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Improving Simulation of Lognormal Sum Distributions With Hyperspace Replication

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Improving Simulation of Lognormal Sum Distributions With Hyp...

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

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

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Location-Aware Hypergraph Coloring Based Spectrum Allocation...

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

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

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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)

Analysis of Memory Capacity for Deep Echo State networks

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arXiv 2019年

作者： Liu, Xuanlin Chen, Mingzhe Yin, Changchuan Saad, Walid Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

—In this paper, the echo state network (ESN) memory capacity, which represents the amount of input data an ESN can store, is analyzed for a new type of deep ESNs. In particular, two deep ESN architectures are studied. First, a parallel deep ESN is proposed in which multiple reservoirs are connected in parallel allowing them to average outputs of multiple ESNs, thus decreasing the prediction error. Then, a series architecture ESN is proposed in which ESN reservoirs are placed in cascade that the output of each ESN is the input of the next ESN in the series. This series ESN architecture can capture more features between the input sequence and the output sequence thus improving the overall prediction accuracy. Fundamental analysis shows that the memory capacity of parallel ESNs is equivalent to that of a traditional shallow ESN, while the memory capacity of series ESNs is smaller than that of a traditional shallow ESN. In terms of normalized root mean square error, simulation results show that the parallel deep ESN achieves 38.5% reduction compared to the traditional shallow ESN while the series deep ESN achieves 16.8% reduction. Copyright © 2019, The Authors. All rights reserved.

关键词： network architecture

Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications

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arXiv 2019年

作者： Li, Zheng Guo, Caili School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

—Device-to-device (D2D) communication underlay cellular networks is a promising technique to improve spectrum efficiency. In this situation, D2D transmission may cause severe interference to both the cellular and other D2D links, which imposes a great technical challenge to spectrum allocation. Existing centralized schemes require global information, which causes a large signaling overhead. While existing distributed schemes requires frequent information exchange among D2D users and cannot achieve global optimization. In this paper, a distributed spectrum allocation framework based on multi-agent deep reinforcement learning is proposed, named multi-agent actor critic (MAAC). MAAC shares global historical states, actions and policies during centralized training, requires no signal interaction during execution and utilizes cooperation among users to further optimize system performance. Moreover, in order to decrease the computing complexity of the training, we further propose the neighbor-agent actor critic (NAAC) based on the neighbor users’ historical information for centralized training. The simulation results show that the proposed MAAC and NAAC can effectively reduce the outage probability of cellular links, greatly improve the sum rate of D2D links and converge quickly. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

Federated learning for task and resource allocation in wireless high altitude balloon networks

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arXiv 2020年

作者： Wang, Sihua Chen, Mingzhe Yin, Changchuan Saad, Walid Hong, Choong Seon Cui, Shuguang Vincent Poor, H. Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Department of Computer Science and Engineering Kyung Hee University Korea Republic of Chinese University of Hong Kong Shenzhen518172 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

In this paper, the problem of minimizing energy and time consumption for task computation and transmission is studied in a mobile edge computing (MEC)-enabled balloon network. In the considered network, each user needs to process a computational task in each time instant, where high-altitude balloons (HABs), acting as flying wireless base stations, can use their powerful computational abilities to process the tasks offloaded from their associated users. Since the data size of each user's computational task varies over time, the HABs must dynamically adjust the user association, service sequence, and task partition scheme to meet the users' needs. This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme. To solve this problem, a support vector machine (SVM)-based federated learning (FL) algorithm is proposed to determine the user association proactively. The proposed SVM-based FL method enables each HAB to cooperatively build an SVM model that can determine all user associations without any transmissions of either user historical associations or computational tasks to other HABs. Given the prediction of the optimal user association, the service sequence and task allocation of each user can be optimized so as to minimize the weighted sum of the energy and time consumption. Simulations with real data of city cellular traffic from the OMNILab at Shanghai Jiao Tong University show that the proposed algorithm can reduce the weighted sum of the energy and time consumption of all users by up to 16.1% compared to a conventional centralized method. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

A visualization compression algorithm considering interactive structure for energy trading of microgrid system 2

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A visualization compression algorithm considering interactiv...

2nd IEEE International Conference on Energy Internet, ICEI 2018

作者： Shen, Yidi Wen, Xiangming Wang, Luhan Guan, Wanqing Beijing Advanced Innovation Center for Future Internet Technology Beijing China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China

ISBN: (纸本)9781538641316

A microgrid is a novel energy production-storageconsumption mode in which energy entities can both generate and consume energy. With the access of these entities, the energy internet becomes a super complex network of information and physics integration. The computational difficulty of obtaining the topological information is improved. Studying the topology for energy trading of energy entities in different geographic regions can help to analyze the transmission of energy and information. Visualization is an important measure to display the network topological structure intuitively and clearly. In this paper, a compression algorithm that considered cross-regional interaction is proposed to obtain the main topology for visualization effectively. The proposed algorithm can realize the different degrees of compression between intra-regional topology and inter-regional topology. The simulation results have shown that the algorithm can preserve the main topological information and has a considerable compression rate. © 2018 IEEE.

关键词： Visualization

Dynamic Resource Orchestration of Service Function Chaining in network Function Virtualizations 1st

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Dynamic Resource Orchestration of Service Function Chaining ...

1st International Conference on 5G for Future Wireless networks, 5GWN 2017

作者： Yu, Bangchao Zheng, Wei Wen, Xiangming Lu, Zhaoming Wang, Luhan Ma, Lu Beijing Laboratory of Advanced Information Networks Beijing China Beijing Advanced Innovation Center for Future Internet Technology Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783319728223

network Functions Virtualization is a new network architecture framework and is revolutionizing the way networking service that how to design and deploy. NFV promotes virtualizing network functions and improves the flexibility to resource orchestration for request service function chains. However, how to find the most suitable resource in NFV-based network resource is a challenge. This paper presents a comprehensive state of the NFV resource orchestration by introducing a dynamic resource orchestration architecture that can configure dynamic resources. With consideration of load balance, energy cost and resource consumption, the resource orchestration is formulated as a multi-objective optimal problem. Finally, a multi-objective simulated annealing algorithm is used to obtain the optimal resource strategy to deploy network functions. Simulation results show that the solution for dynamic resource orchestration can achieve approximate optimal solution in acceptable time and reduce 8% energy consumption with a 0.89 Jain’s fairness index. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： network function virtualization

A fault-tolerant data acquisition scheme with MDS and dynamic clustering in energy internet 2

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A fault-tolerant data acquisition scheme with MDS and dynami...

2nd IEEE International Conference on Energy Internet, ICEI 2018

作者： Chen, Xin Wen, Xiangming Wang, Luhan Jing, Wenpeng Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Advanced Innovation Center for Future Internet Technology Beijing China

ISBN: (纸本)9781538641316

Energy Internet (EI) is regarded as a promising method to solve the energy shortage and improve the efficiency of renewable energy. It demands frequent exchanges of data flow and energy flow, which puts forward strict requests for data veracity and availability. As a result, a reliable and robust data network is critical to realize the promises of EI. This paper introduces the fog nodes with computing ability to enable erasure coding based fault-tolerant techniques so that the data availability can be guaranteed. However, the distributed and irregular deployment of EI nodes have brought many challenges to maximum distance separable code (MDS) based fault-tolerant schemes. As a result, this paper investigates the design of encoding parameters and clustering mechanism to adjust the flexible situations. And then, we propose a novel fault-tolerant mechanism based on MDS and dynamic clustering to improve the data availability. Numerical simulation results show our scheme works better than the backup and traditional MDS based schemes with determined cluster size. © 2018 IEEE.

关键词： Internet of Energy