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Quality Aware Aerial-to-Ground 5G Cells through open-Source Software

Quality Aware Aerial-to-Ground 5G Cells through Open-Source ...

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2019 ieee Global Communications conference (GLOBECOM)

作者： Francesco D'Alterio Ludovico Ferranti Leonardo Bonati Francesca Cuomo Tommaso Melodia Sapienza University of Rome Rome Italy Institute for the Wireless Internet of Things Northeastern University Boston MA USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

This paper investigates the advantages and design challenges of leveraging Unmanned Aerial Vehicles (UAVs) to deploy 4G/5G femto- and pico-cells to provide quality-aware user service and improve network performance. In order to do so, we combine UAVs dashing flight capabilities with Software-defined Radios (SDRs) flexibility and devise the concept of self-optimizing UAV Base Stations (UABSs). The proposed framework allows for on-the-fly drone repositioning based on rigorous optimization techniques using real-time network metrics to enhance users' service. This makes it possible to offload the traditional cellular infrastructure, or to mend its temporary failure, by deploying UABSs in areas of interest. Cellular connectivity is, then, provided to mobile subscribers through the LTE-compliant openAirInterface software interfaced with the on-drone SDR. We first describe the UABS design challenges and approaches. Then, we give details on the devised optimization algorithm and its main requirements. Finally, we illustrate a prototype implementation of the proposed UABS that leverages an SDR device and a PX4 flight controller, and test its effectiveness. Experimental results demonstrate that UABSs are able to autonomously reposition themselves based on cellular network metrics and to improve network performance.

关键词： Optimization Drones Base stations Measurement Linear programming Protocols

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Performance Evaluation of NETCONF-Based Low Latency Cross-Connect for 5G C-RAN architectures 20

Performance Evaluation of NETCONF-Based Low Latency Cross-Co...

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20th International conference on Transparent Optical networks (ICTON)

作者： Andrus, B. Autenrieth, A. Pachnicke, S. Zou, S. Olmos, J. J. V. Monroy, I. T. ADVA Opt Networking Martinsried Germany Tech Univ Denmark Dept Photon Engn Lyngby Denmark Kiel Univ CAU Kiel Germany Mellanox Technol Roskilde Denmark Tech Univ Eindhoven Dept Elect Engn Eindhoven Netherlands

ISBN: (纸本)9781538666050

The development of 5G wireless technology is in progress looking to cope with the increasing demands for high capacity, low latency, and ubiquitous mobile access. Cloud/Centralized Radio Access networks (C-RAN) has been proposed as a promising approach to address the 5G benchmarks. C-RAN is a rising mobile network architecture based on the centralization and pooling of baseband processing elements, with the scope of increasing resource utilization efficiency and air-interface performance gains with fast scaling multi-cell coordination. In this paper we focus on proposing and evaluating a flexible low-latency cross-connect (XC) switch that allows a dynamic scheduling of networking resources between baseband units (BBUs) and remote radio heads (RRHs) in C-RAN deployments. On the one hand, we develop a control plane mechanism for manipulating the XC configuration and state data based on a custom YANG model and network Configuration (NTCONF) protocol. Using a standard open NETCONF interface, automation of heterogeneous C-RAN resource assignment can be facilitated for 5G architectures. Secondly, we evaluate the performance of our XC in relation to the C-RAN stringent requirements and show that the latency and jitter introduced have negligible influence compared to radio interface limits. The impact of switching delay on the performance of a live system has yet to be tested however, a measured average switching time of 252 ms could still disrupt the ongoing connections.

关键词： low latency XC C-RAN NETCONF 5G

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An Integer Linear Program for Mixed-Weight open Locating-Dominating Sets 52

An Integer Linear Program for Mixed-Weight Open Locating-Dom...

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52nd Annual conference on Information Sciences and Systems (CISS)

作者： Givens, Robin M. Kincaid, Rex K. Mao, Weizhen Yu, Gexin Randolph Macon Coll Dept Comp Sci Ashland VA 23005 USA Coll William & Mary Dept Math Williamsburg VA 23185 USA Coll William & Mary Dept Comp Sci Williamsburg VA 23185 USA

ISBN: (纸本)9781538605790

Mixed-weight open locating-dominating sets (mixedweight OLD-sets) model systems that use sensors of multiple strengths to locate and detect problems in a network. Randomly-distributed wireless sensor networks, which often use sensors of different strengths, can be modeled by random geometric graphs. We build and study an Integer Linear Program to solve for mixed-weight OLD-sets and use greedy algorithms to generate mixed-weight OLD-set estimates in random geometric graphs. We also extend our previous results for mixed-weight OLD-sets in random graphs [6] to random geometric graphs by estimating the probabilistic upper bound for the size of the set.

关键词： integer linear programming mixed-weight open locating-dominating sets random geometric graphs wireless sensor networks

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Information Security of IoT Wireless Segment

Information Security of IoT Wireless Segment

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Global Smart Industry conference (GloSIC)

作者： Parotkin, Nikolay Y. Zolotarev, Vyacheslav V. Reshetnev Siberian State Univ Sci & Technol Inst Informat & Telecommun Krasnoyarsk Russia

ISBN: (纸本)9781538673867

The most probable threats for the Internet of Things (IoT) wireless connection channel according to the classification of the Federal agency of technical and export control and Department of Homeland security were considered in the article. The analysis of standard means of ieee 802.11 networks protection implementation in the IoT infrastructure was conducted. The description of the adopted approaches to protection, based on the open source software, was made. The task solution of optimal wireless network coverage zone planning together with signal power maximization on the IoT devices of legitimate access points by automated means, which will reduce the probability of its switch to non-authorized points in case of a re-authentication attack was considered in the frames of the first approach. A described software solves the task of multi-criteria optimization of a wireless network model, based on the user's source data, which reflect the place of network exploitation place with the help of the modified genetic programming algorithm. The results of the theoretical calculation and practical measurement of signal levels as well as the evaluation of the calculations' accordance were presented. The second approach, considered in the article, is the system of wireless segment monitoring on the single-board computer basis. It enables one to detect suspicious frames in radio air and dangerous access points according to the "white" list. The described System can localize and counteract the intruder's access point depending on response parameters and inform the holder about the fact and details of an incident. The advantage of the measures' complex, described in the article, is the ability of optimal installation of such a monitoring device with the coverage of all protected IoT performance zone, based on the network coverage map with the vulnerable points on the area.

关键词： Internet of things IoT IoT modeling IoT security network security wi-fi attack network modeling Wi-Fi coverage

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Uplink-Downlink Duality for Integer-Forcing in Cloud Radio Access networks 56

Uplink-Downlink Duality for Integer-Forcing in Cloud Radio A...

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56th Annual Allerton conference on Communication, Control, and Computing, Allerton 2018

作者： Bakoury, Islam El Nazer, Bobak ECE Department Boston University United States

ISBN: (纸本)9781538665961

Consider a cloud radio access network where neighboring basestations are able to jointly encode and decode their signals via rate-limited links to a central processor. One promising approach to such systems is for the basestations to simply quantize their observations and send them to the central processor (during the uplink phase) and to emit the quantized signals generated by the central processor (during the downlink phase). Several recent works have proposed compression-based architectures based on sequential source and channel coding. In prior work, we proposed an integer-forcing architecture for the uplink phase, and, in this paper, we propose an integer-forcing architecture for the downlink phase. As part of the achievability argument, we introduce a novel 'reverse' integer-forcing source coding strategy that can be used to quantize sources so that their quantization noises are correlated, i.e., multivariate compression. We also establish uplink-downlink duality between our uplink and downlink integer-forcing architectures, and use this as the basis for optimizing the beamforming, equalization, and integer matrices. © 2018 ieee.

关键词： Integer programming

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Deep learning for MRI-based CT synthesis: a comparison of MRI sequences and neural network architectures

Deep learning for MRI-based CT synthesis: a comparison of MR...

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ieee Symposium on Nuclear Science (NSS/MIC)

作者： Andrés Larroza Laura Moliner Juan M. Álvarez-Gómez Sandra Oliver Héctor Espinós-Morató Marina Vergara-Díaz María J. Rodríguez-Álvarez Instituto de Instrumentación para Imagen Molecular (I3M). Universitat Politècnica de València (UPV)-Consejo Superior de Investigaciones Científicas (CSIC) Valencia Spain

ISBN: (数字)9781728141640

ISBN: (纸本)9781728141657

Synthetic computed tomography (CT) images derived from magnetic resonance images (MRI) are of interest for radiotherapy planning and positron emission tomography (PET) attenuation correction. In recent years, deep learning implementations have demonstrated improvement over atlas-based and segmentation-based methods. Nevertheless, several open questions remain to be addressed, such as which is the best of MRI sequences and neural network architectures. In this work, we compared the performance of different combinations of two common MRI sequences (T1- and T2-weighted), and three state-of-the-art neural networks designed for medical image processing (Vnet, HighRes3dNet and ScaleNet). The experiments were conducted on brain datasets from a public database. Our results suggest that T1 images perform better than T2, but the results further improve when combining both sequences. The lowest mean average error over the entire head (MAE = 101.76 ± 10.4 HU) was achieved combining T1 and T2 scans with HighRes3dNet. All tested deep learning models achieved significantly lower MAE (p

关键词： Magnetic resonance imaging Computed tomography Biological neural networks Head Machine learning Convolution

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Developing a Smart Pedestrian network Big Data Platform for Municipal Organizations 2

Developing a Smart Pedestrian Network Big Data Platform for ...

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2nd European conference on Electrical Engineering and Computer Science (EECS)

作者： Papageorgiou, George Petrakis, Corinne Maimaris, Athanasios EUC Res Ctr Nicosia Cyprus

ISBN: (纸本)9781728119298

In our Digital Economy, great opportunities but also challenges are emerging for today's organizations. To successfully implement information system solutions involving a variety of data sources, many aspects should be considered. These revolve around the choice of platform strategy, which should handle aspects such as the provision of Application programming Interfaces (API), privacy and security concerns and how open access to data should be. This paper examines design issues for developing a digital ecosystem for municipal organizations by considering the opportunities presented by Big Data. The main focus is to suggest solutions for the deployment of an envisioned Smart Pedestrian network (SPN) system. The proposed solution combines multiple subsystems, such as Geographical Information Systems, intelligent agents, sensor systems, crowdsourcing, and social networks to create an integrated big data approach for the urban environment. Such an ecosystem is exceptionally useful for the effective involvement of municipality stakeholders, such as members of the public community, management, city planning, public works maintenance teams as well as local businesses.

关键词： Platform Strategies Crowdsourcing Big Data Smart Cities Intelligent Systems Municipal Organizations

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A Study on Invulnerability Optimization of Multi-aircraft Communication network 30

A Study on Invulnerability Optimization of Multi-aircraft Co...

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30th Chinese Control and Decision conference (CCDC)

作者： Lu, Shuren Wang, Lei Jiang, Yuan Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China Nanchang Hangkong Univ Minist Educ Key Lab Nondestruct Test Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781538612446

In order to optimize the existingcomplex multi-aircraft communication network, a new method is developed to improve its invulnerability. We transform the invulnerability problem of the multi-aircraft communication network into a nonlinear mixed-integer programming problem that optimizes the average two-step degree,and use the improved ant colony algorithm to solve the optimal average two-step degree problem. Through simulation and analysis, the average two-step degree can not only be used as a measure of the invulnerability of multi-aircraft communication network but also can help to optimize the invulnerability of multi-aircraft communication network.

关键词： complex networks the average two-step degree invulnerability optimization

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Optimizing Latency Sensitive Applications for Amazon's Public Cloud Platform

Optimizing Latency Sensitive Applications for Amazon's Publi...

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ieee Global Communications conference

作者： Janos Czentye Istvan Pelle Andras Kern Balazs Peter Gero Laszlo Toka Balazs Sonkoly MTA-BME Network Softwarization Research Group Ericsson Research

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Recent cloud technologies enable a diverse set of novel applications with capabilities never seen before. Cloud native programming, microservices, serverless architectures are novel paradigms reducing the burden on both software developers and operators while enabling cloud-grade service deployments. Several types of applications fit in well with the new concepts, however, latency sensitive applications with strict delay constraints pose additional challenges on the platforms. Can we run these applications on today's public cloud platforms making use of the brand new tools and techniques? In this paper, we try to answer this question by addressing one of the most widely used and versatile public cloud platforms, namely Amazon's AWS, and we propose a novel mechanism to optimize the software "layout" based on dynamic performance measurements. Our contribution is threefold. First, we define a combined performance and cost model on CaaS/FaaS (Container/Function as a Service) platforms, specifically for AWS, based on a comprehensive performance analysis, and we also provide an application model capturing the performance requirements. Second, we formulate an optimization problem which minimizes the deployment costs on AWS while meeting the latency constraints. A polynomial algorithm finding the optimal solution is also given. Third, we evaluate the model and the algorithm for different scenarios and investigate the performance on today's system.

关键词： Cloud computing Delays Software Containers Layout Optimization

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PNeuro: a scalable energy-efficient programmable hardware accelerator for neural networks

PNeuro: a scalable energy-efficient programmable hardware ac...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Carbon, A. Philippe, J-M. Bichler, O. Schmit, R. Tain, B. Briand, D. Ventroux, N. Paindavoine, M. Brousse, O. CEA LIST F-91191 Gif Sur Yvette France GlobalSensing Technol 14 Rue Pierre de Coubertin F-21000 Dijon France

ISBN: (纸本)9783981926309

Artificial intelligence and especially Machine Learning recently gained a lot of interest from the industry. Indeed, new generation of neural networks built with a large number of successive computing layers enables a large amount of new applications and services implemented from smart sensors to data centers. These Deep Neural networks (DNN) can interpret signals to recognize objects or situations to drive decision processes. However, their integration into embedded systems remains challenging due to their high computing needs. This paper presents PNeuro, a scalable energy-efficient hardware accelerator for the inference phase of DNN processing chains. Simple programmable processing elements architectured in SIMD clusters perform all the operations needed by DNN (convolutions, pooling, non-linear functions, etc.). An FDSOI 28 inn prototype shows an energy efficiency of 700 GMACS/s/W at 800 MHz. These results open important perspectives regarding the development of smart energy-efficient solutions based on Deep Neural networks.

关键词： Neural networks Neural network hardware Computer architectures FPGA ASIC Low power

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