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Sensor Array and Multichannel Signal Processing Workshop

作者： Xianxiang Yu Guolong Cui Zhenghong Zhang Lin Zhou Jing Yang Lingjiang Kong School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Electronic Information Control Laboratory Chengdu China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper considers a quadratic optimization problem in radar discrete-phase waveform design under similarity and constant modulus constraints. A computationally efficient iterative algorithm based on the Alternating Direction Penalty Method (ADPM) framework is proposed. In each iteration, it converts the considered problem into two subproblems with closed-form solutions via an introduced auxiliary variable, while locally increasing the penalty factor involved in the ADPM framework. The proposed algorithm is ensured to converge for any initialization under some mild conditions and avoids the non-convergence problem of the Alternating Direction Method of Multipliers (ADMM) when handling the NP-hard problems. Finally, numerical simulations demonstrate that the proposed algorithm can outperform their counterparts by providing better objective values.

关键词： Phased arrays NP-hard problem Signal processing algorithms Radar Numerical simulation Convex functions Iterative methods

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Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks

arXiv

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arXiv 2022年

作者： Shu, Feng Chen, Yiwen Zhan, Xichao Cai, Wenlong Huang, Mengxing Jie, Qijuan Li, Yifang Shi, Baihua Wang, Jiangzhou You, Xiaohu Zhao, Xinyi The School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing210094 China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China The School of Information and Communication Engineering Hainan University Haikou570228 China The National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing100854 China The School of Engineering University of Kent CanterburyCT27NT United Kingdom The School of Southeast University Nanjing211189 China

Due to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a valuable green communication technology for future wireless networks. Combining a massive HAD-MIMO with direction of arrival (DOA) will provide a high-precision even ultra-high-precision DOA measurement performance approaching the fully-digital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we review three aspects: detection, estimation, and Cramer-Rao lower bound (CRLB) with low-resolution ADCs at receiver. First, a multi-layer-neural-network (MLNN) detector is proposed to infer the existence of passive emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to eliminate phase ambiguity using only one-snapshot. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB using single snapshot. Copyright © 2022, The Authors. All rights reserved.

关键词： Multilayer neural networks

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A Novel Threat Sensing Architecture for Heterogeneous Cognitive Sensor Networks

A Novel Threat Sensing Architecture for Heterogeneous Cognit...

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2019 IEEE International Conference on Signal, information and Data Processing, ICSIDP 2019

作者： Ren, Yongji Zhang, Yun Zhou, Zheng Xu, Xiaofeng Liu, Xiaolei Naval Aviation University Yantai China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Yantai Vocational College Yantai China

ISBN: (纸本)9781728123455

With the rapid development and application of novel network information technologies, the various radar and/or communication transmitters have become more intelligent and networked in modern electromagnetic operational environment, which causes the threat sensing architecture of traditional electronic warfare system more inadequate, especially encountering unknown threat signals or networked targets. In this case, the threat sensing becomes one of the biggest challenges that the traditional electronic warfare system is facing. Therefore, this paper proposed a novel heterogeneous architecture for cognitive sensor networks to facilitate efficient threat sensing. To deal with the heterogeneous problem of cognitive sensor networks, multiple constraints satisfaction based approach has been employed. And the game based resources scheduling scheme has been utilized to facilitate the efficiency. To the best of our knowledge, our work is the first one that introduces the heterogeneous cognitive architecture into threat sensing domain of electronic warfare system. The simulation results show the proposed novel threat sensing architecture for heterogeneous cognitive sensor networks is feasible and effective. © 2019 IEEE.

关键词： Sensor networks

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Mainlobe Jamming Suppression Via Independent Component Analysis for Polarimetric SIMO Radar

Mainlobe Jamming Suppression Via Independent Component Analy...

引用

Sensor Array and Multichannel Signal Processing Workshop

作者： Mengmeng Ge Guolong Cui Zhenghong Zhang Lin Zhou Xianxiang Yu Feng Yang Lingjiang Kong School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Electronic Information Control Laboratory Chengdu China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

The presence of mainlobe jamming will significantly reduce the radar detection capabilities. Conventional independent component analysis (ICA)-based methods will suffer from the ineffectiveness when the angle of the target is same as that of the jammer. In this paper, exploring polarization characteristics, we propose an approach based on ICA for polarimetric SIMO (P-SIMO) radar to resist mainlobe jamming. Specifically, the signal model of P-SIMO radar accounting for the target and jamming signals is derived. Then, the approach based on ICA is utilized to separate the target component and jamming component while achieving the mainlobe jamming suppression. Finally, the effectiveness and capacities of proposed method are demonstrated by simulations, and the results show that the proposed method outperforms conventional ICA-based method.

关键词： Radar detection Radar Independent component analysis Resists Numerical simulation Radar polarimetry Radar signal processing

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Top-down and bottom-up approaches to video Quality of Experience studies;overview and proposal of a new model

arXiv

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arXiv 2023年

作者： Koniuch, Kamil Baraković, Sabina Husić, Jasmina Baraković De Moor, Katrien Janowski, Lucjan Wierzchoń, Michal Institute of Communication Technologies AGH University of Science and Technology Cracow Poland Faculty of Traffic and Communications University of Sarajevo Sarajevo Bosnia and Herzegovina Zmaja od Bosne bb Sarajevo71000 Bosnia and Herzegovina Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina Zmaja od Bosne bb Sarajevo71000 Bosnia and Herzegovina Department of Information Security and Communication Technology Norwegian University of Science and Technology Trondheim Norway Consciousness Lab Institute of Psychology Jagiellonian University Kraków Poland Centre for Brain Research Jagiellonian University Kraków Poland Jagiellonian Human-Centered Artificial Intelligence Laboratory Jagiellonian University Kraków Poland

Modern video streaming services require quality assurance of the presented audiovisual material. Quality assurance mechanisms allow streaming platforms to provide quality levels that are considered sufficient to yield user satisfaction, with the least possible amount of data transferred. A variety of measures and approaches have been developed to control video quality, e.g., by adapting it to network conditions. These include objective matrices of the quality and thresholds identified by means of subjective perceptual judgments. The former group of matrices has recently gained the attention of (multi)media researchers. They call this area of study "Quality of Experience" (QoE). In this paper, we present a review of QoE’s theoretical models together with a discussion of their properties and implications for the field. We argue that most of them represent the bottom-up approach to modeling. Such models focus on describing as many variables as possible, but with a limited ability to investigate the causal relationship between them;therefore, the applicability of the findings in practice is limited. To advance the field, we therefore propose a structural, top-down model of video QoE that describes causal relationships among variables. We hope that our framework will facilitate designing comparable experiments in the domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Quality assurance

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A security enhanced certificateless aggregate signcryption scheme for VANETs

引用

Peer-to-Peer Networking and Applications 2025年第4期18卷

作者： Liu, Dong Tang, Wentao Wang, Liangliang Liu, Yang Liu, Zhiquan Zhang, Kai Li, Weiwei School of Computer Science and Technology Xidian University Xi’an710699 China the College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China Institute of Network Security Inspection and Evaluation China Industrial Control System Cyber Emergency Response Team Beijing100040 China Key Laboratory of Industrial Information Security Perception and Evaluation Technology Ministry of Industry and Information Technology Beijing100040 China the College of Cyber Security Jinan University Guangzhou510632 China

Unlike traditional Internet of Things (IoT), Vehicular Ad Hoc Networks (VANETs) operate in open environments and transmit information through wireless communication technologies, making VANETs more susceptible to various attacks. To protect the security of drivers’ private information and improve the efficiency of the transportation system, various Certificateless Signcryption (CLSC) schemes have been proposed. Recently, Shao et al. designed a provably secure lightweight authentication scheme, which satisfied aggregate signcryption and batch unsigncryption. Unfortunately, we uncovered there are some vulnerabilities through security analysis in their scheme. In this paper, we first give an analysis method based on their security model with public key replacement to demonstrate the weakness. Second, we introduce a security enhanced CLSC scheme with a small index, demonstrating that the improved scheme is existential unforgeability under adaptive chosen message attacks (EUF-CMA), indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2), and our scheme can resist extra Type-III adversaries which can combine the invalid signcryptions to generate a valid signcryption by formal security proof and heuristic security analysis. Finally, we compare our scheme with recent CLSC schemes in security performance, computation overhead, and communication overhead. The computational overhead of our scheme is below 41 ms under computing resource constraints while still meeting extremely high levels of security. The results show that our scheme is more suitable for VANETs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Ad hoc networks

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Byzantine-Robust Federated Learning Framework via a Server-Client Defense Mechanisms

引用

IEEE Internet of Things Journal 2025年

作者： Song, Anxiao Zhang, Tao Cheng, Ke Cao, Yang Zhu, Xinghui Shen, Yulong Xidian University School of Computer Science and Technology Xi’an China Shaanxi Key Laboratory of Network and System Security Shaanxi Xi’an710071 China Xidian University School of Computer Science and Technology Shaanxi Xi’an710071 China Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi’an710121 China Institute of Science Tokyo Department of Computer Science Tokyo Japan

Federated Learning (FL), a distributed machine learning (ML) framework, is susceptible to Byzantine attacks since the attacker can manipulate clients' local data or models to compromise the performance of the global model. There has been a wealth of defenses developed to mitigate the attacks by limiting the impact of malicious models. Nevertheless, the attacker can easily circumvent these approaches that rely solely on a single server-side defense, stemming from the high dimensionality of models and the variety of Byzantine attacks. Therefore, we propose Basalt, a Byzantine-robust federated learning framework with a server-client joint defense mechanism that enables multiple clients to train a global ML model under Byzantine attacks. On the client side, we design an efficient self-defense approach with model-level penalty loss that restricts local-benign divergence and decreases local-malicious correlation to prevent misclassification. On the server side, we present an efficient defense strategy based on the manifold and maximum clique, further strengthening the FL's resilience against Byzantine attacks. We provide theoretical guarantees for global model convergence in FL with Byzantine attacks. Our extensive experiments demonstrate that Basalt outperforms existing state-of-the-art works. Especially, it achieves nearly 100% accuracy for detecting malicious clients in nonindependent and nonidentically distributed (Non-IID) MNIST datasets under various Byzantine attacks. © 2014 IEEE.

关键词： Federated learning

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Classification and Recognition of Underwater Target Based on MFCC Feature Extraction

Classification and Recognition of Underwater Target Based on...

引用

IEEE International Conference on Signal Processing, communications and Computing (ICSPCC)

作者： Yuze Tong Xin Zhang Yizhou Ge School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Science and Technology on Communication Information Security Control Laboratory NO.36 Research Institute of CETC Jiaxing China

ISBN: (数字)9781728172026

ISBN: (纸本)9781728172033

The key to underwater target recognition is to extract the effective features of underwater target radiation noise. This paper presents an effective method for underwater target recognition and classification by extracting Mel-Frequency Cepstral Coefficients (MFCCs) features of underwater target radiation noise. Compared with traditional spectral analysis methods, MFCC makes full use of the non-linear auditory effect of the human ear with different perception capabilities for sounds of different frequencies. In this paper, the classification experiment of the radiated noise of the three types of measured underwater targets is done, where the MFCC feature vectors of the three types of targets are extracted, and the K-Nearest Neighbor (K-NN) algorithm is used to classify and identify them. Finally, the experimental results show that the method is effective.

关键词： Feature extraction Mel frequency cepstral coefficient Target recognition Classification algorithms Filter banks Ear Auditory system

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Unified Authentication Scheme for IoT Blockchain Based on PUF

Unified Authentication Scheme for IoT Blockchain Based on PU...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Dawei Li Yingxian Song Lixin Zhang Di Liu Baoquan Ma Zhenyu Guan School of Cyber Science and Technology Beihang University Beijing China National Engineering Laboratory for Industry Control System Information Security Technology Beijing China National Computer System Enginering Research Institute of China Beijing China

The number of global Internet of Things devices has increased rapidly and has a wide range of application markets. Access authentication methods for massive heterogeneous IoT devices are complicated, and central servers and trusted platforms cannot take effective measures against tampered devices. Analyzing the problems and challenges faced by IoT devices, this paper proposes a unified authentication scheme for IoT blockchain devices based on PUF. The PUF model is used to authenticate IoT devices, and the model parameters are decomposed into various parts and stored in each node in the blockchain. The homomorphic hash function is used to aggregate the partial authentication response values generated by the distribution in the invisible case to complete the authentication of the device information. It solves the complex and diverse problems of device authentication schemes and realizes the credibility and security of devices data sources and devices information storage stability. Finally, the functional analysis confirmed that the scheme is practical and executable.

关键词： Hash functions Soft sensors Authentication Stability analysis Functional analysis Blockchains security

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