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BUPTCMCC-6G-CMG+: a GBSM-based ISAC standard channel model generator

Science China(Information Sciences) 2025年第5期68卷 235-249页

作者： Changsheng ZHAO Jianhua ZHANG Yuxiang ZHANG Lei TIAN Heng WANG Hanyuan JIANG Yameng LIU Wenjun CHEN Tao JIANG Guangyi LIU State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Future Research Laboratory China Mobile Research Institute

Integrated sensing and communication(ISAC) has been recognized as the key technology in the vision of the sixth generation(6G) era. With the emergence of new concepts in mobile communications, the channel model is the prerequisite for system design and performance evaluation. Currently, the 3rd generation partnership project(3GPP) Release 19 is advancing the standardization of ISAC channel models. Nevertheless, a unified modeling framework has yet to be established. This paper provides a simulation diagram of ISAC channel modeling extended based on the geometry-based stochastic model(GBSM),compatible with existing 5G channel models and the latest progress in the 3GPP Rel-19 standardization. We first introduce the progress of the ISAC channel model standardization in general. Then, a concatenated channel modeling method is presented considering the team's standardization proposals, which are implemented on the BUPTCMCC-6G-CMG+ channel model generator. We validated the model in cumulative probability density function in statistical extension of angle and delay, and radar cross section(RCS). Simulation results show that the proposed model can realistically characterize the feature of channel concatenation and RCS within the ISAC channel.

关键词： channel model generator channel concatenation ISAC channel radar cross section (RCS) 3GPP standardization

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Feasibility study of cooperative sensing: radar cross section, synchronization, cooperative cluster,performance and prototype

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Science China(Information Sciences) 2025年第5期68卷 194-212页

作者： Guangyi LIU Rongyan XI Tao JIANG Xiaoqian WANG Lincong HAN Jing JIN Jing DONG Yajuan WANG Yuxiang ZHANG Qixing WANG Jiangzhou WANG Future Research Laboratory China Mobile Research Institute State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Engineering University of Kent

This paper investigates the potential of integrated sensing and communication(ISAC) for the upcoming sixth generation(6G) mobile communications system, which is expected to go beyond communication and incorporate sensing capabilities. A major challenge with conventional ISAC systems is the self-interference caused by using a base station(BS)as both the sensing transmitter and receiver, which requires complex and costly hardware upgrades to enable full *** address this issue, we propose a cooperative sensing approach, where node A acts as the transmitter and node B as the receiver. In this study, we analyze the scattering characteristics of targets in a cooperative sensing scenario and find that the radar cross section(RCS) of unmanned aerial vehicle(UAV) follows a lognormal distribution. We then propose a time-frequency synchronization method, a cooperative sensing cluster construction method, and a multi-node signal-level fusion method to accurately determine the target's position. Simulation results, incorporating non-ideal factors, show that the proposed system can achieve precise target positioning. The accuracy is improved by more than five times compared to mono-static sensing and over 25% compared to result-level fusion methods. Additionally, prototype experimental results validate the feasibility of the proposed system.

关键词： 6G integrated sensing and communication synchronization bistatic RCS multi-node collaboration cooperative cluster prototype

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

Knowledge Distillation for Image Restoration: Simultaneous L...

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

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

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： feature extractor image restoration Knowledge distillation simultaneous learning

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

Soft Knowledge Distillation with Multi-Dimensional Cross-Net...

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

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

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： contrastive learning image restoration Knowledge distillation multi-dimensional cross-net attention

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An efficient quantum proactive incremental learning algorithm

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 45-53页

作者： Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China

In scenarios where a large amount of data needs to be learned,incremental learning can make full use of old knowledge,signif-icantly reduce the computational cost of the overall learning process,and maintain high *** this paper,taking the MaxCut problem as our example,we introduce the idea of incremental learning into quantum computing,and propose a Quantum Proactive Incremental Learning algorithm(QPIL).Instead of a one-off training of quantum circuit,QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices,proactively reducing large-scale problems to smaller ones to solve in steps,providing an efficient solution for ***,some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at ***,in each incremental phase,the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current *** perform experiments on 120 different small-scale graphs,and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR),time cost,anti-forgetting,and solv-ing *** particular,QPIL’s AR surpasses 20%of mainstream quantum baselines,and the time cost is less than 1/5 of *** idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale MaxCut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

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Towards Bare-Hand Interaction for Whiteboard Collaboration in Virtual Reality

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Proceedings of the ACM on Human-Computer Interaction 2025年第2期9卷 1-31页

作者： Liu, Guangtian Su, Haonan Wang, Jingyu Qi, Qi Sun, Haifeng Zhuang, Zirui Ren, Pengfei Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Haidian Qu Beijing China Pengcheng Laboratory Shenzhen China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China *** Beijing China

Whiteboard collaboration in virtual reality (VR) is an important task in collaborative virtual environments. The current research mainly relies on the use of controllers or dedicated pens but additional devices will cause inconvenience to users. Bare-hand writing offers rich collaborative semantics through natural gestures but remains underexplored. This paper addresses challenges and solutions for bare-hand whiteboard collaboration. We analyze the input process and identify key challenges in determining pen-drop, writing, and pen-lift intentions while maintaining user control over their avatar. Our approach addresses two VR scenarios: one without and one with physical planes. The method for the first case is called Air-writing, which dynamically adjusts the distance between the avatar’s torso and the virtual whiteboard during the processes of pen-drop and pen-lift to ensure a consistent writing experience in VR. The method for the second case is called Physical-writing, which allows users to write smoothly with passive haptic feedback and physical constraints provided by the real surface by remapping the whiteboard in VR with a plane in reality. A comprehensive user study is conducted to evaluate communication efficiency, input accuracy, collaboration efficiency, and user experience of the two methods. The experimental results indicate that bare-hand interaction improves communication efficiency by 8% over controllers and performs similarly to real-world whiteboard collaboration. The Physical-writing method also demonstrates higher accuracy and user satisfaction compared to the Air-writing method. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtual environments

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IncEFL:a sharing incentive mechanism for edge-assisted federated learning in industrial IoT

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Digital Communications and Networks 2025年第1期11卷 106-115页

作者： Jiewei Chen Shaoyong Guo Tao Shen Yan Feng Jian Gao Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Post and TelecommunicationsBeijing 100087China Kunming University of Science and Technology Kunming 650031China Yunnan Provincial Academy of Science and Technology Kunming 650228China

As the information sensing and processing capabilities of IoT devices increase,a large amount of data is being generated at the edge of Industrial IoT(IIoT),which has become a strong foundation for distributed Artificial Intelligence(AI)***,most users are reluctant to disclose their data due to network bandwidth limitations,device energy consumption,and privacy *** address this issue,this paper introduces an Edge-assisted Federated Learning(EFL)framework,along with an incentive mechanism for lightweight industrial data *** order to reduce the information asymmetry between data owners and users,an EFL model-sharing incentive mechanism based on contract theory is *** addition,a weight dispersion evaluation scheme based on Wasserstein distance is *** study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers'profit and ensure the quality of training *** incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is ***,the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.

关键词： Federated learning Data sharing Edge intelligence Incentives Contract theory

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Optimizing Dual-Mode UAV-Assisted Remote IoT Data Collection with Decentralized DRL in 6G-Enabled Space-Air-Ground Networks

Optimizing Dual-Mode UAV-Assisted Remote IoT Data Collection...

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

作者： Jin, Ru Lin, Rongheng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798350368741

As sixth-generation (6G) networks extend to remote regions, efficient data collection from Internet of Things (IoT) devices faces challenges such as limited infrastructure, diverse latency requirements, and energy constraints. To address these, we propose a dual-mode data transmission strategy: (1) a UAV-satellite network for real-time delay-sensitive data, where wireless power transfer (WPT) is introduced to alleviate energy depletion caused by frequent data transmission, and a deadline τmax is set in each timeslot to maintain data freshness. (2) a carry-store mode for delay-tolerant data. Both aim to minimize energy consumption while maximizing data collection. Specifically, we develop a time scheduling policy to allocate data and energy transmission time for delay-sensitive data within τmax, and propose a novel centralized control, distributed execution framework using decentralized deep reinforcement learning (DRL), termed DRL-Schedule, designed to optimize UAV navigation and ensure energy-efficient, timely data collection. Simulation results demonstrate that DRL-Schedule achieves an average improvement of approximately 11.2% and 12.8% over the best baseline PPO in energy efficiency, when varying different number of UAVs and IoTs, respectively, and notably surpassing GA, Greedy and Random. © 2025 IEEE.

关键词： 6G Deep reinforcement learning Energy-efficiency UAV-LEO integrated data collection WPT

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LEP: Leveraging Local Entropy Pruning for Sparsity in Large Language Models

LEP: Leveraging Local Entropy Pruning for Sparsity in Large ...

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

作者： Chen, Yuli Cheng, Bo Zhang, Yingying Zhang, Shuhao Wu, Zhixuan Meng, Fanshen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798350368741

The application of Large Language Models (LLMs) is rapidly expanding in fields such as natural language processing and computer vision. However, due to the enormous number of model parameters, while their emergent capabilities enhance performance, they also incur significant computational and storage costs, particularly during inference. This makes it particularly challenging to deploy large models in resource-constrained environments. To address this issue, we propose an innovative pruning method based on local entropy, aimed at improving model performance while reducing computational costs. Our approach leverages a small amount of calibration data and calculates the importance of weights based on the local entropy of weights and input activations, applying it to both unstructured and semi-structured pruning without the need for complex weight updates or retraining. Experimental results demonstrate that our method shows superior performance across models of various scales. Specifically, at a sparsity rate of 50%, our method significantly reduces model perplexity without the need for weight updates and outperforms existing pruning techniques such as SparseGPT and Wanda in zero-shot tasks. The code is available at: this https URL. © 2025 IEEE.

关键词： entropy large language models model compression unstructed pruning

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Two-Hop Delay-Aware Energy Efficiency Resource Allocation in Space-Air-Ground Integrated Smart Grid Network

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Computers, Materials & Continua 2025年第5期83卷 2429-2447页

作者： Qinghai Ou Min Yang Jingcai Kong Yang Yang Beijing FibrLink Communications Co.Ltd. Beijing100071China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid *** such cases,a space-air-ground integrated network serves as an effective emergency *** study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity *** to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop *** the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory *** first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)*** iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop *** results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.

关键词： Energy efficiency effective capacity delay requirement power allocation smart grid space-air-ground integrated network

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