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Tie-line Power Adjustment Method Based on Proximal Policy Optimization Algorithm

Journal of Physics: Conference Series 2021年第1期1754卷

作者： Jinxiu Hou Zhihong Yu Qingping Zheng Huating Xu Shufang Li China Electric Power Research Institute Beijing 100192 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing 100876 China College of Electrical Engineering Zhejiang University Hangzhou 310027 China

The tie-line power adjustment is an essential part of the power system operation state calculation. Various existing algorithms to solve the tie-line power adjustment problem are mainly implemented by introducing tie-line power equation constraints into conventional power flow calculations. Such methods have low calculation efficiency, not enough automation, and are prone to non-convergence in the power flow calculation. In this paper, the tie-line power adjustment problem is formulated as a Markov decision process, and the proximal policy optimization algorithm is introduced to optimize the decision policy. In order to enhance the effectiveness of the proposed method, a new deep neural network structure suitable for the proximal policy optimization algorithm is designed. The proposed method is verified with the IEEE 39-bus system.

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Power Efficient Deployment of VLC-enabled UAVs

Power Efficient Deployment of VLC-enabled UAVs

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zhiyu Zhu Yang Yang Caili Guo Mingzhe Chen Shuguang Cui H. Vincent Poor Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Princeton University Princeton NJ USA The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9781728144900

ISBN: (纸本)9781728144917

In this paper, a power efficient deployment for visible light communication (VLC)-enabled unmanned aerial vehicles (UAVs) is studied. In the studied model, each UAV can provide communication service for ground users and illumination builds the VLC links between UAVs and users. Hence, each UAV's signal transmission and illumination will affect other UAVs' signal transmission and illumination. Therefore, to deploy VLC-enabled UAVs so as to efficiently service the ground users, the interference caused by the signal transmission and illumination of UAVs must be considered. This problem is formulated as an optimization problem whose goal is to optimize the deployment of UAVs so as to minimize the power consumption for signal transmission and illumination. An iterative algorithm is first proposed to transform the optimization problem into a series of interdependent subproblems, and the transformed problems are then solved by the Lagrangian dual method. In addition, convergence and complexity of the algorithm are also analyzed. Numerical results show that the proposed scheme can reduce at least 53.7% power consumption when compared to the baselines with UAVs at the center of each sub-area.

关键词： Lighting Optimization Light emitting diodes Unmanned aerial vehicles Power demand Optical transmitters convergence

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Topology construction of backbone network based on machine learning 4

Topology construction of backbone network based on machine l...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Chen, Qi Shou, Guochu Hu, Yihong Liu, Yaqiong Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781538683392

In this paper, we propose a topology construction scheme of backbone network. In this scheme, topology construction is abstracted to classification problem in machine learning area. We select/extract features based on network science theory and basic models of topology construction. In the experiments, we collect data set from actual backbone topology in China, compare and analyse several machine learning algorithms and study the influence of different parameters on topology construction. Results show that the proposed scheme has ideal applicability: The similarity between actual topology and the topology built by the scheme exceeds 98%. The feature extraction process is concise and there is no need to deliberately screen the data set. Among the algorithms involved in this paper, the Gradient Boosting Decision Tree (GBDT) and Random Forest algorithm have the most ideal performance. Our study shows that the topology construction in backbone network, which is basically dominated by engineering experience and influenced by many different factors, can be well simulated by machine learning algorithms, providing new ideas for the innovation of backbone network planning. © 2018 IEEE.

关键词： Decision trees

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Priority-based Content Dissemination in Content Centric Vehicular networks 2

Priority-based Content Dissemination in Content Centric Vehi...

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2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Khan, Irfanullah Zhang, Tiankui Xu, Xiaogeng Shan, Siyang Khan, Adil Ahmad, Shabeer Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9781538618035

In Content Centric Vehicular network (CCVN), content forwarding priority is a crucial issue in content dissemination scenario considering the content urgency. To guarantee the prompt and reliable delivery of the contents, a Priority-based Content Dissemination (PCD) scheme is designed for CCVN. In the proposed scheme, content provided to vehicles are broadly classified into safety and non-safety. Safety contents always have higher priority than non-safety contents. The priority of the safety and non-safety contents are assigned based on the content expiration time and the request volume respectively. Contents are propagated according to this priority through wireless communication. The workflow of the proposed PCD scheme is illustrated. The simulation results show that the proposed scheme can reduce the transmission delay of all safety contents and the popular non-safety contents. © 2018 IEEE.

关键词： Safety Reliability Broadcasting systems architecture Data models Vehicular ad hoc networks History

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Inductive Embedding Learning on Attributed Heterogeneous networks via Multi-task Sequence-to-Sequence Learning

Inductive Embedding Learning on Attributed Heterogeneous Net...

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IEEE International Conference on Data Mining (ICDM)

作者： Yunfei Chu Caili Guo Tongze He Yaqing Wang Jenq-Neng Hwang Chunyan Feng Beijing Key Laboratory of Network System Architecture and Convergence Bejing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Department of Electrical Engineering University of Washington Seatle USA

In the paper, we study the problem of inductive embedding learning on attributed heterogeneous networks, and propose a Multi-task sequence-to-sequence learning based Inductive network Embedding framework (MINE) capturing the attribute similarity, network proximity, and partial label information simultaneously. In particular, MINE trains an encoder function that aggregates information from a node's long-range scope of contexts, with the node attribute sequences generated by the proposed type-guided heterogeneous random walk as inputs. We present an one-to-many multi-task sequence-to-sequence model where the encoder is shared between two related tasks: an unsupervised node identity sequence generation task to learn context-aware embeddings, and a semi-supervised label prediction task to learn semantics-rich embeddings. Extensive experiments on real-world datasets demonstrate that the proposed method significantly outperforms several state-of-the-art methods.

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Inferring land use type in urban area with mobile big data 4

Inferring land use type in urban area with mobile big data

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Xing, Zhizhuang Lin, Wenhui Qiao, Yuanyuan Zhang, Hao Sun, Kewu Yang, Jie Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Technology Research Institute Aisino Corporation Beijing China

ISBN: (纸本)9781538683392

Research on the detection of land use has been a challenge for urban sensing and is of great significance to smart city construction. With the popularity of smart phones and the unprecedented growth of mobile phone data, there emerge numerous studies on land use detection with CDR (Call Detail Records), SMS (Short Messaging Service) and mobile traffic data. This is also a combination of urban sensing based on mobile big data and smart city construction. In this paper, we propose a methodology based on existing studies on land use detection with CDR, text data, and mobile traffic data. We propose a new traffic signature that combines the advantages of the above studies and apply it to a real-world dataset collected in northeast of China. We choose the POI (point of interest) data as ground truth data that the obtained land use types are compared with. We find that our method can accurately detect the POI collections of specific types, which proves the effectiveness of our method. After extracting base components of land use types, an NMF (non-negative matrix factorization) based method is applied to explore the mix of land use types in urban areas. Then, we provide reasonable explanations for different areas with mixed land use types. The result of proposed method is evaluated from both components perspective and urban area perspective. © 2018 IEEE.

关键词： Big data

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Polarization-based Cross-tier Interference Alignment in Cognitive Heterogeneous Cellular network 15

Polarization-based Cross-tier Interference Alignment in Cogn...

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15th International Symposium on Wireless Communication systems, ISWCS 2018

作者： Gao, Xiaofang Guo, Caili Chen, Shuo Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781538650059

To eliminate the cross-tier interference and optimize the bit-error-rate performance of small cell for downlink underlay cognitive heterogeneous cellular network, a novel polarization-based cross-tier interference alignment scheme is proposed in this paper by exploiting the polarization resource of two-tier network. The transmitting and receiving polarization states of small cell are designed by interference alignment-like behaviors of the minimum total mean squared error optimization model under interference power constraint. An analytical and noncooperative closed form solution is provided for the model. Simulation results show the proposed scheme outperforms the existing schemes in bit-error-rate performance of small cell while keeping the maximum sum rate of overall system. And the tradeoff between bit-error-rate performances of macro cell and small cell is also evaluated. © 2018 IEEE.

关键词： Polarization

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Improving Simulation of Lognormal Sum Distributions With Hyperspace Replication

Improving Simulation of Lognormal Sum Distributions With Hyp...

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IEEE Global Communications Conference

作者： Norman C. Beaulieu Gan Luan School of Information and Communication Engineering the Beijing Key Laboratory for Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Simulation is widely used to estimate the distributions of sums of lognormal random variables because the characteristic function of the lognormal random variable is not known and numerical integration of multiple integrals is difficult. The left tail of the lognormal sum distribution is the outage probability for some wireless systems, while the right tail represents cochannel interference, both crucial design issues. Importance sampling is sometimes used but becomes less effective as the number of summands increases. A novel simulation procedure dubbed hyperspace replication is proposed and studied. The method achieves estimator variance reductions greater than 64 times in the left tail for practical values of outage probability, and 2 to 3 times in the right tail compared to traditional Monte Carlo simulation. The method can be used in cascade with importance sampling and reduces the variances of the distribution estimates by the same amounts as it does for Monte Carlo simulation.

关键词： Random variables Monte Carlo methods Power system reliability Probability distribution Probability density function Distribution functions

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Location-Aware Hypergraph Coloring Based Spectrum Allocation for D2D Communication 15

Location-Aware Hypergraph Coloring Based Spectrum Allocation...

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15th International Symposium on Wireless Communication systems, ISWCS 2018

作者： Li, Zheng Guo, Caili Chen, Shuo Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781538650059

Device-to-device (D2D) communication underlaying cellular networks has been recognized as a promising technique to improve spectrum efficiency. However, densely deployed D2D pairs could cause severe interference to cellular user equipments (CUEs) without proper spectrum allocation. Since the CUEs are the primary users of the spectrum band, the quality of service(QoS) of CUEs should be guaranteed. Therefore, the location-aware hypergraph coloring (LAHC) based spectrum allocation algorithm is proposed in uplink D2D underlaying cellular networks. For a particular CUE, the entire cell area is divided into location-aware regions. A set of D2D pairs identified by these regions are limited when sharing spectrum with the CUE to protect the CUE from connection interruption. In addition, we study spectrum allocation using hypergraph coloring method to eliminate cumulative interference from multiple D2D pairs. Simulation indicates that the proposed spectrum allocation algorithm can both guarantee that D2D links will not cause service disruption to CUEs and significantly improve the system capacity. © 2018 IEEE.

关键词： Spectrum efficiency

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Power efficient visible light communication (VLC) with unmanned aerial vehicles (UAVs)

arXiv

引用

arXiv 2019年

作者： Yang, Yang Chen, Mingzhe Guo, Caili Feng, Chunyan Saad, Walid Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Virginia Polytechnic Institute and State University Blacksburg United States

A novel approach that combines visible light communication (VLC) with unmanned aerial vehicles (UAVs) to simultaneously provide flexible communication and illumination is proposed. To minimize the power consumption, the locations of UAVs and the cell associations are optimized under illumination and communication constraints. An efficient sub-optimal solution that divides the original problem into two sub-problems is proposed. The first sub-problem is modeled as a classical smallest enclosing disk problem to obtain the optimal locations of UAVs, given the cell association. Then, assuming fixed UAV locations, the second sub-problem is modeled as a min-size clustering problem to obtain the optimized cell association. In addition, the obtained UAV locations and cell associations are iteratively optimized multiple times to reduce the power consumption. Numerical results show that the proposed approach can reduce the total transmit power consumption by at least 53.8% compared to two baseline algorithms with fixed UAV locations. Copyright © 2019, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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