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arXiv 2023年

作者： Guo, Ziqi Niu, Yong Mao, Shiwen Zhang, Changming Wang, Ning Zhong, Zhangdui Ai, Bo The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Research Institute of Intelligent Networks Zhejiang Lab Hangzhou311121 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China

The concept of age of information (AoI) has been proposed to quantify information freshness, which is crucial for time-sensitive applications. However, in millimeter wave (mmWave) communication systems, the link blockage caused by obstacles and the severe path loss greatly impair the freshness of information received by the user equipments (UEs). In this paper, we focus on reconfigurable intelligent surface (RIS)-assisted mmWave communications, where beamforming is performed at transceivers to provide directional beam gain and a RIS is deployed to combat link blockage. We aim to maximize the system sum rate while satisfying the information freshness requirements of UEs by jointly optimizing the beamforming at transceivers, the discrete RIS reflection coefficients, and the UE scheduling strategy. To facilitate a practical solution, we decompose the problem into two subproblems. For the first per-UE data rate maximization problem, we further decompose it into a beamforming optimization subproblem and a RIS reflection coefficient optimization subproblem. Considering the difficulty of channel estimation, we utilize the hierarchical search method for the former and the local search method for the latter, and then adopt the block coordinate descent (BCD) method to alternately solve them. For the second scheduling strategy design problem, a low-complexity heuristic scheduling algorithm is designed. Simulation results show that the proposed algorithm can effectively improve the system sum rate while satisfying the information freshness requirements of all UEs. Copyright © 2023, The Authors. All rights reserved.

关键词： Scheduling algorithms

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Deep Learning Based MIMO Transmission with Precoding and Radio Transformer networks

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Procedia computer Science 2021年 187卷 396-401页

作者： Wenqi Cui Anming Dong Yi Cao Chuanting Zhang Jiguo Yu Sufang Li School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan 250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China School of Information Science and Engineering University of Jinan Jinan 250022 China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

In this paper, we study MIMO transmission schemes based on deep learning (DL). We propose a novel DL-based MIMO communication structure by combing a beamforming network at the transmitter side and a radio transformer network (RTN) at the receiver side. Compared with the classical DL-based MIMO communication systems, the interference is potentially mitigated by a precoding network and a RTN network, which is thus beneficial to improve the performance of signal detection. Simulation results show that the proposed scheme outperforms the classical MIMO transmission schemes in terms of bit error rate (BER).

关键词： Deep Learning Multiple-Input Multiple Output (MIMO) Precoding Radio Transformer Network

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Efficient Ring Signature Scheme Without Random Oracle from Lattices

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Chinese Journal of Electronics 2019年第2期28卷 266-272页

作者： GAO Wen HU Yupu WANG Baocang CHEN Jiangshan WANG Xin College of Electrical and Information Engineering Shaanxi University of Science and Technology State Key Laboratory of Integrated Service Networks Xidian University School of Mathematics and Statistics Minnan Normal University School of Computer Science Shaanxi Normal University

Among post-quantum alternatives, latticebased cryptography is the most promising one, due to its simple operations, reduction from aver-age-case to worstcase hardness, and supporting of rich functionalities. Ring signature enables a user to sign anonymously on behalf of an adaptively chosen group, and has multiple applications in anonymous e-voting, anonymous authentication, whistle blowing etc. However, most lattice-based ring signature schemes were constructed in the random oracle model from lattice basis delegation and they suffer large verification key sizes as a common disadvantage. This work proposes an efficient ring signature scheme from lattice basis delegation without random oracle based on the extended split-SIS problem, whose security is approximately as hard as the worst-case SIVP problem. Our scheme is proved to be anonymous and existentially unforgeable under latticebased assumptions. Finally, the verification key size is significantly reduced to a small constant, instead of increasing linearly with the number of ring members.

关键词： Lattice-based cryptography Digital signature Ring signature

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A two-branch neural network for non-homogeneous dehazing via ensemble learning

arXiv

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arXiv 2021年

作者： Yu, Yankun Liu, Huan Fu, Minghan Chen, Jun Wang, Xiyao Wang, Keyan Department of Electrical and Computer Engineering McMaster University Hamilton Canada Center for Research on Intelligent System and Engineering Institute of Automation University of Chinese Academy of Sciences Beijing China State Key Laboratory of Integrated Service Networks Xidian University Xi’an China

Recently, there has been rapid and significant progress on image dehazing. Many deep learning based methods have shown their superb performance in handling homogeneous dehazing problems. However, we observe that even if a carefully designed convolutional neural network (CNN) can perform well on large-scaled dehazing benchmarks, the network usually fails on the non-homogeneous dehazing datasets introduced by NTIRE challenges. The reasons are mainly in two folds. Firstly, due to its nonhomogeneous nature, the non-uniformly distributed haze is harder to be removed than the homogeneous haze. Secondly, the research challenge only provides limited data (there are only 25 training pairs in NH-Haze 2021 dataset). Thus, learning the mapping from the domain of hazy images to that of clear ones based on very limited data is extremely hard. To this end, we propose a simple but effective approach for non-homogeneous dehazing via ensemble learning. To be specific, we introduce a two-branch neural network to separately deal with the aforementioned problems and then map their distinct features by a learnable fusion tail. We show extensive experimental results to illustrate the effectiveness of our proposed method. The source code is available at https://***/liuh127/Two-branch-dehazing. Copyright © 2021, The Authors. All rights reserved.

关键词： Demulsification

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Modulation Recognition with Alpha-Stable Noise Over Fading Channels 3rd

Modulation Recognition with Alpha-Stable Noise Over Fading C...

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3rd EAI International Conference on 6G for Future Wireless networks, 6GN 2020

作者： Zhang, Lingfei Liu, Mingqian Ma, Jun Liu, Chengqiao College of Physics and Electronic Information Engineering Qinghai Nationalities University Xining810007 China School of Computer Qinghai Normal University Xining810003 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China

ISBN: (纸本)9783030639402

This paper proposes a method based on kernel density estimation (KDE) and expectation condition maximization (ECM) to realize digital modulation recognition over fading channels with non-Gaussian noise in the cognitive radio networks. A compound hypothesis test model is adopt here. The KDE method is used to estimate the probability density function of non-Gaussian noise, and the improved ECM algorithm is used to estimate the fading channel parameters. Numerical results show that the proposed method is robust to the noise type over fading channels. Moreover, when the GSNR is 10 dB, the correct recognition rate for the digital modulation recognition under non-Gaussian noise is more than 90%. Gaussian noise, and the improved ECM algorithm is used to estimate the fading channel parameters. Numerical results show that the proposed method is robust to the noise type over fading channels. Moreover, when the GSNR is 10 dB, the correct recognition rate for the digital modulation recognition under non-Gaussian noise is more than 90%. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Cognitive radio

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High-coherence parallelization in integrated photonics

arXiv

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arXiv 2023年

作者： Zhang, Xuguang Zhou, Zixuan Guo, Yijun Zhuang, Minxue Jin, Warren Shen, Bitao Chen, Yujun Huang, Jiahui Tao, Zihan Jin, Ming Chen, Ruixuan Ge, Zhangfeng Fang, Zhou Zhang, Ning Liu, Yadong Cai, Pengfei Hu, Weiwei Shu, Haowen Pan, Dong Bowers, John E. Wang, Xingjun Chang, Lin State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA United States Frontiers Science Center for Nano-optoelectronics Peking University Beijing China SiFotonics Technologies Co. Ltd. Beijing China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, building coherent systems in integrated photonics previously came at great expense in hardware integration and energy efficiency: the lack of a power-efficient way to generate highly coherent light necessitates bulky lasers and amplifiers, while frequency and phase recovery schemes require huge digital signal processing resources. In this work, we demonstrate a high-coherence parallelization strategy that facilitates advanced integrated coherent systems at a minimum price. Using a self-injection locked microcomb to injection lock a distributed feedback laser array, we boost the microcomb power by a record high gain of up to 60 dB on chip with no degradation in coherence. This strategy enables tens of highly coherent channels with an intrinsic linewidth down to the 10 Hz level and power of more than 20 dBm. The overall electrical to optical wall-plug efficiency reaches 19%, comparable with that of the state-of-the-art semiconductor lasers. Driven by this parallel source, we demonstrate a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s. Importantly, the high coherence we achieve reduces the coherent-related DSP consumption by 99.999% compared with the traditional III-V laser pump scheme. This work paves a way to realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Interpretation-Enabled Software Reuse Detection Based on a Multi-level Birthmark Model

Interpretation-Enabled Software Reuse Detection Based on a M...

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International Conference on Software engineering (ICSE)

作者： Xi Xu Qinghua Zheng Zheng Yan Ming Fan Ang Jia Ting Liu Key Laboratory of Intelligent Networks and Network Security Ministry of Education China School of Computer Science and Technology Xi’an Jiaotong University China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Communications and Networking Aalto University Finland School of Cyber Science and Engineering Xi’an Jiaotong University China

ISBN: (纸本)9781665402965;9781665448314

Software reuse, especially partial reuse, poses legal and security threats to software development. Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand, current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalization to extract core semantics of instructions. For efficiently detecting reused functions, a process for "intent search based on anchor recognition" is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94.8% recall. Moreover, it is resilient to cross-compilation, outperforming state-of-the-art approaches.

关键词： Law Semantics Libraries Security Software reusability Software engineering Resilience

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HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution

arXiv

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arXiv 2021年

作者： Wu, Chaoxiong Li, Jiaojiao Song, Rui Li, Yunsong Du, Qian State Key Laboratory of Integrated Service Networks Xidian University Xi'An710071 China CAS Key Laboratory of Spectral Imaging Technology Xi'An710119 China Department of Electronic and Computer Engineering Mississippi State University StarkvilleMS39762 United States

Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart. Due to the one-to-many nature of the SSR problem, a single RGB image can be reprojected to many HSIs. The key to tackle this illposed problem is to plug into multi-source prior information such as the natural RGB spatial context-prior, deep feature-prior or inherent HSI statistical-prior, etc., so as to improve the confidence and fidelity of reconstructed spectra. However, most current approaches only consider the general and limited priors in their designing the customized convolutional neural networks (CNNs), which leads to the inability to effectively alleviate the degree of ill-posedness. To address the problematic issues, we propose a novel holistic prior-embedded relation network (HPRN) for SSR. Basically, the core framework is delicately assembled by several multi-residual relation blocks (MRBs) that fully facilitate the transmission and utilization of the low-frequency content prior of RGB signals. Innovatively, the semantic prior of RGB input is introduced to identify category attributes and a semantic-driven spatial relation module (SSRM) is put forward to perform the feature aggregation among the clustered similar characteristics using a semantic-embedded relation matrix. Additionally, we develop a transformer-based channel relation module (TCRM), which breaks the habit of employing scalars as the descriptors of channel-wise relations in the previous deep feature-prior and replaces them with certain vectors, together with Transformerstyle feature interactions, supporting the representations to be more discriminative. In order to maintain the mathematical correlation and spectral consistency between hyperspectral bands, the second-order prior constraints (SOPC) are incorporated into the loss function to guide the HSI reconstruction process. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical resolving power

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Heart Disease Prediction Algorithm Based on Ensemble Learning

Heart Disease Prediction Algorithm Based on Ensemble Learnin...

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International Conference on Dependable Systems and Their Applications (DSA)

作者： Ke Yuan Longwei Yang Yabing Huang Zheng Li School of Computer and Information Engineering Henan University International Joint Research Laboratory for Cooperative Vehicular Networks of Henan School of Computer and Information Engineering Henan University

ISBN: (数字)9780738124223

ISBN: (纸本)9781665419536

Nowadays, heart disease is one of the important causes of human deaths. According to statistics, deaths caused by heart disease account for about one-third of all deaths in the world. With further research, the use of machine learning to predict heart disease has become an essential method to prevent and treat heart disease. In recent years, machine learning based on big data analysis has been widely used in various software applications, but it has not been used on a large scale in disease prediction. In this article, we propose a new algorithm named hybrid gradient boosting decision tree with logistic regression (HGBDTLR) based on ensemble learning to improve the accuracy of machine learning in heart disease prediction. The actual results prove that the prediction accuracy of HGBDTLR algorithm can reach 91.8% in the Cleveland heart disease data set.

关键词： Heart Training Machine learning algorithms Software algorithms Big Data Prediction algorithms Diseases

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Interpretation-enabled software reuse detection based on a multi-level birthmark model

arXiv

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arXiv 2021年

作者： Xu, Xi Zheng, Qinghua Yan, Zheng Fan, Ming Jia, Ang Liu, Ting Key Laboratory of Intelligent Networks and Network Security Ministry of Education China School of Computer Science and Technology Xi'an Jiaotong University China School of Cyber Science and Engineering Xi'an Jiaotong University China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Communications and Networking Aalto University Finland

关键词： computer software reusability

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