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arXiv 2025年

作者： Zhang, Shuhao Cheng, Bo Han, Jiale Chen, Yuli Wu, Zhixuan Li, Changbao Gu, Pingli State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Hong Kong University of Science and Technology Hong Kong BigData R&D Center North China Institute of Computing Technology China

Text watermarking provides an effective solution for identifying synthetic text generated by large language models. However, existing techniques often focus on satisfying specific criteria while ignoring other key aspects, lacking a unified evaluation. To fill this gap, we propose the Comprehensive Evaluation Framework for Watermark (CEFW), a unified framework that comprehensively evaluates watermarking methods across five key dimensions: ease of detection, fidelity of text quality, minimal embedding cost, robustness to adversarial attacks, and imperceptibility to prevent imitation or forgery. By assessing watermarks according to all these key criteria, CEFW offers a thorough evaluation of their practicality and effectiveness. Moreover, we introduce a simple and effective watermarking method called Balanced Watermark (BW), which guarantees robustness and imperceptibility through balancing the way watermark information is added. Extensive experiments show that BW outperforms existing methods in overall performance across all evaluation dimensions. We release our code to the community for future research1 Copyright © 2025, The Authors. All rights reserved.

关键词： Watermarking

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Personalized Multi-Modal Federated Learning Over Heterogeneous Edge Devices

Personalized Multi-Modal Federated Learning Over Heterogeneo...

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IEEE Conference on Wireless Communications and networking

作者： Yuxuan Hu Yixuan Li Xueting Han Liang Xin Xiaoqi Qin Nan Ma State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China College of Electronic Information Engineering Taiyuan University of Technology Taiyuan China China Unicom Smart Connection Technology Limited Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Multi-modal learning improves model robustness and accuracy by integrating complementary information from multiple modalities, addressing the limitations of uni-modal approaches in handling complex tasks. To address privacy and communication constraints, federated learning (FL) has been adopted for distributed multi-modal model training, where only model parameters instead of raw data are uploaded. Traditional multi-modal FL methods face performance degradation due to the intricate interplay of three types of modality heterogeneity: modality quantity, modality feature emphasis, and modality statistical distribution. In this paper, we propose HeteroPMMFL, a novel personalized multi-modal FL framework. HeteroPMMFL proposes a model decoupling approach to effectively address modality combination differences and designs personalized collaboration graphs to strengthen cooperation among similar clients. This framework is applicable to any multi-modal dataset and can be easily extended to accommodate various modality combinations. Experimental results show that HeteroPMMFL outperforms both traditional FedAvg and the state-of-the-art Harmony framework in model accuracy, under the presence of three types of modality heterogeneity.

关键词： Training Adaptation models Accuracy Federated learning Collaboration Statistical distributions Focusing Data models Robustness Faces

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OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest

arXiv

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arXiv 2025年

作者： Jing, Yuhan Wang, Jingyu Zhang, Lei Sun, Haifeng He, Bo Zhuang, Zirui Wang, Chengsen Qi, Qi Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China China United Network Communications Co. Ltd. Beijing100033 China

With the growing adoption of time-series anomaly detection (TAD) technology, numerous studies have employed deep learning-based detectors for analyzing time-series data in the fields of Internet services, industrial systems, and sensors. The selection and optimization of anomaly detectors strongly rely on the availability of an effective performance evaluation method for TAD. Since anomalies in time-series data often manifest as a sequence of points, conventional metrics that solely consider the detection of individual point are inadequate. Existing evaluation methods for TAD typically employ point-based or event-based metrics to capture the temporal context. However, point-based metrics tend to overestimate detectors that excel only in detecting long anomalies, while event-based metrics are susceptible to being misled by fragmented detection results. To address these limitations, we propose OIPR1, a novel set of TAD evaluation metrics. It models the process of operators receiving detector alarms and handling faults, utilizing area under the operator interest curve to evaluate the performance of TAD algorithms. Furthermore, we build a special scenario dataset to compare the characteristics of different evaluation methods. Through experiments conducted on the special scenario dataset and five real-world datasets, we demonstrate the remarkable performance of OIPR in extreme and complex scenarios. It achieves a balance between point and event perspectives, overcoming their primary limitations and offering applicability to broader situations. © 2025, CC BY-NC-ND.

关键词： Time series

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Data-Driven Digital Adaptive Timing Mismatch Calibration for Sub-Nyquist Wideband Sampling

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Ma, Yuan Zheng, Yiling Zhang, Xingjian Liao, Bin Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Pengcheng Laboratory Shenzhen518055 China Shenzhen University College of Electronic and Information Engineering Shenzhen518060 China Harbin Institute of Technology Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen518060 China Fudan University Institute of Space Internet Shanghai China

The employment of sub-Nyquist sampling enables the adaptation to the ever-increasing bandwidth demands of wireless communication systems. Multicoset sampling is a nonuniform sub-Nyquist sampling technique that can be realized by using a multi-channel architecture. Nevertheless, the performance of multicoset sampling is significantly affected by timing mismatches among sub-ADCs. Traditional analog calibration scheme mitigates timing mismatch errors by adjusting the sampling clock in the front-end, but its performance is suboptimal due to the difficulty in selecting an appropriate initial step size and the sequential calibration process. In this paper, a data-driven digital adaptive timing mismatch calibration approach is proposed that uses real-time measurement data only to calculate calibration parameters without changing hardware circuits, making it effective for tracking variations in mismatches among multiple channels. Based on the nonlinear relationship between the timing mismatches and the normalized mean squared error of the output spectrum, the proposed data-driven calibration scheme jointly computes calibration parameters among multiple channels in parallel through a series of dynamic linearization data models with a time-varying pseudo-partial derivative parameter estimation procedure, and then adaptively adjusts the sampling matrix in the reconstruction to correct unwanted distortions in the output spectrum. To further improve the calibration performance, particle swarm optimization algorithm is exploited to generate parameter vectors used in the proposed data-driven calibration scheme. Numerical analyses indicate that the proposed data-driven digital calibration scheme can eliminate timing mismatches with fewer calibration iterations and enhanced accuracy. © 2015 IEEE.

关键词： Particle swarm optimization (PSO)

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Efficient Gaussian Process Classification-Based Physical-Layer Authentication With Configurable Fingerprints for 6G-Enabled IoT

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IEEE Transactions on Consumer Electronics 2025年

作者： Meng, Rui Zhu, Fangzhou Cheng, Xiqi Xu, Xiaodong Wang, Bizhu Dong, Chen Xu, Bingxuan Tao, Xiaofeng Zhang, Ping Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Guangdong Shenzhen518066 China

The future 6G-enabled IoT will facilitate seamless global connectivity among ubiquitous wireless devices, but this advancement also introduces heightened security risks such as spoofing attacks. Physical-Layer Authentication (PLA) has emerged as a promising, inherently secure, and energy-efficient technique for authenticating IoT terminals. Nonetheless, the direct application of state-of-the-art PLA schemes to 6G-enabled IoT encounters two major hurdles: inaccurate channel fingerprints and the inefficient utilization of prior fingerprint information. To tackle these challenges, we leverage Reconfigurable Intelligent Surfaces (RISs) to enhance fingerprint accuracy. Additionally, we integrate active learning and Gaussian Processes (GPs) to propose an Efficient Gaussian Process Classification (EGPC)-based PLA scheme, aiming for reliable and lightweight authentication. Following Bayes’ theorem, we model configurable fingerprints using GPs and employ the expectation propagation method to identify unknown fingerprints. Given the difficulty of obtaining sufficient labeled fingerprint samples to train PLA models, we propose three fingerprint selection algorithms. These algorithms select unlabeled fingerprints and query their identities using upper-layer authentication mechanisms. Among these methods, the optimal algorithm reduces the number of training fingerprints needed through importance sampling and eliminates the requirement for PLA model retraining through joint distribution calculation. Simulations results reveal that, in comparison with non-RIS-based approaches, the RIS-aided PLA framework decreases the authentication error rate by 98.69%. In addition, our designed fingerprint selection algorithms achieve a reduction in the authentication error rate of up to 86.93% compared to baseline active learning schemes. © 1975-2011 IEEE.

关键词： Active learning

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Adaptive Bitrate Video Semantic Increment Transmission System Based on Buffer and Semantic Importance

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IEEE Transactions on Network and Service Management 2025年

作者： Wang, Kaijie Bao, Zhicheng Liu, Kaijun Liang, Haotai Dong, Chen Xu, Xiaodong Li, Lin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Peng Cheng Laboratory Department of Broadband Communication Guangdong Shenzhen China MIGU Co. Ltd Beijing China Migu Meland Co. Ltd China

Significant progress has been made in researching video semantic communication technology and adaptive bitrate (ABR) algorithms. However, wireless network fluctuations challenge video semantic communication systems without ABR algorithms to achieve a satisfactory balance between high semantic recovery accuracy and efficient bandwidth utilization. This paper proposes an adaptive bitrate video semantic increment transmission system based on buffer and semantic importance to address this issue. Firstly, a buffer-based video semantic increment transmission system is designed to dynamically adjust the amount of video semantic data transmitted by the transmitter based on network fluctuations. Then, a novel Deep Learning and Reinforcement Learning based ABR algorithm (DR-ABR) is developed to determine the optimal video incremental ratio under the current network conditions. Furthermore, a semantic feature compression technology based on semantic importance is proposed to compress the video data according to the abovementioned ratio. Experimental results demonstrate that the proposed method outperforms traditional approaches in terms of video semantic transmission performance. © 2004-2012 IEEE.

关键词： adaptive bitrate algorithm buffer increment transmission Video semantic communication

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Real-Time Proportional-Integral-Derivative(PID)Tuning Based on Back Propagation(BP)Neural Network for Intelligent Vehicle Motion Control

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Computers, Materials & Continua 2025年第5期83卷 2375-2401页

作者： Liang Zhou Qiyao Hu Xianlin Peng Qianlong Liu School of Information Science and Technology Northwest UniversityXi’an710127China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest UniversityXi’an710127China Generative Artificial Intelligence and Mixed Reality Key Laboratory of Higher Education Institutions in Shaanxi Province Xi’an710127China Shaanxi Silk Road Cultural Heritage Digital Protection and Inheritance Collaborative Innovation Center Xi’an710127China School of Art Northwest UniversityXi’an710127China Network and Data Center Northwest UniversityXi’an710127China

Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and ***,deficiencies in these systems can lead to significant operational *** paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter *** paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and *** BP neural network is first trained to capture the nonlinear dynamic characteristics of the *** network is then combined with the PID controller to forma hybrid control *** output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight *** experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 ***,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.

关键词： PID control backpropagation neural network hybrid control nonlinear dynamic processes edge intelligence

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Service Anycast Forwarding for Software Defined Computing Power Network

Service Anycast Forwarding for Software Defined Computing Po...

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IEEE Conference on Wireless Communications and networking

作者： Wenzheng Wang Renchao Xie Qinqin Tang Tao Huang Tianjiao Chen Zehui Xiong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China China Mobile Research Institute Pillar of Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of the computing power network (CPN), which integrate edge computing, cloud computing, and network infrastructure, replicated computing services are increasingly distributed to meet user demands for location-independent, reliable, and low latency services. Service anycast forwarding coordinates distributed service instances by binding them to a unified identifier and dynamically routing requests to the optimal instance. However, challenges such as varying user demand distribution, network complexity, and service instance heterogeneity complicate balanced service forwarding. To address these, we propose an SDN-based service anycast forwarding mechanism for CPN (SA-CPN). In the data plane, a cyclic forwarding queue efficiently maps weighted strategies and selects instances for each service request, improving policy performance. In the control plane, an optimal transport model balances network and computation latency based on service instance capabilities. We further design an optimal transport-based service anycast forwarding algorithm (OTSAF) using Sinkhorn iterations. Our implementation of SA-CPN in a real system shows that OTSAF consistently outperforms four baseline methods across various performance metrics.

关键词： Computational modeling Software algorithms Distribution networks Routing Software Complexity theory Reliability Software defined networking Low latency communication Edge computing

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Construction and Count of 1-Resilient Rotation Symmetric Boolean Functions on pr Variables

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Chinese Journal of Electronics 2025年第4期23卷 816-820页

作者： Jiao Du Shanqi Pang Qiaoyan Wen Xin Liao College of Mathematics and Information Science Henan Normal University Xinxiang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China College of Information Science and Engineering Hunan University Changsha China

Construction and count of 1-resilient Rotation symmetric Boolean functions (RSBFs) on $p^{r}$ variables are demonstrated. It is proved that constructions of 1-resilient RSBFs on $p^{r}$ variables are equivalent to solving an equation system. An accurate enumeration formula of all 1-resilient RSBFs on $p^{r}$ variables is also proposed. Some examples are given, and the exact numbers of 1-resilient RSBFs on 8 and 9 variables are obtained respectively.

关键词： Sufficient conditions Boolean functions Accuracy Education Medical services NP-complete problem Cryptography

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Semi-Asynchronous Federated Split Learning for Computing-Limited Devices in Wireless Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Ao, Huiqing Tian, Hui Ni, Wanli Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science Singapore

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness. © 2002-2012 IEEE.

关键词： Federated learning

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