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Dynamic Convolution and Transformer Based Dual-Branch Coding in Semantic Communication System

IEEE Communications Letters 2025年第5期29卷 1161-1165页

作者： Yang, Jiaxin Bai, Zhiquan Xu, Xiaodong Teng, Xuchao Sun, Mengying Kwak, KyungSup Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Information Science and Engineering Qingdao266237 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China INHA University Department of Information and Communication Engineering Incheon22212 Korea Republic of

Semantic communication is a potential key technology in 6G intelligent communication era. To reduce the information redundancy and improve the accuracy of text data transmission, this paper proposes a novel dual-branch joint source-channel coding model in semantic communication system that integrates dynamic convolution (DynConv) with Transformer architecture. The system utilizes the convolutional attention mechanism to capture the local semantic details and the Transformer self-attention mechanism for contextual associations within sentences. In particular, we further investigate the dimension allocation strategy between these two attention mechanisms, seeking the optimal balance between the recovery performance and complexity of the system. Simulation results demonstrate that, compared with the traditional communication system and the typical semantic communication system based on the standard Transformer, the proposed system achieves higher recovery performance, better robustness, and lower complexity. © 1997-2012 IEEE.

关键词： Convolutional neural networks

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Electromagneticwave property inspired radio environment knowledge construction and artificial intelligence based verification for6G digital twin channel

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Frontiers of Information technology & Electronic Engineering 2025年第2期26卷 260-277页

作者： Jialin WANG Jianhua ZHANG Yutong SUN Yuxiang ZHANG Tao JIANG Liang XIA State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China China Mobile Research Institute Beijing 100053China

As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless *** electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing *** the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction *** solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location *** effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,*** also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s *** results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.

关键词： Digital twin channel Radio environment knowledge(REK)pool Wireless channel Environmental information Interpretable REK construction Artificial intelligence based knowledge verification

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SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost

Proceedings of the ACM on Management of Data

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Proceedings of the ACM on Management of Data 2025年第1期3卷 1-26页

作者： Zongda Han Xiang Cheng Wenhong Zhao Jiaxin Fu Zhaofeng He Sen Su State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Extreme Gradient Boosting (XGBoost) demonstrates excellent performance in practice and is widely used in both industry and academic research. This extensive application has led to a growing interest in employing multi-party data to develop more robust XGBoost models. In response to increasing concerns about privacy leakage, secure vertical federated XGBoost is proposed. It employs secure multi-party computation techniques, such as secret sharing (SS), to allow multiple parties holding vertically partitioned data, i.e., disjoint features on the same samples, to collaborate in constructing an XGBoost model. However, the running efficiency is the primary obstacle to the practical application of existing protocols, especially in multi-party settings. The reason is that these protocols not only require the execution of data-oblivious computations to protect intermediate results, leading to high computational complexity, but also involve a large number of SS-based non-linear operations with high overheads, e.g., division operations in gain score calculation and comparison operations in best split selection. To this end, we present a secure and efficient multi-party protocol for vertical federated XGBoost, called SecureXGB, which can perform the collaborative training of an XGBoost model in an SS-friendly manner. In SecureXGB, we first propose a parallelizable multi-party permutation method, which can secretly and efficiently permute all samples before model training to reduce the reliance on data-oblivious computations. Then, we design a linear gain score that can be evaluated without involving division operations and has equivalent utility to the original gain score. Finally, we develop a synchronous best split selection method to secretly identify the best split with the maximum gain score using a minimal number of comparison operations. Experimental results demonstrate that SecureXGB can achieve better training efficiency than state-of-the-art protocols without the los

关键词： gradient boosting tree privacy-preserving machine learning vertical federated learning

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Relay Node Placement with Base Stations in Wireless Sensor Networks Fault-Tolerant

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Chinese Journal of Electronics 2025年第4期23卷 794-800页

作者： Zhipeng Gao Kan Chen Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

It has been proposed to deploy relay nodes for the sake of prolonging Wireless sensor networks (WSN) lifetime, such that sensors transmit the sensed data to them which in turn delivers the data to base stations. For survivability requirements, relay placements which considers fault tolerant ability have been noticed and studied. While related works are limited or most existing works don't take factors such as fault tolerance, or base stations into account comprehensively in two tired WSN. We focus on fault tolerant relay node placement in two-tiered heterogeneous WSN with base stations. As far as we know, fault tolerance contains two fundamental aspects, for one is multi-coverage and the second is multi-path. It is a NP-hard problem and figure out an approximation, whose approximation ratio is enhanced to be $(18+\epsilon)$ . While a sub-problem approximation is also described as supplementary. Experimental results verify that the number of relay nodes deployed by our algorithm is somewhat superior to existed relay node placement solutions.

关键词： Measurement Fault tolerance Wireless sensor networks Base stations NP-hard problem Fault tolerant systems Approximation algorithms Sensors Relays

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CycleOIE: A Low-Resource Training Framework For Open Information Extraction 31

CycleOIE: A Low-Resource Training Framework For Open Informa...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Jin, Zhihong Zhang, Chunhong Hu, Zheng Yu, Jibin Ma, Ruiqi Chen, Qingyun Liao, Xiaohao Zhang, Yanxing State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Beijing Unimonet Co. Ltd. China Hebei University of Architecture China

ISBN: (纸本)9798891761964

Open Information Extraction (OpenIE) aims to extract structured information in the form of triples from unstructured text, serving as a foundation for various downstream NLP tasks. Despite the success of neural OpenIE models, their dependence on large-scale annotated datasets poses a challenge, particularly in low-resource settings. In this paper, we introduce a novel approach to address the low-resource OpenIE task through two key innovations: (1) we improve the quality of training data by curating small-scale, high-quality datasets annotated by a large language model (GPT-3.5), leveraging both OpenIE principles and few-shot examples to form LSOIE-g principles and LSOIE-g examples;(2) we propose CycleOIE, a training framework that maximizes data efficiency through a cycle-consistency mechanism, enabling the model to learn effectively from minimal data. Experimental results show that CycleOIE, when trained on only 2k+ instances, achieves comparable results to previous models trained on over 90k instances. Our contributions are further validated through extensive experiments, demonstrating the superior performance of CycleOIE and our curated LSOIE-g datasets in low-resource OpenIE as well as revealing the internal mechanisms of CycleOIE. Code and data at https://***/Jinsns/CycleOIE. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Joint Beamforming Design for Multi-Functional RIS-Aided Over-the-Air Computation

Joint Beamforming Design for Multi-Functional RIS-Aided Over...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Xinran Tian, Hui Ni, Wanli State Key laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9798350368741

To facilitate fast data aggregation in Internet of Things, over-the-air computation (AirComp) is a communication-efficient enabler by virtue of its high spectrum efficiency and low transmission latency. However, traditional AirComp faces challenges such as signal misalignment, imperfect channel state information (CSI), and noisy fading channels. In this paper, we employ a multi-functional reconfigurable intelligent surface (MF-RIS) to alleviate mean square error (MSE) of AirComp through a joint design of transceiver beamforming and MF-RIS coefficients, but it necessitates solving a mixed-integer nonlinear programming problem. To tackle this issue, we propose an alternating optimization algorithm based on the semidefinite relaxation approach and difference-of-convex programming. Numerical results underscore the performance gains achieved by the proposed algorithm, as well as the remarkable proficiency of the MF-RIS in suppressing MSE under imperfect CSI. © 2025 IEEE.

关键词： imperfect channel state information Internet of Things Over-the-air computation reconfigurable intelligent surface

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An Efficient Anti-Quantum Blind Signature with Forward Security for Blockchain-Enabled Internet of Medical Things

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Computers, Materials & Continua 2025年第2期82卷 2293-2309页

作者： Gang Xu Xinyu Fan Xiu-Bo Chen Xin Liu Zongpeng Li Yanhui Mao Kejia Zhang School of Information Science and Technology North China University of TechnologyBeijing100144China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China School of Mathematical Science Heilongjiang UniversityHarbin150080China School of Digtial and Intelligence Industry Inner Mongolia University of Science and TechnologyBaotou014010China Institute for Network Sciences and Cyberspace Tsinghua UniversityBeijing100084China Yunnan Key Laboratory of Blockchain Application Technology Kunming650233China Yunnan Innovation Institute of Beihang University Kunming650233China

Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure, quantum-resistant blind signatures. In this paper, we introduce lattice-based forward-secure blind signature (LFSBS), a lattice-based forward-secure blind signature scheme for medical privacy preservation in BIoMT. LFSBS achieves forward security by constructing a key evolution mechanism using a binary tree structure. This mechanism ensures that even if future encryption keys are leaked, past data can still remain secure. Meanwhile, LFSBS realizes post-quantum security based on the hardness assumption of small integer solution (SIS), making it resistant to potential quantum computing attacks. In addition, we formally define and prove the security of LFSBS in a random oracle model, including blindness and forward-secure unforgeability. Comprehensive performance evaluation shows that LFSBS performs well in terms of computational overhead, with a reduction of 22%–73% compared to previous schemes.

关键词： Internet of Things blockchain forward-secure blind signature

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Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images

Knowledge Distillation for Image Restoration : Simultaneous ...

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

作者： Yongheng Zhang Danfeng Yan State Key Laboratory of Networking and Switching Technology BUPT Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Model compression through knowledge distillation has seen extensive application in classification and segmentation tasks. However, its potential in image-to-image translation, particularly in image restoration, remains underexplored. To address this gap, we propose a Simultaneous Learning Knowledge Distillation (SLKD) framework tailored for model compression in image restoration tasks. SLKD employs a dual-teacher, single-student architecture with two distinct learning strategies: Degradation Removal Learning (DRL) and Image Reconstruction Learning (IRL), simultaneously. In DRL, the student encoder learns from Teacher A to focus on removing degradation factors, guided by a novel BRISQUE extractor. In IRL, the student decoder learns from Teacher B to reconstruct clean images, with the assistance of a proposed PIQE extractor. These strategies enable the student to learn from degraded and clean images simultaneously, ensuring high-quality compression of image restoration models. Experimental results across five datasets and three tasks demonstrate that SLKD achieves substantial reductions in FLOPs and parameters, exceeding 80%, while maintaining strong image restoration performance.

关键词： Degradation Image segmentation Image coding Translation Noise reduction Signal processing Robustness Image restoration Speech processing Image reconstruction

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Knowledge Distillation for Image Restoration: Simultaneous Learning from Degraded and Clean Images

arXiv

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arXiv 2025年

作者： Zhang, Yongheng Yan, Danfeng State Key Laboratory of Networking and Switching Technology BUPT Beijing China

关键词： Image reconstruction

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Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression

arXiv

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arXiv 2025年

作者： Zhang, Yongheng Yan, Danfeng State Key Laboratory of Networking and Switching Technology BUPT Beijing China

Transformer-based encoder-decoder models have achieved remarkable success in image-to-image transfer tasks, particularly in image restoration. However, their high computational complexity—manifested in elevated FLOPs and parameter counts—limits their application in real-world scenarios. Existing knowledge distillation methods in image restoration typically employ lightweight student models that directly mimic the intermediate features and reconstruction results of the teacher, overlooking the implicit attention relationships between them. To address this, we propose a Soft Knowledge Distillation (SKD) strategy that incorporates a Multi-dimensional Cross-net Attention (MCA) mechanism for compressing image restoration models. This mechanism facilitates interaction between the student and teacher across both channel and spatial dimensions, enabling the student to implicitly learn the attention matrices. Additionally, we employ a Gaussian kernel function to measure the distance between student and teacher features in kernel space, ensuring stable and efficient feature learning. To further enhance the quality of reconstructed images, we replace the commonly used L1 or KL divergence loss with a contrastive learning loss at the image level. Experiments on three tasks—image deraining, deblurring, and denoising—demonstrate that our SKD strategy significantly reduces computational complexity while maintaining strong image restoration capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Image reconstruction

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