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arXiv 2020年

作者： Liu, Jing Shou, Guochu Wang, Qingtian Liu, Yaqiong Hu, Yihong Guo, Zhigang Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communications Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ervice function chaining (SFC) is promising to implement flexible and scalable virtual network infrastructure for the Internet of Things (IoT). Edge computing is envisioned to be an effective solution to process huge amount of IoT application data. In order to uniformly provide services to IoT applications among the distributed edge computing nodes (ECNs), we present a unified SFC orchestration framework based on the coordination of SDN and NFV, which provides a synergic edge cloud platform by exploiting the connectivity of FiWi access networks. In addition, we study the VNF deployment problem under our synergic framework, and we formulate it as a mixed-integer nonlinear programming (MINLP) problem jointly considering the load balancing of networking and computing for chaining VNFs. We also propose two approximation optimal deployment algorithms named Greedy-Bisection Multi-Path (GBMP) and KSP Multi-Path (KSMP) taking advantage of the multi-instance virtual network functions (VNFs) deployed in ECNs and the multipath capacity in FiWi access networks. Extensive simulations are conducted in two types of IoT application scenarios in the EC over FiWi access networks. The numerical results show that our proposed algorithms are superior to single path and ECMP based deployment algorithms in terms of load balancing, service acceptance ratio, and network utilization in both two typical scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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Reinforcement Learning for Task Offloading in Mobile Edge Computing for SDN based Wireless networks

Reinforcement Learning for Task Offloading in Mobile Edge Co...

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International Conference on Software Defined systems (SDS)

作者： Nahida Kiran Chunyu Pan Yin Changchuan Beijing Key Laboratory of Network Systeiii Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728172194

ISBN: (纸本)9781728172200

The explosive growth of the distributed computing resources in mobile edge computing (MEC) create a necessity to have a reasonable controller to ensure efficient utilization of distributed computing resources. The software defined networks (SDNs) capabilities can be used in MEC environment to reduce the energy consumption of user device without service disruptions. SDN, which is not proposed specifically for edge computing, can in fact serve as an enabler to let the real potential of edge computing be achieved and to lower the complexity barriers involved. This paper investigates resource allocation problem in wireless MEC by aiming to save the battery power of user device. Learning from experience plays an important role in a SDN based MEC infrastructure where reinforcement learning (RL) takes a long term goal into consideration besides immediate reward, which is very important for a dynamic environment. A novel software defined edge cloudlet (SDEC) based RL optimization framework is proposed in this paper to tackle the energy minimization problem in wireless MEC. Specifically, Q-learning and cooperative Q-learning based RL schemes are proposed for the intractable problem. Simulation results reveal that the proposed scheme achieves superior performance in saving the battery power of a user device compared to other benchmark methods such as Q-learning with a random algorithm and Q-learning with epsilon greedy.

关键词： Task analysis Cloud computing Edge computing Energy consumption Servers Software Resource management

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D2D-enabled Multicast Optimal Scheduling in mmWave Cellular networks

D2D-enabled Multicast Optimal Scheduling in mmWave Cellular ...

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IEEE International Conference on Communications in China (ICCC)

作者： Songling Zhang Danpu Liu Jie Lv Zhilong Zhang Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing P. R. China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

MmWave communications have been considered as the key enablers for future mobile cellular networks. The narrow beam communication of the mmWave large-scale antenna system suppresses co-channel interference and has greater potential for parallel communication and spatial multiplexing. Meanwhile, the introduction of multicast via point-to-multipoint communications further improves the spectrum efficiency for mmWave small cells. In order to reduce energy consumption, concurrent transmission and device-to-device (D2D)-enabled multicast has attracted great interests of many researchers, and some heuristic solutions have been provided. In this paper, we propose an optimal D2D-enabled multicast scheduling policy (OSP) aiming to minimize energy consumption in mmWave cellular networks. The optimal scheduling strategy is obtained by formulating the joint optimization of D2D pairing, parallel link selection and time slot allocation into an integer linear program (ILP) problem. Specifically, the concurrent transmissions which globally maximizes the spatial sharing gain while consuming the least energy at each time slot are identified by solving the ILP problem. Extensive simulations results demonstrate that our proposed algorithm reduces energy consumption by more than 18% compared with the baselines in all cases.

关键词： Cellular networks Energy consumption Simulation Optimal scheduling Scheduling Device-to-device communication Resource management

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Joint LED selection and precoding optimization for multiple-user multiple-cell VLC systems

arXiv

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arXiv 2021年

作者： Yang, Yang Yang, Yujie Chen, Mingzhe Feng, Chunyan Xia, Hailun Cui, Shuguang Poor, H. Vincent The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The Chinese University of Hong Kong Shenzhen518172 China

This paper proposes a hybrid dimming scheme based on joint LED selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLC) systems. In TASP-HD, both the LED selection and the precoding of each cell can be dynamically adjusted to reduce the intra- and inter-cell interferences while satisfying illumination constraints. First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated. In this studied problem, the indices of activated LEDs and precoding matrices are optimized, which result in a complex non-convex mixed integer problem. To solve this problem, the original problem is separated into two subproblems. The first subproblem, which maximizes the sum-rate of users via optimizing the LED selection with a given precoding matrix, is a mixed integer problem solved by the penalty method. With the optimized LED selection matrix, the second subproblem which focuses on the maximization of the sum-rate via optimizing the precoding matrix is solved by the Lagrangian dual method. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario under a dimming level of 70%, the mean bandwidth efficiency of TASPHD is 4.8 bit/s/Hz and 7.13 bit/s/Hz greater than AD and DD, respectively. © 2021, CC BY-SA.

关键词： Light emitting diodes

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A Fast Global Adaptive Solution to Low-light Images Enhancement in Visual SLAM

A Fast Global Adaptive Solution to Low-light Images Enhancem...

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International Conference on Computer and Communications (ICCC)

作者： Weishao Cheng Yanting Zhang Yonggang Qi Jun Liu Fang Liu Beijing Laboratory of Advanced Information Networks and Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Donghua University Shanghai China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Simultaneous Localization and Mapping (SLAM) is considered as an essential basis for autonomous robots and vehicles. Visual SLAM applies visual sensors to collect information and estimate the motion of the sensor in an unknown environment. However, it relies heavily on feature matching within images, so that the quality of the images has a great effect on the efficiency. In a low light intensity environment, the performance of motion estimation declines seriously. This paper presents a global adaptive solution to compensate the light condition and applies this solution to motion estimation in ORB-SLAM as an optimization module. key of the solution is to obtain global adaptive brightness gain for image intensifiers. The performance of the image intensifier is also evaluated in this paper as well as the proof of higher estimation accuracy. The result of the strengthened ORB- SLAM on the TUM benchmark shows that there is a significant performance boost especially in low luminance environment.

关键词： Visualization Simultaneous localization and mapping Motion estimation Brightness Image intensifiers Harmonic analysis Sensors

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Multi-UAV Collaborative Data Collection for IoT Devices Powered by Battery

Multi-UAV Collaborative Data Collection for IoT Devices Powe...

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IEEE Conference on Wireless Communications and networking

作者： Yue Wang Xiangming Wen Zhiqun Hu Zhaoming Lu Jiansong Miao Chuanzhi Sun Hang Qi Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Hubei University Wuhan China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

Due to the limited energy of the Internet of Things (IoT) device, unmanned aerial vehicle (UAV) as a mobile fusion center can effectively prolong the lifetime of IoT device via supporting communication with the device directly. Moreover, since UAV's energy constrained, it will be a good measure to take multiple UAVs to collect data from devices in large areas. In this paper, we investigate multi-UAV collaborative data collection system, where multiple UAVs collect data from two-dimensional distributed devices on flying mode or hovering mode. The objective is to minimize UAVs' total flight time while allowing each device to complete data upload successfully with limited energy. To this end, firstly, a cell partition based on Voronoi diagram is used to allocate the collection areas of each UAV. Then, in each associated area, UAV determines the whole trajectory to serve devices. Lastly, given load requirement of ground devices and energy limitation, the optimal data collection mode of each device is decided to minimize flight time of each UAV. Simulation results show that the proposed multi-UAV data collection scheme can shorten collection task completion time significantly.

关键词：

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Deep Reinforcement Learning for Energy Saving in Radio Access network

Deep Reinforcement Learning for Energy Saving in Radio Acces...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Keran Zhang Xiangming Wen Yawen Chen Zhaoming Lu Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728187556

ISBN: (纸本)9781728193106

Energy consumption of radio access networks (RANs) has become a significant issue, due to the growing density of base stations (BSs). Recent works have validated the possibility of saving energy in RANs by dynamically turning on/off some BSs. However, most of existing works are based on the stochastic traffic models, whose performance is unknown with the real-world traffic. In this paper, we propose a data-driven and deep reinforcement learning based BS sleeping scheme. Specifically, we first build TrafficNet, which is a anonymous traffic data set collected from a commercial RAN in Shanghai. Moreover, we propose a Double Deep Q-Learning network based BS Sleeping Algorithm (DDQS) to optimize energy utilization while compromising the quality of services (QoS). In addition, to expedite the training process under the TrafficNet, we pre-process the data using manipulation tool to facilitate the data analysis. Simulation results show that the proposed DDQS shows superior performance to the state-of-art sleeping algorithms in terms of energy saving and QoS guarantee.

关键词： Quality of service Energy consumption Machine learning Load modeling Adaptation models Conferences Central Processing Unit

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Three-Dimensional Aerial Cell Partitioning Based on Optimal Transport Theory

Three-Dimensional Aerial Cell Partitioning Based on Optimal ...

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IEEE International Conference on Communications Workshops, ICC

作者： Yue Wang Zhiqun Hu Xiangming Wen Zhaoming Lu Jiansong Miao Hang Qi Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Hubei University Wuhan China

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

In this paper, we investigate a three-dimensions (3D) cellular network that incorporates both unmanned aerial vehicle-base stations (UAV-BSs) and UAV-users (UAV-UEs). In the considered model, UAV-BSs hover in 3D space to serve UAV-UEs that are distributed according to an arbitrary spatial distribution function. We propose an optimal 3D cell partitioning method based on fair resource allocation scheme for which the total amount of data service is maximized. Utilizing the mathematical tool of optimal transport theory, the cell partitioning problem can be modeled as a semi-discrete optimal transport problem which is easy to be solved. To this end, we apply steepest-ascent search algorithm to solve the 3D cell partition optimization problem simplified by the optimal transport theory. The simulation results show that fairness of the proposed cell partitioning algorithm is higher than max-SINR algorithm, and it also guarantees the amount of system data service.

关键词： Three-dimensional displays Silicon Optimization Partitioning algorithms Resource management Interference Microprocessors

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Subject-independent human pose image construction with commodity Wi-Fi

arXiv

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arXiv 2020年

作者： Zhou, Shuang Guo, Lingchao Lu, Zhaoming Wen, Xiangming Zheng, Wei Wang, Yiming Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Recently, commodity Wi-Fi devices have been shown to be able to construct human pose images, i.e., human skeletons, as fine-grained as cameras. Existing papers achieve good results when constructing the images of subjects who are in the prior training samples. However, the performance drops when it comes to new subjects, i.e., the subjects who are not in the training samples. This paper focuses on solving the subject-generalization problem in human pose image construction. To this end, we define the subject as the domain. Then we design a Domain-Independent Neural network (DINN) to extract subject-independent features and convert them into fine-grained human pose images. We also propose a novel training method to train the DINN and it has no re-training overhead comparing with the domain-adversarial approach. We build a prototype system and experimental results demonstrate that our system can construct fine-grained human pose images of new subjects with commodity Wi-Fi in both the visible and through-wall scenarios, which shows the effectiveness and the subject-generalization ability of our model. Copyright © 2020, The Authors. All rights reserved.

关键词： Sampling

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Polarization-space based interference alignment for cognitive heterogeneous cellular network 13th

Polarization-space based interference alignment for cognitiv...

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13th EAI International Conference on Cognitive Radio Oriented Wireless networks, CROWNCOM 2018

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

ISBN: (纸本)9783030054892

In underlay cognitive heterogeneous cellular network (CHCN), small cells can transmit their signals as long as the interference to macro cell is below a threshold. Consider a two-layer CHCN with polarized MIMO small cells, a novel polarization-space based interference alignment scheme is proposed. The cross-tier interference between macro cell and small cells is addressed by two given algorithms with different purposes. Orthogonal projection based polarization-space interference alignment (OP-PSIA) for ensuring the minimum effect to macro cell and interference constrained polarization-space interference alignment (IC-PSIA) for maximizing the performances of small cells if permitted. The co-tier interference between small cells are reduced by a minimum total mean squared error (MMSE) algorithm. Then we give specific solutions for two algorithms both including orthogonal projection processing and analytically iterative calculations. Simulation results show the improvement of two algorithms in BER performance of small cells while ensuring the protection of macro cell and keeping maximum overall sum rate. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Polarization

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