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arXiv 2023年

作者： Ge, Songyang Chang, Tsung-Hui The Shenzhen Research Institute of Big Data The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

Stochastic distributed optimization methods that solve an optimization problem over a multi-agent network have played an important role in a variety of large-scale signal processing and machine leaning applications. Among the existing methods, the gradient tracking (GT) method is found robust against the variance between agents’ local data distribution, in contrast to the distributed stochastic gradient descent (SGD) methods which have a slowed convergence speed when the agents have heterogeneous data distributions. However, the GT method can be communication expensive due to the need of a large number of iterations for convergence. In this paper, we intend to reduce the communication cost of the GT method by integrating it with the local SGD technique. Specifically, we propose a new local stochastic GT (LSGT) algorithm where, within each communication round, the agents perform multiple SGD updates locally. Theoretically, we build the convergence conditions of the _LSGT algorithm and show that it can have an improved convergence rate of O(1/√ET), where E is the number of local SGD updates and T is the number of communication rounds. We further extend the LSGT algorithm to solve a more complex learning problem which has linearly coupled variables inside the objective function. Experiment results demonstrate that the proposed algorithms have significantly improved convergence speed even under heterogeneous data distribution. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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A Distribution Optimization Framework for Confidence Bounds of Risk Measures

arXiv

引用

arXiv 2023年

作者： Liang, Hao Luo, Zhi-Quan School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China

We present a distribution optimization framework that significantly improves confidence bounds for various risk measures compared to previous methods. Our framework encompasses popular risk measures such as the entropic risk measure, conditional value at risk (CVaR), spectral risk measure, distortion risk measure, equivalent certainty, and rank-dependent expected utility, which are well established in risk-sensitive decision-making literature. To achieve this, we introduce two estimation schemes based on concentration bounds derived from the empirical distribution, specifically using either the Wasserstein distance or the supremum distance. Unlike traditional approaches that add or subtract a confidence radius from the empirical risk measures, our proposed schemes evaluate a specific transformation of the empirical distribution based on the distance. Consequently, our confidence bounds consistently yield tighter results compared to previous methods. We further verify the efficacy of the proposed framework by providing tighter problem-dependent regret bound for the CVaR bandit. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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research on recognition algorithm for gesture page turning based on wireless sensing

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intelligent and Converged Networks 2023年第1期4卷 15-27页

作者： Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China School of Electronic Information Engineering Gannan University of Science and TechnologyGanzhou 341000China Affiliated High School of Peking University Beijing 100086China Peking University&National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

关键词： Wi-Fi signal Channel State Information(CSI) wireless sensing human behavior recognition

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Gradient Tracking with Multiple Local SGD for Decentralized Non-Convex Learning

Gradient Tracking with Multiple Local SGD for Decentralized ...

引用

IEEE Conference on Decision and Control

作者： Songyang Ge Tsung-Hui Chang School of Science and Engineering The Chinese University of Hong Kong Shenzhen and Shenzhen Research Institute of Big Data Shenzhen China

The stochastic Gradient Tracking (GT) method for distributed optimization, is known to be robust against the inter-client variance caused by data heterogeneity. However, the stochastic GT method can be communication-intensive, requiring a large number of communication rounds of message exchange for convergence. To address this challenge, this paper proposes a new communication-efficient stochastic GT algorithm called the Local Stochastic GT(LSGT) algorithm, which adopts the local stochastic gradient descent (local SGD) technique in the GT method. With LSGT, each agent can perform multiple SGD updates locally within each communication round. Although it is not known previously whether the stochastic GT method can benefit from the local SGD, we establish the conditions under which our proposed LSGT algorithm enjoys the linear speedup brought by local SGD. Compared with the existing work, our analysis requires less restrictive conditions on the mixing matrix and algorithm stepsize. Moreover, it reveals that the local SGD does not only reserve the resilience of the stochastic GT method against the data heterogeneity but also speeds up reducing the tracking error reduction in the optimization process. The experimental results demonstrate that the proposed LSGT exhibits improved convergence speed and robust performance in various heterogeneous environments.

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Max-and-Min Fractional Programming for Communications and Sensing

Max-and-Min Fractional Programming for Communications and Se...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Yannan Chen Licheng Zhao Kaiming Shen School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China Shenzhen Research Institute of Big Data China

There is considerable interest in the use of fractional programming (FP) for the communication system design because many problems in this area are fractionally structured. Notably, max-FP and min-FP are not interchangeable in general if there are multiple ratios, so the two types of FP are often dealt with separately in the existing literature. As a result, an FP method for maximizing the signal-to-interference-plus-noise ratios (SINRs) typically cannot be used for minimizing the Cramér-Rao bounds (CRBs). In contrast, this work proposes a unified approach that bridges the gap between max-FP and min-FP. Particularly, we examine the theoretical basis of this unified approach from a minorization-maximization (MM) perspective, and in return obtain a matrix extension of this new FP technique. Moreover, this work presents two application cases: (i) joint radar sensing and (ii) multi-cell secure transmission, neither of which can be efficiently addressed by the existing FP tools.

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A Novel Multi-Edge Cooperative Offloading Genetic Algorithm towards Environmental Monitoring System 5

A Novel Multi-Edge Cooperative Offloading Genetic Algorithm ...

引用

5th International Conference on Telecommunications and Communication engineering, ICTCE 2022

作者： Fang, Jie Li, Wenzao Wang, Yue Sun, Xiulan College of Communication Engineering Chengdu University of Information Technology China Network and Data Security Key Lab of Sichuan Pro. University of Electronic Science and Technology of China China Institute of Data Application Research Educational Informalization and Big Data Center China

ISBN: (纸本)9781450397797

In environmental monitor architecture, the usual way of processing data is centralized. On the one hand, this leads to a long time of data processing, and with the increasing number of monitoring sensors, the pressure of data processing in data centers increases sharply. The edge computing provides a feasible solution to relieve the processing pressure and decrease the delay of data processing in data center. However, in the environmental monitor system, the edge servers are often load unbalance because of the nonuniform distribution of sensors. While, it is usually ignored in recently researches. In this paper, we proposed a cooperative offloading strategy (MECOGA) based on genetic algorithm (GA). It aims to ease the data processing pressure and reduce the data processing time in the data center. In addition, the load balance of edge servers is also one of our optimization goals. In this paper, we aim at decreasing task processing delay and improving the system load balancing. Contrast to other algorithms, our MECOGA decreases the delay near 20% before collaborative loading. And the balance variance is 15% smaller than the Balance strategy. © 2022 Association for Computing Machinery.

关键词： Genetic algorithms

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Inverse Quadratic Transform for Minimizing A Sum of Ratios

Inverse Quadratic Transform for Minimizing A Sum of Ratios

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yannan Chen Licheng Zhao Yaowen Zhang Kaiming Shen School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China Shenzhen Research Institute of Big Data China

A major challenge with the multi-ratio Fractional Program (FP) is that the existing methods for the maximization problem typically do not work for the minimization case. We propose a novel technique called inverse quadratic transform for the sum-of-ratios minimization problem. Its main idea is to reformulate the min-FP problem in a form amenable to efficient iterative optimization. Furthermore, this transform can be readily extended to a general cost-function-of-multiple-ratios minimization problem. We also give a Majorization-Minimization (MM) interpretation of the inverse quadratic transform, showing that all those desirable properties of MM can be carried over to the new technique. Moreover, we demonstrate the application of inverse quadratic transform in minimizing the Age-of-Information (AoI) of data networks.

关键词： Transforms Signal processing Minimization Information age Cost function Acoustics Iterative methods

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Embracing Uncertainty: A Diffusion Generative Model of Spectrum Efficiency in 5G Networks 15

Embracing Uncertainty: A Diffusion Generative Model of Spect...

引用

15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Wang, Hao Tang, Zhiwei Zhang, Shutao Shen, Chao Chang, Tsung-Hui Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China CUHKSZ-SRIBD-Huawei Joint Research Laboratory for Optimization of Future Network Systems Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9798350324662

Spectrum efficiency modeling is a crucial enabling technology for optimizing 5G networks. Neural networks have been employed to model the complex and nonlinear relationship between spectrum efficiency and other 5G network features, including reference signal received power (RSRP), modulation and coding scheme (MCS), and rank indicator (RI). However, the current neural network model can only produce deterministic predictions, disregarding the stochastic nature of wireless networks, which may lead to suboptimal network performance. To address this limitation and embrace the uncertainty, we propose a diffusion generative model that can learn the conditional distribution of spectrum efficiency given the information of RSRP, MCS, and RI, providing a more accurate representation of the inherent variability in wireless networks. Additionally, we introduce a novel neural network architecture that incorporates a U-Net structure with Multi-Layer Perceptron (MLP) as building blocks, facilitating efficient learning of the denoising function within the diffusion generative model. Simulation results demonstrate that the proposed diffusion generative model accurately learns the statistical distribution of spectrum efficiency. Moreover, the proposed diffusion generative model yields improved fitting performance based on real-world measurements and enables quantification of uncertainty in spectrum efficiency modeling, which is essential for better network optimization. © 2023 IEEE.

关键词： diffusion spectrum efficiency U-Net

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Finding the Missing data: A BERT-inspired Approach Against Package Loss in Wireless Sensing

arXiv

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arXiv 2024年

作者： Zhao, Zijian Chen, Tingwei Meng, Fanyi Li, Hang Li, Xiaoyang Zhu, Guangxu Shenzhen Research Institute of Big Data China School of Computer Science and Engineering Sun Yat-sen University China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier at a time, CSI-BERT captures the sequential relationships across different subcarriers. Experimental results demonstrate that CSIBERT achieves lower error rates and faster speed compared to traditional interpolation methods, even when facing with high loss rates. Moreover, by harnessing the recovered CSI obtained from CSI-BERT, other deep learning models like Residual Network and Recurrent Neural Network can achieve an average increase in accuracy of approximately 15% in Wi-Fi sensing tasks. The collected dataset WiGesture and code for our model are publicly available at https://***/RS2002/CSI-BERT. Copyright © 2024, The Authors. All rights reserved.

关键词： Wi-Fi

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