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arXiv 2023年

作者： Wang, Lei Ai, Bo Niu, Yong Jiang, Haiyan Mao, Shiwen Zhong, Zhangdui Wang, Ning The State Key Laboratory of Advanced Rail Autonomous Operation The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China

Terahertz wireless backhaul is expected to meet the high-speed backhaul requirements of future ultra-dense networks using millimeter-wave (mmWave) base stations (BSs). In order to achieve higher network capacity with limited resources and meet the quality of service (QoS) requirements of more users in the integrated mmWave access and terahertz backhaul network, this paper formulates a problem of maximizing the number of users successfully served in both the access and backhaul links. Since the problem is a non-linear integer optimization problem, a minimum rate ratio user association and transmission scheduling algorithm is proposed to obtain a suboptimal solution. The proposed algorithm takes the minimum rate ratio as the user association criterion and schedules first the users with fewer backhaul transmission slots. In addition, the algorithm will update the number of access transmission slots allocated to users and the access scheduling results after the backhaul scheduling phase. Numerical results show that the proposed algorithm outperforms several benchmark algorithms in terms of the number of served users and system throughput, and it can cope with a large number of bursty user requests. Copyright © 2023, The Authors. All rights reserved.

关键词： Millimeter waves

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FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection

引用

IEEE Transactions on Mobile Computing 2025年

作者： Geng, Jiaxiang Li, Boyu Qin, Xiaoqi Li, Yixuan Li, Liang Hou, Yanzhao Pan, Miao Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Taiyuan University of Technology College of Electronic Information Engineering Taiyuan030024 China Peng Cheng Laboratory Department of Advanced Interdisciplinary Research 518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States

Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can remarkably reduce its training latency over homogeneous clients, yet encounter severe model staleness, model drifts, memory cost and straggler issues in heterogeneous environments. To unleash the full potential of overlapping, we propose, FedEx, a novel federated learning approach to expedite FL training over mobile devices under data, computing and wireless heterogeneity. FedEx redefines the overlapping procedure with staleness ceilings to constrain memory consumption and make overlapping compatible with participation selection (PS) designs. Then, FedEx characterizes the PS utility function by considering the latency reduced by overlapping, and provides a holistic PS solution to address the straggler issue. FedEx also introduces a simple but effective metric to trigger overlapping, in order to avoid model drifts. Experimental results show that compared with its peer designs, FedEx demonstrates substantial reductions in FL training latency over heterogeneous mobile devices with limited memory cost. © 2002-2012 IEEE.

关键词： Federated learning Heterogeneous mobile devices Overlapping computing and communications Participant selection

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Reconfigurable Intelligent Surface Assisted High-Speed Train Communications: Coverage Performance Analysis and Placement Optimization

arXiv

引用

arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

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Revolutionizing Genomics with Reinforcement Learning Techniques

arXiv

引用

arXiv 2023年

作者： Karami, Mohsen Jahanian, Khadijeh Alizadehsani, Roohallah Argha, Ahmadreza Dehzangi, Iman Gorriz, Juan M. Zhang, Yu-Dong Hajati, Farshid Yang, Min Alinejad-Rokny, Hamid Amirkabir University of Technology Tehran Iran Faculty of Engineering and IT University of Technology Sydney Sydney Australia Deakin University VIC Australia School of Biomedical Engineering UNSW SYDNEY SydneyNSW2052 Australia Department of Computer Science Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States Department of Signal Theory Networking and Communications Universidad de Granada Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2350 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering UNSW SYDNEY SydneyNSW2052 Australia

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics. © 2023, CC BY-SA.

关键词： Adversarial machine learning

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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

arXiv

引用

arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Joint energy-efficient optimization of downlink and uplink with eicic in hetnet 1

引用

4th International Conference on IoT as a Service, IoTaaS 2018

作者： Zheng, Jie Gao, Ling Zhu, Dongxiao Wang, Hai Sun, Qian Niu, Jinping Li, Xiaoya Yang, Jianfeng State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xian710127 China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Xi’an Polytechnic University Xi’an China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (数字)9783030146573

ISBN: (纸本)9783030146566

. In this work, we propose to configure uplink access to the macro in almost blank subframes (ABSs) improving the its utilization. The ABSs based on Downlink and Uplink Decoupled (DUDe) are modeled as an energy-efficient optimization problem in Heterogeneous Networks (HetNets). The formulation is a mixed integer programming problem so that we provide a suboptimal algorithm. Simulation study show our proposed algorithm demonstrates better performance according to energy efficiency and capacity. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Energy efficiency

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Backhaul-Aware Uplink Communications In Full-Duplex Dbs-Aided Hetnets

arXiv

引用

arXiv 2019年

作者： Zhang, Liang Ansari, Nirwan Advanced Networking Laboratory Department of Electrical and Computer Engineering New Jersy Institute of Technology

Drone-mounted base stations (DBSs) are promising solutions to provide ubiquitous connections to users and support many applications in the fifth generation of mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we have investigated the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network, and this problem is decomposed into two sub-problems: the DBS Placement problem (including the vertical dimension and horizontal dimensions) and the joint UE association, power and bandwidth assignment (Joint-UPB) problem. Since the BUD problem is NP-hard, we propose approximation algorithms to solve the sub-problems and another, named the AA-BUD algorithm, to solve the BUD problem with guaranteed performance. The performance of the AA-BUD algorithm has been demonstrated via extensive simulations, and it is superior to two benchmark algorithms with up to 45.8% throughput improvement. Copyright © 2019, The Authors. All rights reserved.

关键词： Base stations

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Is perfect filtering enough leading to perfect phase correction for dMRI data?

arXiv

引用

arXiv 2021年

作者： Feihong, Liu Junwei, Yang Xiaowei, He Luping, Zhou Jun, Feng Dinggang, Shen School of Information Science and Technology Northwest University Xi’an China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom State-Province Joint Engineering Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi’an China School of Electrical and Information Engineering University of Sydney Sydney Australia Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Being complex-valued and low in signal-to-noise ratios, magnitude based diffusion MRI is confounded by the noise-floor that falsely elevates signal magnitude and incurs bias to the commonly used diffusion indices, such as fractional anisotropy (FA). To avoid noise-floor, most existing phase correction methods explore improving filters to estimate the noise-free background phase. In this work, after diving into the phase correction procedures, we argue that even a perfect filter is insufficient for phase correction because the correction procedures are incapable of distinguishing sign-symbols of noise, resulting in artifacts (i.e., arbitrary signal loss). With this insight, we generalize the definition of noise-floor to a complex polar coordinate system and propose a calibration procedure that could conveniently distinguish noise sign-symbols. The calibration procedure is conceptually simple and easy to implement without relying on any external technique, while keeping distinctly effective. Extensive experimental results, including those on both synthetic and real diffusion MRI data, demonstrate that the calibrated procedures successfully mitigate artifacts in diffusion MR images and FA maps, with improved accuracy on estimating FA in particular. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Uncertain Location Transmitter and UAV-Aided Warden Based LEO Satellite Covert Communication Systems

arXiv

引用

arXiv 2025年

作者： Peng, Pei Chen, Xianfu Xu, Tianheng Wu, Celimuge Zou, Yulong Ni, Qiang Soljanin, Emina School of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Nanjing210003 China Shenzhen CyberAray Network Technology Company Ltd. Guangdong Shenzhen518042 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Meta-Networking Research Center The University of Electro-Communications Tokyo182-8585 Japan School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom Department of Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States

We propose a novel covert communication system in which a ground user, Alice, transmits unauthorized message fragments to Bob, a low-Earth orbit satellite (LEO), and an unmanned aerial vehicle (UAV) warden (Willie) attempts to detect these transmissions. The key contribution is modeling a scenario where Alice and Willie are unaware of each other’s exact locations and move randomly within a specific area. Alice utilizes environmental obstructions to avoid detection and only transmits when the satellite is directly overhead. LEO satellite technology allows users to avoid transmitting messages near a base station. We introduce two key performance metrics: catch probability (Willie detects and locates Alice during a message chunk transmission) and overall catch probability over multiple message chunks. We analyze how two parameters impact these metrics: 1) the size of the detection window and 2) the number of message chunks. The paper proposes two algorithms to optimize these parameters. The simulation results show that the algorithms effectively reduce the detection risks. This work advances the understanding of covert communication under mobility and uncertainty in satellite-aided systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

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Energy-Aware Virtual Machine Management in Inter-Datacenter Networks over Elastic Optical Infrastructure

Energy-Aware Virtual Machine Management in Inter-Datacenter ...

引用

作者： Zhang, Liang Han, Tao Ansari, Nirwan Department of Electrical and Computer Engineering Advanced Networking Laboratory New Jersey Institute of Technology NewarkNJ07102 United States Department Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC28223 United States

Datacenters (DCs), deployed in a large scale to support the ever increasing demand for data processing applications, consume tremendous energy. Powering DCs with renewable energy can effectively reduce the brown energy consumption. Owing to geographically distributed deployment of DCs, the renewable energy generation and the data processing demands usually vary in different DCs. Migrating virtual machines (VMs) among DCs according to the availability of renewable energy helps match the energy demands and the renewable energy generation in DCs, and thus maximizes the utilization of renewable energy. We first elicit the renewable energy-aware inter-DC (inter-DC) VM migration problem in an inter-DC network over the elastic optical infrastructure, present it as a many-manycast communications problem, and then formulate it as an integer linear programming problem. The objective is to minimize the total cost of the brown energy consumption of DCs in such inter-DC network via VM migration. We use CVX and Gurobi to solve this problem for small network configurations, and we propose a few heuristic algorithms that approximate the optimal solution for large network configurations. Through extensive simulations, we show that the proposed algorithms, by migrating VM among DCs, can reduce up to 19.7% cost of the brown energy consumption. © 2017 IEEE.

关键词： Energy utilization

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