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arXiv 2018年

作者： Brunner, Gino Szebedy, Bence Tanner, Simon Wattenhofer, Roger Computer Engineering and Networks Laboratory ETH Zurich

Drone delivery has been a hot topic in the industry in the past few years. However, existing approaches either focus on rural areas or rely on centralized drop-off locations from where the last mile delivery is performed. In this paper we tackle the problem of autonomous last mile delivery in urban environments using an off-the-shelf drone. We build a prototype system that is able to fly to the approximate delivery location using GPS and then find the exact drop-off location using visual navigation. The drop-off location could, e.g., be on a balcony or porch, and simply needs to be indicated by a visual marker on the wall or window. We test our system components in simulated environments, including the visual navigation and collision avoidance. Finally, we deploy our drone in a real-world environment and show how it can find the drop-off point on a balcony. To stimulate future research in this topic we open source our code. Copyright © 2018, The Authors. All rights reserved.

关键词： Location

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224 Gb/s Transmission over 10 km of smf at 1550 nm enabled by a sin optical dispersion compensator and stokes vector direct detect receiver

224 Gb/s Transmission over 10 km of smf at 1550 nm enabled b...

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Signal Processing in Photonic Communications, SPPCom 2020

作者： Alam, Md Samiul Osman, Mohamed Morsy Shahriar, Kh Arif Fiky, Eslam El Lessard, Stephane Angelis, Gabriele De Sorianello, Vito Fresi, Francesco Cavaliere, Fabio Poti, Luca Midrio, Michele Romagnoli, Marco Llosera, Gemma Vall Plant, David V. Department of Electrical and Computer Engineering McGill University MontrealQCH3A 0E9 Canada Ericsson Canada 8275 Trans Canada Route MontrealQCH4S 0B6 Canada Cnit Photonic Networks and Technologies National Laboratory Pisa56124 Italy Scuola Superiore Sant Anna TeCIP Institute Pisa56124 Italy Ericsson Via Giuseppe Moruzzi Pisa56124 Italy Cnit Università Degli Studi di Udine Udine33100 Italy Ericsson Research Stockholm16480 Sweden

We demonstrate 224 Gb/s net rate dual-polarization PAM4 signal transmission over 10 km of SMF in the C-band below the HD-FEC threshold of 3.8×10-3, using Stokes vector receiver and an integrated ring resonator ba... 详细信息

ISBN: (纸本)9781943580798

关键词： Signal receivers

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An Evaluation of Machine Learning Algorithms To Detect Attacks in Scada Network

An Evaluation of Machine Learning Algorithms To Detect Attac...

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Mediterranean Congress of Telecommunications (CMT)

作者： Sara Tamy Hicham Belhadaoui Mahmoud Almostafa Rabbah Nabila Rabbah Mounir Rifi RITM Laboratory Networks Computer Science Telecommunication Multimedia ESTC Hassan II University Casablanca Morocco Laboratory of Structural Engineering Intelligent Systems and Electrical Energy ENSAM Hassan II University Casablanca Morocco

ISBN: (纸本)9781728144214

Today, with the digitization of manufacturing, industries are becoming more and more competitive and offer personalized products and services. With The Industry of the Future also known as Industry 4.0 or Fourth Industrial Revolution, architecture has fundamentally changed. It is based on intelligent automation and integrates a new technologies that allow permanent communication between machines and systems to ensure an increasing improvement in production. Despite the advantages of industry 4.0, there are also challenges to be addressed: Network security has become very important than before, therefore we have to guarantee better intrusion detection, based on machine learning algorithms. To this end, we examine four supervised learning methods, namely: Naïve Bayes, Support Vector Machines (SVM), Trees J48 and Random Forest, and compare the results using different performance measures (accuracy, recall, time to build the model etc). The data set used in this study comes from a laboratory scale gas pipeline, entitled “10% Random Sample Gas Pipeline The objective of this research is to select the best algorithm to detect and predict attacks in SCADA Network, in order to take preventive measures against the risk of intrusion.

关键词： Support vector machines Vegetation Intrusion detection Integrated circuits Random forests Protocols

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Service Function Chain Planning with Resource Balancing in Space-Air-Ground Integrated networks

Service Function Chain Planning with Resource Balancing in S...

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IEEE Global Communications Conference

作者： Guangchao Wang Sheng Zhou Zhisheng Niu Shan Zhang Xuemin (Sherman) Shen Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Beijing Key Laboratory of Computer Networks School of Computer Science and Engineering Beihang Univesity Beijing China University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Space-air-ground integrated network (SAGIN) brings great potentials to extend the terrestrial networks and satisfy the diverse service demands from many emerging applications. The major challenge is the coordination of large-scale networks with heterogeneous communication and computation resources. In this paper, flexible and reconfigurable service provisioning based on service function chaining (SFC) is exploited to address the challenge, where the traffic flow of the network services need to pass through specified virtual network functions (VNFs) in a given order. Our main target is to optimize the planning of the service function chains under limited heterogeneous resources and to map them on physical networks, considering the balance of resource utilization of both communication and computation. The SFC planning problem is formulated as an integer non-linear programming problem, which is NP-hard. Then, we propose a heuristic SFC planning algorithm (HSP) to reduce the computational complexity. Moreover, we propose a new metric, aggregation ratio (AR), to observe the tradeoff between communication and computation resource consumptions. The simulations results demonstrate that the HSP achieves near-optimal performance and the communication and computation resources can be well tradeoffed via tuning AR. The service blockage probability is significantly decreased and the efficiency of resource utilization is improved by integrating SAGIN based on SFC.

关键词： Planning Delays Heuristic algorithms Bandwidth Quality of service Linear programming Computational modeling

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Cooperative Intelligence for Autonomous Driving

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ZTE Communications 2019年第2期17卷 44-50页

作者： CHENG Xiang DUAN Dongliang YANG Liuqing ZHENG Nanning State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Department of Electrical and Computer Engineering University of WyomingLaramieWY 82071USA Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’anShaanxi 710049China

Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent *** this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving *** first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for *** this,a cooperative intelligence framework is proposed for autonomous driving *** general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

关键词： autonomous driving cooperative intelligence information fusion vehicular communications and networking

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DISCO: Influence maximization meets network embedding and deep learning

arXiv

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arXiv 2019年

作者： Li, Hui Xu, Mengting Bhowmick, Sourav S. Sun, Changsheng Jiang, Zhongyuan Cui, Jiangtao State Key Laboratory of Integrated Services Networks Xidian University China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Computer Science and Technology Xidian University China School of Cyber Engineering Xidian University China

Since its introduction in 2003, the influence maximization (IM) problem has drawn significant research attention in the literature. The aim of IM is to select a set of k users who can influence the most individuals in the social network. The problem is proven to be NP-hard. A large number of approximate algorithms have been proposed to address this problem. The state-of-the-art algorithms estimate the expected influence of nodes based on sampled diffusion paths. As the number of required samples have been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency. In this paper, we present an orthogonal and novel paradigm to address the IM problem by leveraging deep learning models to estimate the expected influence. Specifically, we present a novel framework called DISCO that incorporates network embedding and deep reinforcement learning techniques to address this problem. Experimental study on real-world networks demonstrates that DISCO achieves the best performance w.r.t efficiency and influence spread quality compared to state-of-the-art classical solutions. Besides, we also show that the learning model exhibits good *** Codes 91D30, 68T01 Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Interference Recycling: Exploiting Interfering Signals to Enhance Data Transmission

Interference Recycling: Exploiting Interfering Signals to En...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zhao Li Jie Chen Kang G. Shin Jia Liu State Key Laboratory of Integrated Service Networks Xidian University Department of Electrical Engineering and Computer Science The University of Michigan Center for Cybersecurity Research and Development National Institute of Informatics

With the rapid development of wireless communication technologies, the demand for higher data rate and more concurrent transmissions has been continually increasing. Due to the widespread deployment of various wireless technologies, interference has become a key roadblock to the improvement of network performance. Interference has long been known to be harmful, leading to development of numerous interference management (IM) mechanisms based on resource segmentation or signal processing to mitigate or suppress interference. Since a desired signal can be distorted by interference, and thus be incorrectly decoded at the destination, we argue that interference can also be transformed intentionally to extract the desired data from interfering signal(s). Based on this observation, we propose Interference ReCycling (IRC). Under IRC, a recycling signal is generated with the interference a victim receiver (Rx) is subjected to, and then sent by the Rx's associated transmitter (Tx). Under the influence of the recycling signal, the desired data of the victim Tx-Rx pair can be recovered from the interference at the victim Rx. That is, by exploiting the interactions among multiple signals, i.e., recycling signal and interference, useful data information can be extracted from interference, or the unintended data carried in interference can be artificially converted to the desired one. Our theoretical analysis and numerical evaluation have shown that the proposed IRC can fully exploit interference, and hence can significantly improve the spectral efficiency (SE) of the victim Rx compared to the other existing IM methods.

关键词： Interference Recycling Wireless communication Receivers Data communication Macrocell networks Signal processing

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Improving receiver performance of diffusion-based molecular MIMO communication 18

Improving receiver performance of diffusion-based molecular ...

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18th International Conference on Nanotechnology, NANO 2018

作者： Hou, Pengfeng Zhao, Liqiang Eckford, Andrew W. State Key Laboratory of Integrated Services Networks XiDian University Xi'an China Department of Electrical Engineering and Computer Science York University TorontoON Canada

For the purpose of improving the relatively high transmission rate, we propose a novel multiple-input multiple-output (MIMO) system for molecular communication. To the best of our knowledge, our paper is the first that introduce the design of utilizes multiple types of molecules, multiple molecules emitters at transmitters and multiple molecules detectors at receivers in molecular MIMO. The impulse response of channel is obtained and mathematically modeled, the model is used to determine inter symbol interference (ISI). The designed receivers consist of three parts: concentration processor, detector and data fusion. The signal processing techniques for the molecular concentration with low computational complexity are proposed. For the design of detector, the Neyman-Pearson test is introduced to obtain the optimal detection performance. A low-complexity data fusion rule is proposed for data process. © 2018 IEEE.

关键词： Molecules

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Reconfigurable intelligent surface assisted mobile edge computing with heterogeneous learning tasks

arXiv

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arXiv 2020年

作者： Huang, Shanfeng Wang, Shuai Wang, Rui Wen, Miaowen Huang, Kaibin The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Electrical and Electronic Engineering The Department of Computer Science and Engineering The Sifakis Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China The Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it is with rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and internet of things (IoT) devices. In this paper, we present an infrastructure to perform ML tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criterions are to maximize the throughput, we aim at maximizing the learning performance. Specifically, we minimize the maximum learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS. An alternating optimization (AO)-based framework is proposed to optimize the three terms iteratively, where a successive convex approximation (SCA)-based algorithm is developed to solve the power allocation problem, closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching (ELS) framework to effectively solve the challenging nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified communication-training-inference platform is developed based on the CARLA platform and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Resource allocation technique for hybrid TDMA-NOMA system with opportunistic time assignment

arXiv

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arXiv 2020年

作者： Wei, Xinchen Al-Obiedollah, Haitham Cumanan, Kanapathippillai Zhang, Miao Tang, Jie Wang, Wei Dobre, Octavia A. Department of Electronic Engineering University of York YorkYO10 5DD United Kingdom Hashemite University Jordan School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Information Science and Technology Nantong University Nantong226000 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering Memorial University St. John's Canada

In this paper, we develop a resource allocation technique for a hybrid time division multiple access (TDMA) - non-orthogonal multiple access (NOMA) system with opportunistic time assignment. In particular, the available transmission time is divided into several time-slots, through which multiple users are served by exploiting power-domain NOMA. To fully exploit underlying benefits of this hybrid TDMA-NOMA system, we utilize the available resources efficiently by jointly allocating transmit power and time-slots to several groups of users in the system. Furthermore, these resources are allocated to maximize minimum rate of the users in the system. However, this max-min resource allocation problem is non-convex due to coupled design parameters of time and power allocations. Hence, we exploit a novel second-order cone formulation to overcome this non-convexity issue and develop an iterative algorithm to realize a solution to the original max-min problem. Simulation results show that this joint resource allocation technique has a considerable performance enhancement in terms of both minimum achieved rate and overall system throughput compared to that of the conventional resource allocation technique where equal time-slots are assigned to the groups of users. Copyright © 2020, The Authors. All rights reserved.

关键词： Time division multiple access

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