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Cross-domain intelligent cooperative spectrum sensing algorithm based on Federated Learning and Swin-Transformer neural network

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Lingwei Xu Zhihe Gao Yufang Li T. Aaron Gulliver Department of Information Science and Technology Qingdao University of Science & Technology Qingdao 266061 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang 441053 Hubei China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(Guangxi Minzu University) Nanning 530006 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 2Y2 Canada

The deployment of 6th Generation (6G) mobile networks will bring revolutionary changes to organizations and users through higher speeds, lower latency, and more intelligent network management. This will require more efficient spectrum sharing to improve network performance. Cognitive Radio (CR) can be employed to help achieve this goal. However, data collection at the fusion center for cooperative spectrum sensing (CSS) creates a risk of sensing user (SU) data leakage. In this paper, an intelligent CSS algorithm for mobile communication is proposed based on Federated Learning with a Swin-Transformer. First, to achieve the cross-domain process, a Continuous Wavelet Transform (CWT) is used with the normalized time series data to obtain a time-frequency spectrum map with joint time-frequency features. Second, Federated Learning is employed to realize distributed CSS to reduce sensing overhead, improve the security of sensing data, and eliminate the SU data isolation with traditional distributed CSS. Then, a new Swin-Transformer spectrum sensing is proposed for fusion learning of the time-frequency spectrum map. The fusion of Federated Learning and Swin-Transformer neural network provides cooperation with multiple SUs to achieve efficient spectrum sensing using the K -rank fusion criterion. Compared with the Convolutional Neural Network (CNN), ViT-Transformer, Graph Neural Network (GNN) and EfficientNet algorithms, the detection probability of the proposed algorithm has increased by 45 %, and the false alarm probability has decreased by 13 %.

关键词： Cross domain processing Federated learning Mobile cooperative spectrum sensing Swin-transformer

Gaussian noise removal with exponential functions and spectral norm of weighted hankel matrices

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arXiv 2020年

作者： Qiu, Tianyu Liao, Wenjing Guo, Di Liu, Dongbao Wang, Xin Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China School of Mathematics Georgia Institute of Technology AtlantaGA United States School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen China

Exponential functions are powerful tools to model signals in various scenarios, such as magnetic resonance spectroscopy/ imaging, radar, and concatenative text-to-speech synthesis. Exponential signals, however, are usually corrupted by Gaussian noise in practice, raising difficulties in sequential analysis and quantification of the signals. In this work, we propose a denoising method based on low-rank Hankel matrices for exponential signals corrupted by Gaussian noise. An accurate estimate on the spectral norm of weighted Hankel matrices is provided as a theoretical guidance to set the regularization parameter. The bound can be efficiently calculated since it only depends on the standard deviation of the noise and a constant. Aided by the bound, one can easily obtain a good regularization parameter to produce promising denoised results. Our experiments on simulated and magnetic resonance spectroscopy data demonstrate a superior denoising performance of our proposed approach in comparison with the typical Cadzow and the state-ofthe-art QR decomposition methods, especially in the low signalto-noise ratio regime. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

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arXiv 2020年

作者： Liu, Yi Garg, Sahil Nie, Jiangtian Zhang, Yang Xiong, Zehui Kang, Jiawen Shamim Hossain, M. School of Data Science and Technology Heilongjiang University Harbin China Electrical Engineering Department École de technologie supérieure Uni-versité du Québec Montréal Canada Energy Research Institute Interdisciplinary Graduate Programme School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Technology Wuhan University of Technology China Alibaba-NTU Joint Research Institute School of Computer Science and Engineering NTU Singapore Singapore Energy Research Institute Nanyang Technological University Singapore Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user’s private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-k selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50% compared to the federated learning framework that does not use gradient compression scheme. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

Matrix-monotonic optimization − Part II: Multi-variable optimization

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arXiv 2020年

作者： Xing, Chengwen Wang, Shuai Chen, Sheng Ma, Shaodan Poor, H. Vincent Hanzo, Lajos School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Electronics and Computer Science University of Southampton United Kingdom King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

In contrast to Part I of this treatise [1] that focuses on the optimization problems associated with single matrix variables, in this paper, we investigate the application of the matrix-monotonic optimization framework in the optimization problems associated with multiple matrix variables. It is revealed that matrix-monotonic optimization still works even for multiple matrix-variate based optimization problems, provided that certain conditions are satisfied. Using this framework, the optimal structures of the matrix variables can be derived and the associated multiple matrix-variate optimization problems can be substantially simplified. In this paper several specific examples are given, which are essentially open problems. Firstly, we investigate multi-user multiple-input multiple-output (MU-MIMO) uplink communications under various power constraints. Using the proposed framework, the optimal structures of the precoding matrices at each user under various power constraints can be derived. Secondly, we considered the optimization of the signal compression matrices at each sensor under various power constraints in distributed sensor networks. Finally, we investigate the transceiver optimization for multi-hop amplify-and-forward (AF) MIMO relaying networks with imperfect channel state information (CSI) under various power constraints. At the end of this paper, several simulation results are given to demonstrate the accuracy of the proposed theoretical results. Copyright © 2020, The Authors. All rights reserved.

关键词： MIMO systems

RETRACTED ARTICLE: Memory-attended semantic context-aware network for video captioning

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Soft Computing 2021年第2期28卷 425-425页

作者： Chen, Shuqin Zhong, Xian Wu, Shifeng Sun, Zhixin Liu, Wenxuan Jia, Xuemei Xia, Hongxia School of Computer Science and Technology Wuhan University of Technology Wuhan China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology Wuhan China ZhongQianLiYuan Engineering Consulting Co. Ltd Wuhan China

Scoring the terabit/s goal: Broadband connectivity in 6G

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arXiv 2020年

作者： Rajatheva, Nandana Atzeni, Italo Bicaïs, Simon Björnson, Emil Bourdoux, André Buzzi, Stefano D'Andrea, Carmen Doré, Jean-Baptiste Erkucuk, Serhat Fuentes, Manuel Guan, Ke Hu, Yuzhou Huang, Xiaojing Hulkkonen, Jari Jornet, Josep Miquel Katz, Marcos Makki, Behrooz Nilsson, Rickard Panayirci, Erdal Rabie, Khaled Rajapaksha, Nuwanthika Salehi, Mohammadjavad Sarieddeen, Hadi Shahabuddin, Shahriar Svensson, Tommy Tervo, Oskari Tölli, Antti Wu, Qingqing Xu, Wen Centre for Wireless Communications University of Oulu Finland CEA-Leti France KTH Royal Institute of Technology Sweden Linköping University Sweden IMEC Belgium University of Cassino and Southern Latium Italy Italy Department of Electrical and Electronics Engineering Kadir Has University Turkey Fivecomm Valencia Spain State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications China Algorithm Department ZTE Corporation China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney Australia Nokia Bell Labs Finland Institute for the Wireless Internet of Things Department of Electrical and Computer Engineering Northeastern University United States Ericsson Research Gothenburg Sweden Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Engineering Manchester Metropolitan University United Kingdom Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia Nokia Finland Department of Electrical Engineering Chalmers University of Technology Sweden State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau China Huawei Technologies Germany

This paper explores the road to vastly improving the broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, with peak data rates up to 1 Tbps. Several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be mainly used for pushing the peak rates of point-to-point links. Finally, at the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latency, higher reliability, and reduced complexity. © 2020, CC BY.

关键词： MIMO systems

Data augmentation for deep learning-based radio modulation classification

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arXiv 2019年

作者： Huang, Liang Pan, Weijian Zhang, You Qian, LiPing Gao, Nan Wu, Yuan College of Information Engineering Zhejiang University of Technology College of Computer Science and Technology Zhejiang University of Technology State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau

Deep learning has recently been applied to automatically classify the modulation categories of received radio signals without manual experience. However, training deep learning models requires massive volume of data. An insufficient training data will cause serious overfitting problem and degrade the classification accuracy. To cope with small dataset, data augmentation has been widely used in image processing to expand the dataset and improve the robustness of deep learning models. However, in wireless communication areas, the effect of different data augmentation methods on radio modulation classification has not been studied yet. In this paper, we evaluate different data augmentation methods via a state-of-the-art deep learning-based modulation classifier. Based on the characteristics of modulated signals, three augmentation methods are considered, i.e., rotation, flip, and Gaussian noise, which can be applied in both training phase and inference phase of the deep learning algorithm. Numerical results show that all three augmentation methods can improve the classification accuracy. Among which, the rotation augmentation method outperforms the flip method, both of which achieve higher classification accuracy than the Gaussian noise method. Given only 12.5% of training dataset, a joint rotation and flip augmentation policy can achieve even higher classification accuracy than the baseline with initial 100% training dataset without augmentation. Furthermore, with data augmentation, radio modulation categories can be successfully classified using shorter radio samples, leading to a simplified deep learning model and shorter the classification response time. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

A Parallel Retinex Image Enhancement Algorithm Based on OpenMP 16th

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A Parallel Retinex Image Enhancement Algorithm Based on Open...

16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019

作者： Cheng, Shixiong Liu, Bin He, Dongjian He, Jinrong Li, Yuancheng Du, Yanning College of Information Engineering Northwest A&F University YanglingShaanxi China Key Laboratory of Agricultural Internet of Things Northwest A&F University Ministry of Agriculture and Rural Affairs YanglingShaanxi712100 China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Northwest A&F University YanglingShaanxi712100 China College of Mechanical and Electronic Engineering Northwest A&F University YanglingShaanxi China College of Mathematics and Computer Science Yan’an University Yan’an Shaanxi China School of Computer Science and Technology Xi’an University of Science and Technology Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China

ISBN: (纸本)9783030307080

Retinex image enhancement algorithm occupies an important position in eliminating image uneven exposure, low contrast, and smog influence. However, with the increasing of image resolution, the real-time performance of the serial Retinex algorithm has not satisfied the requirements of practical applications. This paper proposes an OpenMP-based parallel Retinex algorithm. The parallelism of the Retinex algorithm is first identified by theoretical analyses. Then, the time-consuming sub-algorithms such as Gaussian convolution and exponential transformation, of the serial algorithm are designed and executed in parallel. On Tianhe-2 supercomputer platform, the experimental results show that the speedup of the parallel algorithm is significantly improved, and the test image set achieves an average speedup of 12. It indicates that the parallel algorithm can satisfy the needs of real-time processing in image enhancement field. © 2019, IFIP International Federation for Information Processing.

关键词： Image enhancement

Time-dependent performance analysis of the 802.11p-based platooning communications under disturbance

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arXiv 2020年

作者： Wu, Qiong Ge, Hongmei Fan, Pingyi Wang, Jiangzhou Fan, Qiang Li, Zhengquan School of Internet of Things Engineering Jiangnan University Wuxi214122 China State key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China School of Engineering and Digital Arts University of Kent CanterburyCT2 7NT United Kingdom Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Platooning is a critical technology to realize autonomous driving. Each vehicle in platoons adopts the IEEE 802.11p standard to exchange information through communications to maintain the string stability of platoons. However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i.e., aerodynamics drag and rolling resistance moment etc. Disturbances acting on one vehicle may inevitably affect the following vehicles and cause that the spacing error is propagated or even amplified downstream along the platoon, i.e., platoon string instability. In this case, the connectivity among vehicles is dynamic, resulting in the performance of 802.11p in terms of packet delay and packet delivery ratio being time-varying. The effect of the string instability would be further deteriorated once the time-varying performance of 802.11p cannot satisfy the basic communication requirement. Unlike the existing works which only analyze the steady performance of 802.11p in vehicular networks, we will focus on the impact of disturbance and construct models to analyze the time-dependent performance of 802.11p-based platooning communications. The effectiveness of the models is validated through simulation results. Moreover, the time-dependent performance of 802.11p Copyright © 2020, The Authors. All rights reserved.

关键词： Military applications