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Make Power Allocation More Adaptive in Ultra Dense networks: Priority-Driven Deep Reinforcement Learning via Noise-Perturbations

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IEEE Transactions on Wireless Communications 2025年第5期24卷 4010-4023页

作者： Sun, Xiaochuan Han, Jinpeng Li, Yingqi Zhu, Kaiyu Zhang, Haijun Hebei Key Laboratory of Industrial Intelligent Perception Tangshan063210 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China

Efficient and stable resource management in ultra-dense networks is essential for interference reduction and quality of service guarantee for large-scale users. Although reinforcement learning is currently the most talked-about method for achieving efficient resource allocation, it still faces significant challenges when dealing with the key adaptive issues, such as strong interference, large-scale users dynamic demands, and differentiated user priority requirements. In the case, this paper proposes a pervasive power allocation method in the framework of deep reinforcement learning. Concretely, we develop a noisy deep Q-network for guaranteeing stable exploration of average rate and accelerating the model convergence, where the parameterized noise is incorporated into the connection weights of neural networks. On the other hand, considering the varying priority levels of different users, we devise a prioritized experience replay mechanism aimed at enhancing the service quality for users with high importance. Simulation results demonstrate that our proposal can surpass the state-of-the-art methods in terms of average rate, convergence, stability, and model complexity, achieving more adaptive power allocation. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Enhancing Security and Privacy in Federated Learning Using Low-Dimensional Update Representation and Proximity-Based Defense

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3372-3385页

作者： Li, Wenjie Fan, Kai Zhang, Jingyuan Li, Hui Lim, Wei Yang Bryan Yang, Qiang Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi'an710071 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduce a novel framework named Federated Learning with Low-Dimensional Update Representation and Proximity-Based defense (FLURP), designed to address privacy preservation and resistance to Byzantine attacks in distributed learning environments. FLURP employs $\mathsf {LinfSample}$LinfSample method, enabling clients to compute the $l{ }$l∞ norm across sliding windows of updates, resulting in a Low-Dimensional Update Representation (LUR). Calculating the shared distance matrix among LURs, rather than updates, significantly reduces the overhead of Secure Multi-Party Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and poisoned updates. Additionally, FLURP integrates a privacy-preserving proximity-based defense mechanism utilizing optimized SMPC protocols to minimize communication rounds. Our experiments demonstrate FLURP's effectiveness in countering Byzantine adversaries with low communication and runtime overhead. FLURP offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security. © 1989-2012 IEEE.

关键词： Federated learning

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Generative Adversarial Network Enhanced DRL for ISAC With Double Active RISs

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IEEE Internet of Things Journal 2025年第10期12卷 13487-13499页

作者： Zhang, Jifa Sheng, Min Xing, Chengwen Liu, Junyu Zhao, Nan Karagiannidis, George K. Dalian University of Technology School of Information and Communication Engineering Liaoning Dalian China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

Integrated sensing and communication (ISAC) is a promising paradigm to alleviate spectrum congestion and facilitate a variety of emerging internet of things (IoT) applications. However, the direct links from the ISAC base station (BS) to the users may be blocked due to the obstacles. In this paper, we investigate the double-active reconfigurable intelligent surfaces (RISs) assisted ISAC, where two active RISs are used to establish virtual line-of-sight (LoS) links from the ISAC BS to the users. In addition, the sum of the minimum sensing signal-to-interference-plus-noise ratios (SINRs) among multiple targets during a series of time slots is maximized, subject to quality of service (QoS) and transmit power constraints, through the joint optimization of transmit, reflection and receive beamforming. We first transform this non-convex optimization problem in the dynamic environment into a Markov decision process, and then propose a double delayed deep deterministic policy gradient (TD3)-based algorithm to solve it. Moreover, to enhance the generalization and stability, we integrate the generative adversarial network (GAN) into the TD3 algorithm and propose a GAN-TD3-based algorithm to handle the beamforming optimization problem. Compared with the TD3-based algorithm, the proposed GAN-TD3-based algorithm achieves the better performance and higher stability at the cost of higher computational complexity and slower convergence speed. Simulation results are presented to verify the effectiveness of our proposed algorithms and the superiority of the active RIS over the passive counterpart. © 2014 IEEE.

关键词： Generative adversarial networks

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Lip-Audio Modality Fusion for Deep Forgery Video Detection

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computers, Materials & Continua 2025年第2期82卷 3499-3515页

作者： Yong Liu Zhiyu Wang Shouling Ji Daofu Gong Lanxin Cheng Ruosi Cheng College of Cyberspace Security Information Engineering UniversityZhengzhou450001China Research Institute of Intelligent Networks Zhejiang LabHangzhou311121China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China Key Laboratory of Cyberspace Security Ministry of EducationZhengzhou450001China

In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.

关键词： Deep forgery video detection lip-audio modality fusion mel frequency cepstrum coefficient siamese neural network band-pass filter

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HG2former: HSV-Gamma Guided Transformers for Efficient Underwater Image Enhancement

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IEEE Journal of Oceanic engineering 2025年第2期50卷 866-878页

作者： Qing, Yuhao Shen, Liquan Fang, Zhijun Wang, Yueying Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai200444 China Donghua University School of Computer Science and Technology Shanghai200051 China

Due to optical phenomena, such as the absorption and scattering of light in underwater environments, underwater images often suffer from degradation in color, contrast, clarity, and noise. Existing deep learning-based methods for underwater image enhancement typically learn a direct mapping from low-quality to high-quality underwater images, without fully considering the mapping of local luminance, chrominance, and contrast features. In this article, we propose a transformer model guided hue, saturation, value (HSV) and gamma correction for underwater image enhancement. The HG2former combines the HSV color model and gamma correction techniques to isolate the three fundamental characteristics of color, providing rich, differentiated enhancement for both color and contrast in underwater images. In addition, nonlinear gamma correction adaptively adjusts the brightness and contrast of images, addressing issues of visibility reduction and color distortion in underwater imaging. Furthermore, we introduce a meticulously designed encoder–decoder structure, along with an improved multihead self-attention module, to capture the spatial distribution patterns of underwater images while modeling both local and long-range dependencies. Extensive experimental results on multiple data sets demonstrate that the proposed HG2former outperforms other state-of-the-art methods. © 1976-2012 IEEE.

关键词： Color

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Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading Channels

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IEEE Wireless Communications Letters 2025年第5期14卷 1411-1415页

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Kaklamani, Dimitra I. Venieris, Iakovos S. University of Hertfordshire Communications and Intelligent Systems Research Group HatfieldAL10 9AB United Kingdom Microwave and Fiber Optics Laboratory Greece National Technical University of Athens Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering Zografou Athens15780 Greece

The recent combination of the rising architectures, known as stacked intelligent metasurface (SIM) and holographic multiple-input multiple-output (HMIMO), drives toward breakthroughs for next-generation wireless communication systems. Given the fact that the number of elements per surface of the SIM is much larger than the base station (BS) antennas, the acquisition of the channel state information (CSI) in SIM-aided multi-user systems is challenging, especially when a line-of-sight (LoS) component is present. Thus, in this letter, we address the channel procedure under conditions of Rician fading by proposing a protocol in terms of a minimum mean square error (MMSE) estimator for wave-based design in a single phase. Moreover, we derive the normalized mean square error (NMSE) of the suggested estimator, and provide the optimal phase shifts minimising the NMSE. Numerical results illustrate the performance of the new channel estimation protocol. © 2012 IEEE.

关键词： Mean square error

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Throughput Improvement for RIS-Empowered Wireless Powered Anti-Jamming Communication networks (WPAJCN)

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4622-4637页

作者： Chu, Zheng Chieng, David Kwong, Chiew Foong Jin, Huan Zhu, Zhengyu Huang, Chongwen Yuen, Chau University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Ningbo China Next Generation Internet of Everything Laboratory Ningbo315100 China University of Nottingham Ningbo China School of Computer Science Ningbo315100 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

In this paper, we propose a reconfigurable intelligent surface (RIS)-aided wireless powered anti-jamming communication network (WPAJCN), where the RIS is utilized to participate in downlink wireless power transfer (WPT), as well as uplink anti-jamming wireless information transfer (AJ-WIT). To evaluate the network anti-jamming performance, we maximize a sum anti-jamming throughput, with the constraints of downlink WPT and uplink AJ-WIT time scheduling, and unit-modulus RIS phase shifts. The formulated problem is not convex in terms of these two types of coupled variables, which cannot be directly solved. To address this problem, the Lagrange dual method and Karush-Kuhn-Tucker conditions are presented to transform its sum-of-logarithmic objective function into the logarithmically fractional counterpart, which reformulate the original problem into that with respect to RIS phase shift vectors and WPT time scheduling. Next, we propose to apply the Dinkelback algorithm to solve a non-linear fractional programming with respect to the downlink WPT and uplink AJ-WIT RIS phase shifts in an alternating fashion, each of which is derived into a semi-closed solution by utilizing the Riemannian Manifold Optimization (RMO). In addition, the optimal WPT time scheduling is obtained by numerical search. Finally, the numerical results are demonstrated to confirm the improved performance of the proposed approach compared to the benchmark counterparts, which highlights the that RIS can effectively enhance the uplink anti-jamming WIT capability as well as the downlink WPT efficiency. © 2005-2012 IEEE.

关键词： Lagrange multipliers

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Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14953-14966页

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong Xi’an Jiaotong University School of Automation Science and Engineering Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi’an710049 China University of Pittsburgh Department of Industrial Engineering Department of Electrical and Computer Engineering PittsburghPA15106 United States Tsinghua University Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Xi’an Jiaotong University School of Electrical Engineering Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitude. © 2004-2012 IEEE.

关键词： Stochastic programming

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Decentralized Authentication Scheme Incorporating Reputation and Attribute Signature for Cross-Web3 Applications

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IEEE Internet of Things Journal 2025年第10期12卷 14526-14537页

作者： Song, Xiaoling Xu, Guangxia Huang, Yongfei Liu, Yong Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China

Driven by Web 3.0, the number of decentralized applications (DAPPs) continues to increase, and the need for decentralized authentication across applications becomes more and more significant. In addition, multi-dimensional reputation assessment becomes especially critical in a decentralized environment. However, there is still a lack of trusted and flexible self-sovereign identity (SSI) authentication mechanisms in Web 3.0 decentralized zero-trust cross-applications access scenarios. In response to these challenges, we propose a decentralized authentication scheme that fuses reputation assessment and attribute signature for the open and self-organized Web3 application. Firstly, we design an attribute signature method based on the attributes in a user's verifiable credentials (VCs). The signature can be used for authentication across Web3 applications, and the authenticator can flexibly adjust the attributes to be verified and their number according to specific needs. Meanwhile, in the attribute signature, we construct a multi-dimensional reputation assessment mechanism to comprehensively assess the user's reputation from multiple aspects to determine his/her security risk level, so as to reduce the possibility of identity fraud. Ultimately, through comparative analysis, we verify the advantages of the scheme in terms of privacy, scalability and attack resistance. Moreover, we analyze the performance of the key algorithms in the scheme through simulation experiments, and the experimental results show that our scheme is feasible. © 2014 IEEE.

关键词： Authentication Protocol

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