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Joint Scheduling and Resource Allocation for Federated Learning in SWIPT-Enabled Micro UAV Swarm Networks

China Communications 2022年第1期19卷 119-135页

作者： WanliWen yunjian jia Wenchao Xia School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210009China jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China

Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.

关键词： micro unmanned aerial vehicle federated learning simultaneous wireless information and power transfer scheduling resource allocation

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Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model

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Frontiers of Information Technology & Electronic Engineering 2023年第12期24卷 1763-1775页

作者： Yonghua QUAN Zhong TIAN Zhengchuan CHEN Min WANG yunjian jia School of Microelectronics and Communication Engineering Chongqing UniversityChongqing400044China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts&TelecommunicationsXi’an710121China School of Optoelectronic Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic TechnologyGuilin541004China

Reconfigurable intelligent surfaces(RISs)have the capability to change the wireless environment smartly Considering the attenuation of subchannels and crowding users involved in the wideband system,we introduce RISs into the multi-user multi-input single-output(MU-MISO)system with orthogonal frequency division multiplexing(OFDM)for performance enhancement.Maximizing the minimum rate of dense users in an MU-MISO-OFDM system assisted by RIS with an approximate practical model is formulated as the joint optimization problem involving subcarrier allocation,transmit precoding(TPC)matrices at the base station,and RIS passive beamforming.A coalition-game subcarrier allocation(CSA)algorithm is proposed to solve space–frequency resource allocation on subcarriers,which reforms the interference topology among dense users.Fractional programming and convex optimization method are used to optimize the TPC matrices and the RIS passive beamforming,which improves the spectral efficiency synthetically across all subchannels in the wideband system.Simulation results indicate that the CSA algorithm provides a significant gain for dense users.Besides,the proposed joint optimization method shows the considerable advantage of the RISs in the MU-MISO-OFDM system.

关键词： Reconfigurable intelligent surface Max-min rate Coalition-game subcarrier allocation

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Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

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数字通信与网络:英文版 2023年第4期9卷 971-980页

作者： Tianci Zhang Junjie Zhou Zhengchuan Chen Zhong Tian Wanli Wen yunjian jia School of Microelectronics and Communication Engineering Chongqing UniversityChongqing400044China National Mobile Communications Research Laboratory Southeast UniversityNanjingChina

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

关键词： Internet of things Information freshness Age of information Multi-source M/M/1 queuing model

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Joint Optimization of Task Caching,Computation Offloading and Resource Allocation for Mobile Edge Computing

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China Communications 2022年第12期19卷 142-159页

作者： Zhixiong Chen Zhengchuan Chen Zhi Ren Liang Liang Wanli Wen yunjian jia School of Microelectronics and Communication Engineering Chongqing UniversityChongqing400044 China Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion Chongqing400044 China National Mobile Communications Research Laboratory Southeast UniversityNanjing210096 China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065 China

Applications with sensitive delay and sizeable data volumes,such as interactive gaming and augmented reality,have become popular in recent years.These applications pose a huge challenge for mobile users with limited resources.Computation offloading is a mainstream technique to reduce execution delay and save energy for mobile users.However,computation offloading requires communication between mobile users and mobile edge computing(MEC) servers.Such a mechanism would difficultly meet users’ demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for users.Caching task data can effectively reduce the data transmissions when users offload their tasks to the MEC server.The limited caching space at the MEC server calls for judiciously decide which tasks should be cached.Motivated by this,we consider the joint optimization of computation offloading and task caching in a cellular network.In particular,it allows users to proactively cache or offload their tasks at the MEC server.The objective of this paper is to minimize the system cost,which is defined as the weighted sum of task execution delay and energy consumption for all users.Aiming at establishing optimal performance bound for the system design,we formulate an optimization problem by jointly optimizing the task caching,computation offloading,and resource allocation.The problem is a challenging mixed-integer non-linear programming problem and is NP-hard in general.To solve it efficiently,by using convex optimization,Karmarkar ’s algorithm and the proposed fast search algorithm,we obtain an optimal solution of the formulated problem with manageable computational complexity.Extensive simulation results show that in comparison to some representative benchmark methods,the proposed solution can effectively reduce the system cost.

关键词： mobile edge computing computation offloading caching resource allocation

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Joint association and beamforming optimization in reconfigurable intelligent surface-enhanced user-centric networks

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Intelligent and Converged Networks 2022年第4期3卷 340-350页

作者： Ye Yao Zhong Tian Zhengchuan Chen Min Wang yunjian jia School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China School of Optoelectronics Engineering Chongqing University of Posts and TelecommunicationsChongqing 400044China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

Fully coordinated Cell-Free(CF)networks can alleviate the Inter-Cell Interference(ICI)for the cell-edge users in cellular networks.Due to the complex topology of the association between the Access Points(APs)and the users in CF networks,it is challenging to deploy CF networks in practical scenarios.In order to make CF networks feasible,we introduce User-Centric(UC)networks enabling each user served by a limited number of APs.As a low-cost and energy-efficient technology,Reconfigurable Intelligent Surface(RIS)can be embedded in UC networks to further improve the system performance.First,we provide a brief survey on the prior works in UC networks for clear comprehension.Then,we formulate a Spectral Efficiency(SE)maximization problem for RIS-enhanced UC networks.For solving the non-convex problem,we divide it into three subproblems and propose a joint optimization framework for optimizing AP-user association,active beamforming of multiple antennas at the APs,and the passive beamforming of the RIS.Besides,a channel gain based association method coupled with the design of RIS is proposed to construct a dynamic and efficient association.The subproblems about optimizing active and passive beamforming are solved with the fractional programming.Simulation results show that the proposed joint optimization framework for RIS-enhanced UC networks can obtain good SE compared with other benchmark schemes.

关键词： User-Centric(UC)networks Reconfigurable Intelligent Surface(RIS) association optimization

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Efficient Task Completion for Parallel Offloading in Vehicular Fog Computing

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China Communications 2019年第11期16卷 42-55页

作者： Jindou Xie yunjian jia Zhengchuan Chen Zhaojun Nan Liang Liang Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion School of Microelectronics and Communication Engineering Chongqing University

In this paper,we investigate vehicular fog computing system and develop an effective parallel offloading scheme.The service time,that addresses task offloading delay,task decomposition and handover cost,is adopted as the metric of offloading performance.We propose an available resource-aware based parallel offloading scheme,which decides target fog nodes by RSU for computation offloading jointly considering effect of vehicles mobility and time-varying computation capability.Based on Hidden Markov model and Markov chain theories,proposed scheme effectively handles the imperfect system state information for fog nodes selection by jointly achieving mobility awareness and computation perception.Simulation results are presented to corroborate the theoretical analysis and validate the effectiveness of the proposed algorithm.

关键词： parallel offloading vehicular fog computing task offloading HMM

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中日风景园林本科教育比较分析

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华中建筑 2024年第1期42卷 149-152页

作者：禹文东陶欣杜佳吴涛罗云建扬州大学园艺园林学院扬州大学植物保护学院

要风景园林一级学科被取消,对行业教育产生了重大影响。风景园林行业的未来取决于风景园林教育的发展,如何提升风景园林专业的教育质量,实现风景园林专业教育的可持续发展是目前亟需解决的重要课题。借鉴国外专业办学中的先进经验,扬长... 详细信息

要风景园林一级学科被取消,对行业教育产生了重大影响。风景园林行业的未来取决于风景园林教育的发展,如何提升风景园林专业的教育质量,实现风景园林专业教育的可持续发展是目前亟需解决的重要课题。借鉴国外专业办学中的先进经验,扬长避短、开拓创新是解决问题的有效途径。日本古典园林的发展深受中国园林的影响,但日本现代园林却形成了自己的风格和特色,值得我们学习和借鉴。选取中国扬州大学和日本千叶大学的风景园林本科专业教育进行比较,取长补短,对我国风景园林本科教育的发展提出建议。

关键词：风景园林本科教育比较分析

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Data-Driven User Complaint Prediction for Mobile Access Networks

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Journal of Communications and Information Networks 2018年第3期3卷 9-19页

作者： Huimin Pan Sheng Zhou yunjian jia Zhisheng Niu Meng Zheng Lu Geng Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Communication Engineering Chongqing UniversityChongqing 400044China Hitachi(China)Research&Development Cooperation Beijing 100190China

In this paper,we present a user-complaint prediction system for mobile access networks based on network monitoring data.By applying machine-learning models,the proposed system can relate user complaints to network performance indicators,alarm reports in a data-driven fashion,and predict the complaint events in a fine-grained spatial area within a specific time window.The proposed system harnesses several special designs to deal with the specialty in complaint prediction;complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events.A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations.Furthermore,we combine up-sampling with down-sampling to combat the severe skewness towards negative samples.The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator,in which,events due to complaint bursts account approximately for only 0:3%of all recorded events.Re-sults show that our system can detect 30%of complaint bursts 3 h ahead with more than 80%precision.This will achieve a corresponding proportion of quality of experi-ence improvement if all predicted complaint events can be handled in advance through proper network maintenance.

关键词： data-driven complaint prediction complaint location network management machine learning pipeline

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一种分布式网络环境下基于挑战-响应模型的可信评估方法

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电子与信息学报 2023年第2期45卷 600-607页

作者：梁靓张镨丹武彦飞贾云健重庆大学微电子与通信工程学院重庆400044

运用信任模型进行可信评估是解决分布式网络安全问题的重要手段。然而,目前大部分研究工作把研究重点放在如何收集更完整的信任证据,以及如何利用一些新手段如机器学习、区块链等评估节点信任值,很少对如何获取节点可靠的初始信任值进... 详细信息

运用信任模型进行可信评估是解决分布式网络安全问题的重要手段。然而,目前大部分研究工作把研究重点放在如何收集更完整的信任证据,以及如何利用一些新手段如机器学习、区块链等评估节点信任值,很少对如何获取节点可靠的初始信任值进行研究。实际上,针对分布式网络提出的很多信任模型都依赖于历史信任证据,而初次对网络进行可信评估时并不具备相关历史信息。基于此,该文面向分布式网络环境的安全问题,提出了基于挑战-响应模型的可信评估方法。首先利用挑战-响应模型获取节点可靠的初始信任值,并利用此初始信任值对网络中的节点进行分簇,在簇内进行信任值计算和信任值更新,完成分布式网络环境下完整的可信评估流程。仿真结果表明,相较于统一设置初始信任值的方式,该文所提方法能对恶意节点、自私节点的信任值有较准确的预测,同时对恶意节点的检测率也更高。

关键词：可信评估分布式网络挑战-响应模型初始信任值

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基于主从博弈的分层联邦学习激励机制研究

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电子与信息学报 2023年第4期45卷 1366-1373页

作者：贾云健黄宇梁靓万杨亮周继华重庆大学微电子与通信工程学院重庆400044 95696部队重庆400030 重庆金美通信有限责任公司复杂环境通信重庆市重点实验室重庆400030

为了优化分层联邦学习(FL)全局模型的训练时延,针对实际场景中终端设备存在自私性的问题,该文提出一种基于博弈论的激励机制。在激励预算有限的条件下,得到了终端设备和边缘服务器之间的均衡解和最小的边缘模型训练时延。考虑终端设备... 详细信息

为了优化分层联邦学习(FL)全局模型的训练时延,针对实际场景中终端设备存在自私性的问题,该文提出一种基于博弈论的激励机制。在激励预算有限的条件下,得到了终端设备和边缘服务器之间的均衡解和最小的边缘模型训练时延。考虑终端设备数量不同,设计了基于主从博弈的可变激励训练加速算法,使得一次全局模型训练时延达到最小。仿真结果显示,所提出的算法能够有效降低终端设备自私性带来的影响,提高分层联邦学习全局模型的训练速度。

关键词：分层联邦学习博弈论激励机制

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