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Cloud-Assisted Distributed Edge Brains for Multi-Cell Joint Beamforming Optimization for 6G

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China Communications 2022年第3期19卷 36-49页

作者： Juan Deng Kaicong Tian Qingbi Zheng Jielin Bai Kuo Cui Yitong liu Guangyi LIU Beijing University of Posts and Telecommunications Beijing 100876China Future Research Lab China Mobile Research InstituteBeijing 100032China

In 5G networks,optimization of antenna beam weights of base stations has become the key application of AI for network optimization.For 6G,higher frequency bands and much denser cells are expected,and the importance of automatic and accurate beamforming assisted by AI will become more prominent.In existing network,servers are“patched”to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead,large inference latency and potential risks of data security.Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response,but it is inefficient in collecting and sharing global information among base stations.In this paper,we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization.We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation.Compared with the centralized solution,our solution improves prediction accuracy by 24.66%,and reduces storage cost by 83.82%.Compared with the decentralized solution,our solution improves prediction accuracy by 68.26%,and improves coverage performance by 0.4 dB.At last,the future research work is prospected.

关键词： artificial intelligence collaborative cloud edge centralized cloud brain decentralized edge brain 6G mobile communication

Deep Learning in Power Systems Research:A Review

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 209-220页

作者： Mahdi Khodayar Guangyi LIU Jianhui Wang Mohammad E.Khodayar Global Energy Interconnection Research Institute North America(GEIRI North America) San JoseCAUSA Department of Computer Science at the University of Tulsa TulsaOKUSA Khodayar are with the Department of Electrical and Computer Engineering of Southern Methodist

With the rapid growth of power systems measurements in terms of size and complexity,discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction,demand response,energy disaggregation,and state estimation is considered a crucial challenge.In recent years,deep learning has emerged as a novel class of machine learning algorithms that represents power systems data via a large hypothesis space that leads to the state-of-the-art performance compared to most recent data-driven algorithms.This study explores the theoretical advantages of deep representation learning in power systems research.We review deep learning methodologies presented and applied in a wide range of supervised,unsupervised,and semi-supervised applications as well as reinforcement learning tasks.We discuss various settings of problems solved by discriminative deep models including stacked autoencoders and convolutional neural networks as well as generative deep architectures such as deep belief networks and variational autoencoders.The theoretical and experimental analysis of deep neural networks in this study motivates longterm research on optimizing this cutting-edge class of models to achieve significant improvements in the future power systems research.

关键词： Autoencoder convolution neural network deep learning discriminative model deep belief network generative architecture variational inference

Environment Information-Based Channel Prediction Method Assisted by Graph Neural Network

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China Communications 2022年第11期19卷 1-15页

作者： Yutong Sun Jianhua Zhang Yuxiang Zhang Li Yu Qixing Wang Guangyi LIU State Key Lab of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Future Research Laboratory China Mobile Research InstituteBeijing 100053China

Recently,whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless channel.This paper presents an environment information-based channel prediction(EICP)method for connecting the environment with the channel assisted by the graph neural networks(GNN).Firstly,the effective scatterers(ESs)producing paths and the primary scatterers(PSs)generating single propagation paths are detected by building the scatterercentered communication environment graphs(SCCEGs),which can simultaneously preserve the structure information and highlight the pending scatterer.The GNN-based classification model is implemented to distinguish ESs and PSs from other scatterers.Secondly,large-scale parameters(LSP)and small-scale parameters(SSP)are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and PSs.Simulation results show that the average normalized mean squared error(NMSE)of LSP and SSP predictions are 0.12 and 0.008,which outperforms the methods of linear data learning.

关键词： channel prediction propagation environment graph scatterer detection GNN

Dynamic user-centric multi-dimensional resource allocation for a wide-area coverage signaling cell based on DQN

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Frontiers of Information Technology & Electronic Engineering 2023年第1期24卷 154-163页

作者： Zhou TONG Na LI Huimin ZHANG Quan ZHAO Yun ZHAO Junshuai SUN Guangyi LIU Future Research Lab China Mobile Research InstituteBeijing 100053China

The rapid development of communications industry has spawned more new services and applications.The sixth-generation wireless communication system(6G)network is faced with more stringent and diverse requirements.While ensuring performance requirements,such as high data rate and low latency,the problem of high energy consumption in the fifth-generation wireless communication system(5G)network has also become one of the problems to be solved in 6G.The wide-area coverage signaling cell technology conforms to the future development trend of radio access networks,and has the advantages of reducing network energy consumption and improving resource utilization.In wide-area coverage signaling cells,on-demand multi-dimensional resource allocation is an important technical means to ensure the ultimate performance requirements of users,and its effect will affect the efficiency of network resource utilization.This paper constructs a user-centric dynamic allocation model of wireless resources,and proposes a deep Q-network based dynamic resource allocation algorithm.The algorithm can realize dynamic and flexible admission control and multi-dimensional resource allocation in wide-area coverage signaling cells according to the data rate and latency demands of users.According to the simulation results,the proposed algorithm can effectively improve the average user experience on a long time scale,and ensure network users a high data rate and low energy consumption.

关键词： 6G Wide-area coverage signaling cell Multi-dimensional resource allocation Deep Q-network(DQN)

Service Scheduling Based on Edge Computing for Power Distribution IoT

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Computers, Materials & Continua 2020年第3期62卷 1351-1364页

作者： Zhu liu Xuesong Qiu Shuai Zhang Siyang Deng Guangyi LIU State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China State Grid Information&Telecommunication Group Co. Ltd.Beijing102211China Global Energy Interconnection Research Institute North America San JoseCA95134USA

With the growing amounts of multi-micro grids,electric vehicles,smart home,smart cities connected to the Power Distribution Internet of Things(PD-IoT)system,greater computing resource and communication bandwidth are required for power distribution.It probably leads to extreme service delay and data congestion when a large number of data and business occur in emergence.This paper presents a service scheduling method based on edge computing to balance the business load of PD-IoT.The architecture,components and functional requirements of the PD-IoT with edge computing platform are proposed.Then,the structure of the service scheduling system is presented.Further,a novel load balancing strategy and ant colony algorithm are investigated in the service scheduling method.The validity of the method is evaluated by simulation tests.Results indicate that the mean load balancing ratio is reduced by 99.16%and the optimized offloading links can be acquired within 1.8 iterations.Computing load of the nodes in edge computing platform can be effectively balanced through the service scheduling.

关键词： PD-IoT edge computing service scheduling load balancing strategy ant colony algorithm

The SOLIDS 6G Mobile Network Architecture:Driving Forces,Features,and Functional Topology

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Engineering 2022年第1期8卷 42-59页

作者： Guangyi LIU Na Li Juan Deng Yingying Wang Junshuai Sun Yuhong Huang China Mobile Research Institute Beijing 100053China

With the large-scale commercial launch of fifth generation(5G)mobile network,the development of new services and applications catering to the year 2030,along with the deep convergence of information,communication,and data technologies(ICDT),and the lessons and experiences from 5G practice will drive the evolution of the next generation of mobile networks.This article surveys the history and driving forces of the evolution of the mobile network architecture and proposes a logical function architecture for sixth generation(6G)mobile network.The proposed 6G network architecture is termed SOLIDS(related to the following basic features:soft,on-demand fulfillment,lite,native intelligence,digital twin,and native security),which can support self-generation,self-healing,self-evolution,and self-immunity without human involvement and address the primary issues in the legacy 5G network(e.g.,high cost,high power consumption,and highly complicated operation and maintenance),significantly well.

关键词： Sixth generation Network features Network architecture

Data-driven and Model-based Hybrid Reinforcement Learning to Reduce Stress on Power Systems Branches

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 433-442页

作者： Mariana Kamel Renchang Dai Yawei Wang Fangxing Li Guangyi LIU The University of Tennessee.Knoxville TNUSA Puget Sound Energy BellevueWAUSA PayPal San JoseCAUSA Envision Digital.Redwood City CAUSA

This work proposes a reinforcement learning(RL)approach to tackle the control problem of branch overload relief in large power systems.Accordingly,a control agent is trained to change generators'real power output in order to relieve the stressed branches.For large power systems,this control problem becomes one whose decision space(i.e.,the action space)is both highly-dimensioned and continuous.This makes it extremely difficult to have successful training for RL-based agents.To improve the effectiveness,a data-driven and model-based hybrid approach is proposed to optimize the control by combining RL-agent actions and generator shifting factor-driven actions.Accordingly,with the proposed approach the RL-agent successfully trains on large power systems.The proposed design is tested on both the IEEE 118-bus testing system and a 2749-bus real system.The obtained results show that the proposed hybrid approach outperforms the data-driven training approach.

关键词： Deep deterministic policy gradient generation re-dispatch hybrid learning overload relief reinforcement learning

A comparative investigation into floatability of bastnaesite with three di/trialkyl phosphate surfactants

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Journal of Rare Earths 2021年第11期39卷 1442-1449,I0005页

作者： Hongli Fan Xianglin Yang Jing Qi Guangyi LIU Jingqin Qin College of Chemistry and Chemical Engineering Central South UniversityChangsha 410083China

Flotation separation and recovery of rare earth minerals(REM) have returned to an important position due to the growing strategy demand for rare earth elements(REE).In this paper,a comparative investigation into the floatability of bastnaesite((Ce,La)FCO3) was conducted by using three di/trialkyl phosphate collectors,di(2-ethylhexyl) phosphate(DEHPA),dibutyl phosphate(DBP) and tributyl phosphate(TBP).The density functional theory(DFT) computation recommends that the chemical activity of the three phosphate collectors is in order of DEHPA≥ DBP >> TBP,and their hydrophobization assuggested by the IgP(oil-water partition coefficient) value is in the order of DEHPA> TBP> DBP.The micro-flotation indicates that the preferable pH values for flotation of bastnaesite with the three phosphate collectors are 7.0-8.0,and DEHPA achieves much higher flotation recovery of bastnaesite,followed by DBP,and then TBP,which coincides with their reactivity and hydrophobicity,the two prerequisites for froth flotation.The contact angle,zeta potential,Fourier transform infrared spectroscopy(FTIR) and X-ray photoelectron spectroscopy(XPS) deduce that DEHPA likely reacts with the Ce(Ⅲ)atoms of bastnaesite interface through its O atom(s) of the P(=O)-O-group to generate the Ce-O-P bonds,and its two 2-ethylhexyl groups orient outside for attaching bubbles,resulting in flotation enrichment of bastnaesite.Furthermore,this investigation offers a novel strategy for developing collectors in selective beneficiation of REM.

关键词： di(2-ethylhexyl)phosphoric acid Bastnaesite Flotation Mechanism Activity Rare earths

Graph Computing Based Security Constrained Unit Commitment in Hydro-thermal Power Systems Incorporating Pumped Hydro Storage

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 485-496页

作者： Longfei Wei Guangyi LIU Shen Yan Renchang Dai Yachen Tang Hitachi ABB Power Grids San JoseCAUSA School of Electrical Engineering Xi'an University of TechnologyXi'anChina Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-ChampaignChampaignILUSA Puget Sound Energy BellevueWAUSA Envision Digital Inc. Redwood CityCAUSA

This paper proposes a graph computing based mixed integer programming(MIP)framework for solving the security constrained unit commitment(SCUC)problem in hydro-thermal power systems incorporating pumped hydro storage(PHS).The proposed graph computing-based MIP framework considers the economic operations of thermal units,cascade hydropower stations and PHS stations,as well as their technical impacts towards the network security.First,the hydro-thermal power system data and unit information are stored in a graph structure with nodes and edges,which enables nodal and hierarchical parallel computing for the unit commitment(UC)solution calculation and network security analysis.A MIP model is then formulated to solve the SCUC problem with the mathematical models of thermal units,cascade hydropower stations and PHS stations.In addition,two optimization approaches including convex hull reformulation(CHR)and special ordered set(SOS)methods are introduced for speeding up the MIP calculation procedure.To ensure the system stability under the derived UC solution,a parallelized graph power flow(PGPF)algorithm is proposed for the hydro-thermal power system network security analysis.Finally,case studies of the IEEE 118-bus system and a practical 2749-bus hydro-thermal power system are introduced to demonstrate the feasibility and validity of the proposed graph computing-based MIP framework.

关键词： Graph computing hydro-thermal system mixed integer programming network security unit commitment