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Empowering over-the-air personalized federated learning via RIS

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Science China(Information Sciences) 2024年第11期67卷 371-372页

作者： Wei SHI Jiacheng YAO Jindan xu Wei xu lexi xu Chunming ZHAO National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories School of Electrical and Electronics Engineering Nanyang Technological University Research Institute China United Network Communications Corporation

Federated learning(FL) is a promising distributed learning approach due to its privacy-enhancing characteristic [1–3].To enhance communication efficiency of FL, over-the-air computation(AirComp) has emerged as a key technique by exploiting the waveform superposition property of multiple access channels [4, 5]. Although AirComp-enabled FL(AirFL) offers significant performance gains, it does not address the data heterogeneity in most real-life FL scenarios with non-independent and identically distributed local datasets.

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Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey

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China Communications 2022年第1期19卷 218-237页

作者： Kai Chen Qinglei Kong Yijue Dai Yue xu Feng Yin lexi xu Shuguang Cui School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China Future Network of Intelligence Institute(FNii) The Chinese University of Hong KongShenzhen 518172China School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China Research Institute China United Network Communications CorporationBeijing 100048China Alibaba Group Hangzhou 310052China

Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable future. In this paper, we review a promising family of nonparametric Bayesian machine learning models,i.e., Gaussian processes(GPs), and their applications in wireless communication. Since GP models demonstrate outstanding expressive and interpretable learning ability with uncertainty, they are particularly suitable for wireless communication. Moreover, they provide a natural framework for collaborating data and empirical models(DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GP models. Then, we present the background of the GPs in terms of covariance structure and model inference. The expressiveness of the GP model using various interpretable kernels, including stationary, non-stationary, deep and multi-task kernels,is showcased. Furthermore, we review the distributed GP models with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.

关键词： wireless communication Gaussian process machine learning kernel interpretability uncertainty

QoS-Aware Offloading Based on Communication-Computation Resource Coordination for 6G Edge Intelligence

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China Communications 2023年第3期20卷 236-251页

作者： Chaowei Wang Xiaofei Yu lexi xu Fan Jiang Weidong Wang Xinzhou Cheng School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and TelecommunicationsXi’an 710121China Research Institute China United Network Communications CorporationBeijing 100048China

Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge *** this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual *** we construct the system utility measuring QoS based on application latency and user devices’energy *** also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O *** results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.

关键词： mobile edge computing serial task of-floading computation partitioning 6G edge intelli-gence I/O interference

A Joint Power and Bandwidth Allocation Method Based on Deep Reinforcement Learning for V2V Communications in 5G

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China Communications 2021年第7期18卷 25-35页

作者： Xin Hu Sujie xu Libing Wang Yin Wang Zhijun Liu lexi xu You Li Weidong Wang School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China China Unicom Research Institute Beijing 100032China Department of Geomatics Engineering University of CalgaryAB T2N 1N4Canada

Vehicular communications have recently attracted great interest due to their potential to improve the intelligence of the transportation *** maintaining the high reliability and low latency in the vehicle-to-vehicle(V2V)links as well as large capacity in the vehicle-to-infrastructure(V2I)links,it is essential to flexibility allocate the radio resource to satisfy the different requirements in the V2V *** paper proposes a new radio resources allocation system for V2V communications based on the proximal strategy optimization *** this radio resources allocation framework,a vehicle or V2V link that is designed as an *** through interacting with the environment,it can learn the optimal policy based on the strategy gradient and make the decision to select the optimal sub-band and the transmitted power *** the proposed method can output continuous actions and multi-dimensional actions,it greatly reduces the implementation complexity of large-scale communication *** simulation results indicate that the allocation method proposed in this paper can meet the latency constraints and the requested capacity of V2V links under the premise of minimizing the interference to vehicle-to-infrastructure communications.

关键词： 5G V2V communication power allocation bandwidth allocation deep reinforcement learning

Collaborative Caching in Vehicular Edge Network Assisted by Cell-Free Massive MIMO

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Chinese Journal of Electronics 2023年第6期32卷 1218-1229页

作者： WANG Chaowei WANG Ziye xu lexi YU Xiaofei ZHANG Zhi WANG Weidong School of Electronic Engineering Beijing University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Research Institute China United Network Communications Corporation School of Information and Communication Engineering Beijing University of Posts and Telecommunications

The 6G mobile communications demand lower content delivery latency and higher quality of service for vehicular edge network. With the popularity of content-centric networks, mobile users are paying more and more attention to the delay and reliability of fetching cached content. For reducing communication costs, increasing network capacity and improving the content delivery, we propose a collaborative caching scheme based on deep reinforcement learning for vehicular edge network assisted by cell-free massive multiple-input multipleoutput(MIMO) system, in which the macro base station is considered as the central processor unit, and the roadside units are treated as roadside access points(RSAPs).The proposed scheme can effectively cache contents in edge nodes, i.e., RSAPs and vehicles with caching capability. We jointly consider the mobility of vehicles and the content request preferences of users, then we use deep Qnetworks algorithm to optimize the caching *** results show that the proposed scheme can significantly reduce the content delivery average latency and increase the content cache hit ratio.

关键词： Deep learning Simulation Collaboration Massive MIMO Reinforcement learning Quality of service MIMO

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泛在电力物联网大数据平台架构及应用探讨

邮电设计技术 2019年第9期 25-30页

作者：阳锐刘娜李俊珠徐乐西北京智芯微电子科技有限公司北京102200 亿阳信通股份有限公司北京100093 中国联通网络技术研究院北京100048

随着物联网、互联网+时代的到来,尤其是泛在电力物联网建设的推进,电网数据也呈现出海量、多样性等趋势,挖掘电网数据价值,助力电网服务水平的提升,拓展电网数据的创新应用,成为国网公司的核心引擎及新增长极。在介绍泛在电力物联网的... 详细信息

随着物联网、互联网+时代的到来,尤其是泛在电力物联网建设的推进,电网数据也呈现出海量、多样性等趋势,挖掘电网数据价值,助力电网服务水平的提升,拓展电网数据的创新应用,成为国网公司的核心引擎及新增长极。在介绍泛在电力物联网的基础上,阐述了电网数据的特征,并提出了泛在电力物联网大数据分析平台架构,探讨了关键技术,最后介绍了泛在电力物联网大数据的潜在应用。

关键词：泛在电力物联网电网大数据创新应用

基于双向中继的终端直通(D2D)干扰协调方案

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邮电设计技术 2015年第10期 20-24页

作者：贾玉玮程新洲晁昆徐乐西中国联通网络技术研究院北京100048

LTE-Advanced系统要求更高的数据吞吐率,由于蜂窝通信系统以基站为中心的小区覆盖和业务提供方式本身的局限性,LTE-Advanced系统仍存在覆盖和容量等方面的问题。基于双向中继的终端直通(D2D)技术在节省资源和减小干扰等多方面有巨大优... 详细信息

LTE-Advanced系统要求更高的数据吞吐率,由于蜂窝通信系统以基站为中心的小区覆盖和业务提供方式本身的局限性,LTE-Advanced系统仍存在覆盖和容量等方面的问题。基于双向中继的终端直通(D2D)技术在节省资源和减小干扰等多方面有巨大优势。给出了D2D方案的应用场景,并通过仿真验证了该方案能够有效地消除中继辅助的D2D通信场景用户间干扰,改善系统吞吐率,降低资源开销,提高系统频谱效率。

关键词： LTE-Advanced系统终端直传双向中继网络编码干扰协调

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大数据质量稽核的监控实施方法

邮电设计技术 2021年第11期 6-10页

作者：张恒曹丽娟程新洲徐乐西中国联通研究院北京100048

数据质量的好坏直接关系到信息准确度和业务有效性。只有解决数据质量问题并保证数据资产的科学性,才能有效反映企业运营和市场事实。科学有效的数据能够让企业经营决策可靠精确。大数据时代,数据将会呈爆炸式增长,海量的数据一方面为... 详细信息

数据质量的好坏直接关系到信息准确度和业务有效性。只有解决数据质量问题并保证数据资产的科学性,才能有效反映企业运营和市场事实。科学有效的数据能够让企业经营决策可靠精确。大数据时代,数据将会呈爆炸式增长,海量的数据一方面为运营商带来大量信息资产,另一方面无用数据、干扰数据也呈指数级增长。因此,围绕企业大数据的生命周期管理,实现数据的全过程质量监控非常重要。

关键词：大数据稽核规则配置质量评分

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VoLTE语音质量研究

邮电设计技术 2015年第10期 51-55页

作者：姜先贵李勇辉朱斌徐乐西中国联通网络技术研究院北京100048

近些年,OTT语音业务迅猛发展,新业务层出不穷,运营商也根据自身网络演进推出VoLTE语音业务,但VoLTE语音相比OTT语音是否更具有优势是大家关注的问题。首先介绍了VoLTE网络架构、VoLTE优化关键技术,并研究语音质量评估的方法以及影响语... 详细信息

近些年,OTT语音业务迅猛发展,新业务层出不穷,运营商也根据自身网络演进推出VoLTE语音业务,但VoLTE语音相比OTT语音是否更具有优势是大家关注的问题。首先介绍了VoLTE网络架构、VoLTE优化关键技术,并研究语音质量评估的方法以及影响语音的主要因素,然后通过实验数据分析VoLTE语音与OTT语音在MOS评分、呼叫建立时延、流量占用等方面的对比优势。

关键词： VoLTE IMS 语音质量 MOS