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Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年 PP卷 1-11页

作者： Lan, Wei Chen, Jianwei Liu, Mingyang Chen, Qingfeng Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Wei Lan is School of Computer Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning Guangxi China School of Computer Electronic and Information Guangxi University Nanning Guangxi China School of Computer Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University Nanning Guangxi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Computer Science and Information Technology La Trobe University Melbourne Victoria Australia

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE

关键词： RNA

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A 94GHz Down-Conversion Mixer with High LO-to-RF Isolation in 130nm SiGe BiCMOS

A 94GHz Down-Conversion Mixer with High LO-to-RF Isolation i...

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IEEE International Wireless Symposium (IWS)

作者： Zhishun Han Jixin Chen Peigen Zhou Zongxiang Wang Yang Chun Zhe Chen Lin Peng State Key Lab. of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation

ISBN: (数字)9798350389999

ISBN: (纸本)9798350390001

In this paper, a down-conversion mixer with operating frequency of 88-96GHz is presented. Based on 130nm SiGe BiCMOS process, the mixer has the characteristics of high isolation. The mixer includes a double-balanced Gilbert core, a local oscillator drive amplifier and an intermediate frequency drive amplifier. A single stage differential amplifier is used in the intermediate frequency output to improve the conversion gain. The mixer and drive amplifier use a current mirror for DC bias to reduce the influence of temperature and process on RF performance. The design adds a ground wall between the LO and RF signal lines, which increases the isolation well and optimizes the amplitude phase imbalance of the input and output balun. The measured input 1dB compression point (IP1dB) is -9dBm@95GHz, the peak conversion gain is 9.3dB@88GHz in the frequency range of 88-96GHz, the maximum LO-to-RF isolation is 57.4dB and the LO-to-IF isolation is 67.6dB, and the RF-to-IF isolation is 37.6dB.

关键词： Wireless communication Temperature measurement Radio frequency Transmitters RF signals Millimeter wave technology BiCMOS integrated circuits Frequency measurement Mixers Silicon germanium

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A 115-140GHz Power Amplifier With High Output Power in SiGe BiCMOS

A 115-140GHz Power Amplifier With High Output Power in SiGe ...

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IEEE International Wireless Symposium (IWS)

作者： Peng Yang Zhe Chen Zongxiang Wang Yue Qi Pinqing Wang Peigen Zhou Lin Peng PinPin Yan Jixin Chen Wei Hong State Key Lab. of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation

ISBN: (数字)9798350389999

ISBN: (纸本)9798350390001

This paper presents a power amplifier (PA) operating in the frequency range of 115-140 GHz, implemented using a 0.13 μm germanium-silicon (SiGe) BiCMOS process. The single stage amplifier employs a cascode structure and integrates gmboosting technology to enhance gain. To ensure superior broadband performance at high frequencies, input and output matching are achieved through the utilization of a Marchand balun. The PA operates from 115 GHz to 140 GHz (3dB bandwidth) with a peak gain of 23.7 dB at 130GHz. At 116 GHz, the amplifier achieves a saturated output power of 16.2 dBm, with the peak power added efficiency (PAE) of 10.2%. The total chip size is 0.92×0.73 mm 2 .

关键词： Wireless communication Transmitters Baluns Millimeter wave technology Power amplifiers BiCMOS integrated circuits Power generation Gain Silicon germanium Periodic structures

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DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green communications and networking 2025年

作者： Zhang, Zheng Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi214122 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering State Key laboratory of Space Network and Communications Tsinghua University Beijing100084 China Xidian University State Key Lab. of ISN School of Telecom-munications Engineering Xi'an710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Department of Electrical and Computer Engineer-ing Hong Kong

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI. © 2017 IEEE.

关键词： Deep reinforcement learning

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Rate-Splitting Multiple Access Enabled Green Probabilistic Semantic Communication over Wireless networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green communications and networking 2025年

作者： Xu, Ruopeng Yang, Zhaohui Mao, Yijie Huang, Chongwen Yang, Qianqian Xu, Lexi Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering China Hangzhou310027 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Zhejiang Prov. Key Lab. of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China China United Network Communications Corporation Research Institute Beijing100048 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China

In this paper, we propose a multi-user green probabilistic semantic communication (PSC) system for semantic communication (SemCom) facilitated by probabilistic knowledge graphs (PKGs), which are knowledge graphs (KGs) integrated with probability to represent semantic information. In the considered model, semantic information of different users is represented by PKGs. On this basis, we design a semantic compression model for multi-user downlink task-oriented SemCom, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Utilizing rate-splitting multiple access (RSMA) technology, the transmitted messages are splitted into shared and private ones. Considering the limited wireless resources and constraint in semantic communication, an optimization problem with the goal of minimizing system energy consumption comprehensively considering the computation and communication process is formulated. In order to address the optimization problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design with successive convex approximation (SCA) method, semantic compression ratio optimization with variable substitution method, and computation capacity allocation with obtaining closed-form optimal solution. Simulation results verify the effectiveness of the proposed algorithm. © 2017 IEEE.

关键词： Knowledge graph

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PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X

arXiv

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arXiv 2025年

作者： Wu, Qiong Ji, Maoxin Fan, Pingyi Wang, Kezhi Cheng, Nan Chen, Wen Letaief, Khaled B. The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The Department of Electronic Engineering State Key laboratory of Space Network and Communications Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The Department of Computer Science Brunel University Middlesex LondonUB8 3PH United Kingdom The State Key Lab. of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Hong Kong

On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth integration of vehicles from the merging lane onto the main road, optimizing both fuel efficiency and passenger comfort. Furthermore, we recognize the impact of vehicle-to-vehicle (V2V) communication on control strategies and introduce an enhanced protocol leveraging Cellular Vehicle-to-Everything (C-V2X) Mode 4. This protocol aims to reduce the Age of Information (AoI) and improve communication reliability. In our simulations, we employ two AoI-based metrics to rigorously assess the protocol's effectiveness in autonomous driving scenarios. By combining the NS3 network simulator with Python, we simulate V2V communication and vehicle control simultaneously. The results demonstrate that the enhanced C-V2X Mode 4 outperforms the standard version, while the proposed control scheme ensures safe and reliable vehicle operation during on-ramp merging. Copyright © 2025, The Authors. All rights reserved.

关键词： Problem oriented languages

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

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arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is availab.e, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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Blockchain-enabled Wireless IoT networks with Multiple Communication Connections

Blockchain-enabled Wireless IoT Networks with Multiple Commu...

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IEEE International Conference on communications (ICC)

作者： Jingxin Zhu Yao Sun Lei Zhang Bin Cao Gang Feng Muhammad Ali Imran Glasgow College University of Electronic Science and Technology of China Chengdu China National Key Lab. on Communications University of Electronic Science and Technology of China Chengdu China James Watt School of Engineering University of Glasgow Glasgow Institute of Network Technology Beijing University of Post and Telecommunications Beijing China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Blockchain-enabled wireless network has been recognized as an emerging network architecture to be widely employed into the Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without the involvement of a third party. However, the uncertainty and vulnerability of wireless channels among the IoT nodes may pose a serious challenge to facilitate the deployment of blockchain in wireless networks. In this paper, we first present a generic system model for blockchain enabled wireless networks with multiple communication connections, where the number of communication connections between a client IoT node and the blockchain full nodes can be any arbitrary positive integer to satisfy different security requirements. Based on the proposed spatial-temporal network model, we theoretically calculate the transmission successful probability and the required communication throughput to support a wireless blockchain network. Finally, simulation results validate the accuracy of our theoretical analysis.

关键词： Interference Communication system security Throughput Wireless networks

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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Performance Analysis for Blockchain Driven Wireless IoT Systems Based on Tempo-Spatial Model

Performance Analysis for Blockchain Driven Wireless IoT Syst...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Yao Sun Lei Zhang Gang Feng Bowen Yang Bin Cao Muhammad Imran National Key Lab. on Communications University of Electronic Science and Technology of China Chengdu China School of Engineering University of Glasgow Glasgow Institute of Network Technology Beijing University of Posts and Telecommunications Beijing China University of Glasgow Glasgow UK

Blockchain has shown a great potential for Internet of Things (IoT) systems to establish trust and consensus mechanisms with no involvement of any third party. It has been not clear how the low complexity devices and the wireless communications among them can pose constraints on the blockchain enabled IoT systems. In this paper, we establish a fundamental analysis model to underpin the blockchain enabled IoT system. By considering spatio-temporal domain Poisson distribution, i.e., node geographical distribution and transaction arrival rate in time domain are both modeled as Poisson point process (PPP), we first derive the distribution of signal-to-interference-plus-noise ratio (SINR), blockchain transaction successful rate as well as overall throughput. Then, based on the analytical model, we design an optimal full function node deployment for blockchain system to achieve the maximum transaction throughput with the minimum full function node density. Numerical results validate the accuracy of our theoretical analysis and evaluate the relationship between blockchain full function node deployment and the density of IoT nodes.

关键词： Blockchain Interference Analytical models Throughput Signal to noise ratio Wireless communication Probability density function

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