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Resolution-aware beam scanning for joint detection and communication in ISAC systems

science China(Information sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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Cache Allocation in Multi-Tenant Edge Computing: An Online Model-Based Reinforcement Learning Approach

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IEEE Transactions on Cloud Computing 2025年第2期13卷 459-472页

作者： Ben-Ameur, Ayoub Araldo, Andrea Chahed, Tijani Dán, György Institut Polytechnique de Paris SAMOVAR Telecom SudParis Palaiseau91120 France KTH Royal Institute of Technology Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Stockholm11428 Sweden

We consider a Network Operator (NO) that owns Edge Computing (EC) resources, virtualizes them and lets third party Service Providers (SPs) run their services, using the allocated slice of resources. We focus on one specific resource, i.e., cache space, and on the problem of how to allocate it among several SPs in order to minimize the backhaul traffic. Due to confidentiality guarantees, the NO cannot observe the nature of the traffic of SPs, which is encrypted. Allocation decisions are thus challenging, since they must be taken solely based on observed monitoring information. Another challenge is that not all the traffic is cacheable. We propose a data-driven cache allocation strategy, based on Reinforcement Learning (RL). Unlike most RL applications, in which the decision policy is learned offline on a simulator, we assume no previous knowledge is available to build such a simulator. We thus apply RL in an online fashion, i.e., the model and the policy are learned by directly perturbing and monitoring the actual system. Since perturbations generate spurious traffic, we thus need to limit perturbations. This requires learning to be extremely efficient. To this aim, we devise a strategy that learns an approximation of the cost function, while interacting with the system. We then use such an approximation in a Model-Based RL (MB-RL) to speed up convergence. We prove analytically that our strategy brings cache allocation boundedly close to the optimum and stably remains in such an allocation. We show in simulations that such convergence is obtained within few minutes. We also study its fairness, its sensitivity to several scenario characteristics and compare it with a method from the state-of-the-art. © 2013 IEEE.

关键词： Contrastive Learning

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An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

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computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

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AutoGraph: Enabling Visual Context via Graph Alignment in Open Domain Multi-Modal Dialogue Generation 24

AutoGraph: Enabling Visual Context via Graph Alignment in Op...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhao, Deji Han, Donghong Yuan, Ye Ning, Bo Li, Mengxiang He, Zhongjiang Song, Shuangyong School of Computer Science and Engineering Northeastern University Shenyang China School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Information Science and Technology Dalian Maritime University Dalian China China Telecom Corp Ltd Beijing China China Telecom Corp Ltd Beijing China

ISBN: (纸本)9798400706868

Open-domain multi-modal dialogue system heavily relies on visual information to generate contextually relevant responses. The existing open-domain multi-modal dialog generation methods ignore the complementary relationship between multiple modalities, and are difficult to integrate with LLMs. To tackle these challenges, we introduce AutoGraph, an innovative method for constructing visual context graphs automatically. We aim to structure complex information and seamlessly integrate it with large language models (LLMs), aligning information from multiple modalities at both semantic and structural levels. Specifically, we fully connect the text graphs and scene graphs, and then trim unnecessary edges via LLMs to automatically construct a visual context graph. Next, we design several graph sampling grammar for the first time to convert graph structures into sequence which is suitable for LLMs. Finally, we propose a two-stage fine-tuning strategy to allow LLMs to understand graph sampling grammar and generate responses. We validate our proposed method on text-based LLMs, and visual-based LLMs, respectively. Experimental results show that our proposed method achieves state-of-the-art performance on multiple public datasets. © 2024 ACM.

关键词： Semantics

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Point-Multipoint Communication Scheduling in Industrial Internet: A Quick Survey 11

Point-Multipoint Communication Scheduling in Industrial Inte...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Chen, Yufeng Jiang, Ping Qu, Yida Wu, Mengqi Luo, Lu Yang, Ming Lu, Wenqian Southeast University School of Computer Science and Engineering School of Software Nanjing China China Telecom Nanjing Branch Nanjing China China Telecom Beijing China

ISBN: (纸本)9798350345346

The industrial Internet represents a significant innovation merging Internet technologies, big data, artificial intelligence, and advanced manufacturing, which is characterized by high technological and commercial value. Industrial communication systems need to satisfy precision manufacturing, including the expansive connections and tight requirements such as real time, determinism, and reliability. Hence, an efficient communication scheduling mechanism is essential. In contrast to conventional separate point-to-point (P2P) scheduling, point-to-multipoint multicasting and multipoint-to-point data aggregation are proven to be more effective in alleviating network congestion, enhancing throughput, and improving efficiency in the context of the ubiquitous point-to-multipoint communication in the industrial Internet. This paper focuses on communication in intra-datacenter and inter-datacenter subject to industrial quality of service (QoS) demands. Initially, we introduce a mathematic model for network and point-to-multipoint communication requests. Subsequently, we provide a comprehensive analysis of multicast route selection and traffic rate allocation algorithms that are deadline-aware and delay-aware. Additionally, we discuss the benefits of in-network data aggregation, including insights into aggregation pathways and aggregator placement algorithms, which are valuable for reference in data transfer scheduling in the industrial Internet. © 2023 IEEE.

关键词： Multicasting

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Multi-Objective Optimization for NOMA-Based Mobile Edge Computing Offloading by Maximizing System Utility

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China Communications 2023年第12期20卷 156-165页

作者： Hong Qin Haitao Du Huahua Wang Li Su Yunfeng Peng Chongqing University of Post and Telecom Chongqing 400065China China Mobile Research Institute Beijing 100000China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

Mobile Edge Computing(MEC)is a technology for the fifth-generation(5G)wireless communications to enable User Equipment(UE)to offload tasks to servers deployed at the edge of ***,taking both delay and energy consumption into consideration in the 5G MEC system is usually complex and ***-orthogonal multiple access(NOMA)enable more UEs to offload their computing tasks to MEC servers using the same spectrum resources to enhance the spectrum efficiency for 5G,which makes the problem even more complex in the NOMA-MEC *** this work,a system utility maximization model is present to NOMA-MEC system,and two optimization algorithms based on Newton method and greedy algorithm respectively are proposed to jointly optimize the computing resource allocation,SIC order,transmission time slot allocation,which can easily achieve a better trade-off between the delay and energy *** simulation results prove that the proposed method is effective for NOMA-MEC systems.

关键词： computation offloading mobile edge computing non-orthogonal multiple access resource allocation

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RecGuard: An efficient privacy preservation blockchain-based system for online social network users

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Blockchain(Research and Applications) 2023年第1期4卷 36-47页

作者： Samuel Akwasi Frimpong Mu Han Edward Kwadwo Boahen Rexford Nii Ayitey Sosu Isaac Hanson Otu Larbi-Siaw Isaac Baffour Senkyire Department of Computer Science and Technology Jiangsu University212013ZhenjiangChina Department of Computer Engineering Ghana Communication Technology UniversityGhana Department of Computer Science Ghana Communication Technology UniversityGhana Department of Telecom Engineering Ghana Communication Technology UniversityGhana School of Finance and Economics Jiangsu University 212013ZhenjiangChina

Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and ***,it raises several questions about privacy,especially for users who prefer to remain anonymous,especially on online social networks(OSNs).Moreover,due to the commercialization of online users'data,some service providers sell users'data to third parties at the blind side of the users,which leads to trust issues between users and service *** matters call for a system that gives online users much-needed control and autonomy of their *** the advancement of blockchain technology,many research institutions are experimenting with decentralized technologies to resolve the OSN user dilemma of privacy intrusion against third parties and *** resolve these limitations,we propose RecGuard,a privacy preservation blockchain-based network *** developed two smart contracts,RG-SH and RG-ST,to ensure the security and privacy of user *** RG-SH manages user data,whereas the RGST stores data.A graph convolutional network(GCN)was integrated with the blockchain-based system to detect malicious ***,we implemented our framework prototype on a locally simulated *** analysis and experiment results show that the proposed scheme demonstrates the effectiveness and privacy of users in our framework.

关键词： Blockchain Privacy Online social network Recommender system

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Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty 25th

Would You Trust an AI Doctor? Building Reliable Medical Pre...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Azam, Ubaid Razzak, Imran Vishwakarma, Shelly Hacid, Hakim Zhang, Dell Jameel, Shoaib School of Electronics and Computer Science University of Southampton Southampton United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia Technology Innovation Institute Abu Dhabi United Arab Emirates China Telecom Shanghai China

ISBN: (纸本)9789819605729

The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability. This paper proposes a novel approach to address these challenges, introducing a Bayesian Monte Carlo Dropout model with kernel modelling. Our model is designed to enhance reliability on small medical datasets, a crucial barrier to the wider adoption of AI in healthcare. This model leverages existing language models for improved effectiveness and seamlessly integrates with current workflows. We demonstrate significant improvements in reliability, even with limited data, offering a promising step towards building trust in AI-driven medical predictions and unlocking its potential to improve patient care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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MmRotation: Unlocking Versatility of a Single mmWave Radar via Horizontal Mobility and Azimuthal Rotation

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6045-6061页

作者： Zhang, Duo Zhang, Xusheng Yin, Zhehui Xie, Yaxiong Wei, Hewen Chang, Zhaoxin Li, Wenwei Zhang, Daqing Peking University School of Electronics Engineering and Computer Science Beijing100871 China University at Buffalo BuffaloNY United States Peking University School of Software and Microelectronics Beijing100871 China Peking University Key Lab of High Confidence Software Technologies Ministry of Education School of Computer Science Beijing100871 China Institut Polytechnique de Paris Telecom SudParis Cedex Evry91011 France

Indoor mmWave-based sensing technologies have garnered substantial interest from both the industrial and academic. Yet, the intrinsic challenge posed by the limited Field-of-View (FOV) of mmWave radars significantly restricts their coverage. This limitation necessitates careful selection of installation positions and orientations to optimize performance, thereby severely curtailing the versatility and widespread adoption of these systems. Traditionally, expanding coverage involved increasing the number of radar units. This paper introduces a novel approach to enhance the FOV by incorporating mobility, achieved by affixing the radar onto a pan-tilt unit capable of rotating along both the horizontal and azimuthal. Nevertheless, the disparity between the pan-tilt and the radar presents significant challenges for accurately rotating the radar's orientation. To mitigate this, we propose an automated calibration algorithm for radar and pan-tilt, ensuring precise calibration. Additionally, we have devised a radar orientation adjustment algorithm intended to automatically align the radar's FOV with the positions of detected objects to facilitate various applications. Through three case studies, we have demonstrated that mmRotation can greatly expand the sensing range, enabling support for multiple applications on a single radar, such as vital signs monitoring and fall detection. Comprehensive experimental results underscore that our system surpasses the current state-of-the-art (SOTA). © 2002-2012 IEEE.

关键词： Calibration

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Optimizing Acoustic Echo Cancellation with Variable Step Size in Adaptive Filtering

Optimizing Acoustic Echo Cancellation with Variable Step Siz...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Chen, Yen-Yi Wang, Jian-Hong Chan, Po-Cheng Liang, Kai-Wen Wang, Ze-Yu Wang, Jia-Ching School of Computer Science and Technology Shandong University of Technology Shandong Zibo China National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan Advanced Technology Laboratory Chunghwa Telecom Laboratories Taiwan

ISBN: (纸本)9798350353983

This paper presents an innovative approach to acoustic echo cancellation (AEC) by applying Variable Step Size (VSS) separately to each adaptive filtering technique: Normalized Least Mean Square (NLMS), and Proportionate Normalized Least Mean Square (PNLMS). This application is tailored to overcome the challenges of step size adjustment and signal sparsity, significantly enhancing system performance. We demonstrate that utilizing VSS with each algorithm independently allows for optimized control over convergence rates-larger step sizes quicken convergence while smaller sizes ensure smoother transitions. This method improves the robustness and efficiency of echo cancellation systems, removing the need for manual step size adjustments and proving highly effective in real-time applications. © 2024 IEEE.

关键词： Echo suppression

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