEL, ESOPHAGEAL cancer, CANCER chemotherapy, LINCRNA
Abstract

Introduction: Esophageal cancer presents significant challenges due to limited treatment options and poor prognosis, particularly in advanced stages. Dysregulated long non-coding RNAs (lncRNAs) are implicated in cancer progression and treatment resistance. This study investigated the roles of dysregulated lncRNA NONHSAT227443.1, identified through lncRNA-seq, and its downstream target gene MRTFB in esophageal squamous cell carcinoma (ESCC). Methods: Dysregulated lncRNAs were identified through lncRNA-seq in esophageal cancer tissues with varying chemotherapy response. The regulatory interaction of overexpressed NONHSAT227443.1 was assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Functional assays, including cell viability, cell proliferation, and flow cytometry analyses, were performed to comprehensively investigate the influence of NONHSAT227443.1 and its downstream molecules on ESCC. Results: NONHSAT227443.1 was significantly overexpressed in paclitaxel plus platinum chemotherapy non-responders and esophageal cancer cell lines. Chemotherapy exposure led to diminished NONHSAT227443.1 expression. NONHSAT227443.1 negatively regulated MRTFB expression, and their combined dysregulation correlated with increased cancer activity, proliferation, and suppressed apoptosis. Diminished MRTFB expression was associated with PI3K/AKT pathway activation. Conclusion: Our study provides insights into NONHSAT227443.1 and MRTFB roles in esophageal cancer, contributing to aggressive traits and treatment resistance. NONHSAT227443.1 and MRTFB may serve as potential therapeutic targets to enhance the response to paclitaxel plus platinum chemotherapy in non-responsive cases. [ABSTRACT FROM AUTHOR]

Published
2024
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38. List of contributors

Author

Chen, Wenhong, primary, Cui, Shengjiang, additional, Ding, Yi, additional, Du, Zhongda, additional, Fang, Yun, additional, Fu, Zhe, additional, Guo, Li, additional, Guo, Yali, additional, He, Chuanfeng, additional, Hu, Rongyi, additional, Hu, Yi, additional, Huang, Yingpei, additional, Li, Haitao, additional, Lin, Xue, additional, Liang, Bin, additional, Lin, Hao, additional, Lin, Kevin, additional, Lin, Yanan, additional, Liu, Jianhua, additional, Liu, Qifei, additional, Liu, Wendong, additional, Liu, Yang, additional, Lu, Qianxi, additional, Shao, Shuai, additional, Shen, Jia, additional, Shi, Cong, additional, Shi, Yongsheng, additional, Shi, Zhihua, additional, So, Tricci, additional, Su, Jinxi, additional, Tian, Jiejiao, additional, Tian, Wenqiang, additional, Wang, Shukun, additional, Wu, Zuomin, additional, Xiao, Han, additional, Xing, Jinqiang, additional, Xu, Jing, additional, Xu, Weijie, additional, Xu, Yang, additional, Yang, Haorui, additional, Yang, Ning, additional, You, Xin, additional, Zhan, Wenhao, additional, Zhang, Shichang, additional, Zhang, Zhi, additional, Zhao, Nande, additional, Zhao, Zhenshan, additional, and Zuo, Zhisong, additional

Published
2022
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49. Human-altered soil loss contributes to nearly half of water erosion in China

Author

Wang, Zhen, primary, li, Keke, additional, Yang, Jingya, additional, Wang, Jingyu, additional, Zeng, Yi, additional, Borrelli, Pasquale, additional, Hubacek, Klaus, additional, Hu, Yuanchao, additional, Xu, Baodong, additional, Fang, Nufang, additional, Zeng, Chen, additional, Zhou, Zhanhang, additional, and Shi, Zhihua, additional

Published
2023
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