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arXiv 2020年

作者： Hao, Wanming Sun, Gangcan Zhou, Fuhui Mi, De Shi, Jia Xiao, Pei Leung, Victor C.M. School of Information Engineering Zhengzhou University Zhengzhou450001 China 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China State Key Laboratory of Integrated Services Networks Xi'dian University Xian710071 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T1Z4 Canada

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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Intelligent Eco Networking (IEN) II: A Knowledge-Driven Future Internet Infrastructure for Value-Oriented Ecosystem

Intelligent Eco Networking (IEN) II: A Knowledge-Driven Futu...

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IEEE International Conference on Hot Information-Centric Networking (HotICN)

作者： Kai Lei Shuokang Huang Jiyue Huang Huafang Liu Jin Liu PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Shenzhen Key Lab for Information Centric Networking & Blockchain Technology (ICNLAB) School of Electronic and Computer Engineering (SECE) Peking University Shenzhen P.R. China

ISBN: (数字)9781728157818

ISBN: (纸本)9781728157825

Intelligent Eco Networking (IEN) is intent on evolving the IP-based network architecture into a Knowledge-Driven Future Internet Infrastructure based on hierarchical heterogeneous networks composed of virtualized and configurable devices, taking the valuable contents as first-class entity, d riven by knowledge intelligence, integrating the decentralized consensus trust preservation, to establish a value-oriented social ecosystem. To counter with the complicated but inefficient mechanisms of IP, a tokenized fine-grained allocation mechanism i s proposed, taking comprehensive consideration of storage, computing and networking, bringing about a collaborative and mutually beneficial value-interconnection. Knowledge-Driven Scheme (KDS), as the guideline of IEN's establishment, illustrates the way to extract knowledge from the network, the value consensus of Blockchain, and the feedback control of Federated Learning, making economy-based distributed commerce feasible.

关键词： Knowledge engineering Quality of service Internet Protocols Ecosystems

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Research on Automatic Steering System of Agricultural Machinery Based on Fuzzy Neural Network 2

Research on Automatic Steering System of Agricultural Machin...

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

作者： Duau, Jie Zhang, Lei Zhang, Zhaoiinz Zhao, Jinzbo Jiang, Yan College of Automation Engineering Qingdao University of Technology Qingdao266500 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Qingdao City Blood Center Station Qingdao266000 China

ISBN: (纸本)9781538618035

Modern agriculture requires agricultural machinery to achieve real-time and efficient control performance in the process of work. In this paper, the neural network and the fuzzy control method combining fuzzy neural network controller based on fuzzy control rules and the improvement of the neural network based on genetic algorithm, the algorithm of fuzzy neural network controller, get the target output and the actual output curves by MATLAB simulation. The test platform was built on the Dongfang red tractor and both the output response test and the angle test were carried out, and the performance of the control system was analyzed by MATLAB simulation. It can be seen that this method can effectively shorten the response time of agricultural steering system, reduce overshoot and improve steering accuracy, which is a more stable control method. © 2018 IEEE.

关键词： Fuzzy control

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High throughput and low cost LDPC reconciliation for quantum key distribution

arXiv

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arXiv 2019年

作者： Mao, Haokun Li, Qiong Han, Qi Guo, Hong Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin150080 China State Key Laboratory of Advanced Optical Communication Systems and Networks Institute of Quantum Electronics School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China

Reconciliation is a crucial procedure in post-processing of Quantum Key Distribution (QKD), which is used for correcting the error bits in sifted key strings. Although most studies about reconciliation of QKD focus on how to improve the efficiency, throughput optimizations have become the highlight in high-speed QKD systems. Many researchers adpot high cost GPU implementations to improve the throughput. In this paper, an alternative high throughput and efficiency solution implemented in low cost CPU is proposed. The main contribution of the research is the design of a quantized LDPC decoder including improved RCBP-based check node processing and saturation-oriented variable node processing. Experiment results show that the throughput up to 60Mbps is achieved using the bi-directional approach with reconciliation efficiency approaching to 1.1, which is the optimal combination of throughput and efficiency in Discrete-Variable QKD (DV-QKD). Meanwhile, the performance remains stable when Quantum Bit Error Rate (QBER) varies from 1% to 8%. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

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Unsupervised feature learning for audio analysis 5

Unsupervised feature learning for audio analysis

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5th International Conference on Learning Representations, ICLR 2017

作者： Meyer, Matthias Beutel, Jan Thiele, Lothar Computer Engineering and Networks Laboratory ETH Zurich Zurich Switzerland

Identifying acoustic events from a continuously streaming audio source is of interest for many applications including environmental monitoring for basic research. In this scenario neither different event classes are known nor what distinguishes one class from another. Therefore, an unsupervised feature learning method for exploration of audio data is presented in this paper. It incorporates the two following novel contributions: First, an audio frame predictor based on a Convolutional LSTM autoencoder is demonstrated, which is used for unsupervised feature extraction. Second, a training method for autoencoders is presented, which leads to distinct features by amplifying event similarities. In comparison to standard approaches, the features extracted from the audio frame predictor trained with the novel approach show 13 % better results when used with a classifier and 36 % better results when used for clustering. © 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. All Rights Reserved.

关键词： Long short-term memory

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Joint Optimization of Networking and Computing Resources for Green M2M Communications Based on DRL

Joint Optimization of Networking and Computing Resources for...

引用

IEEE Global Communications Conference

作者： Meng Li Le Yang F. Richard Yu Wenjun Wu Zhuwei Wang Yanhua Zhang Beijing Laboratory of Advanced Information Networks Beijing University of Technology Beijing P.R. China Faculty of Information Technology Beijing University of Technology Beijing P.R. China Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Recent advances in Internet of Things (IoT) provide plenty of opportunities for various areas. Nevertheless, the machine-to-machine (M2M) communications-based IoT develops rapidly but suffers from extra energy consumption, large data transmission latency as well as overmuch network cost, because various of machine-type communication devices (MTCDs) are deployed in the network. To meet the requirements of energy efficient M2M communications, in this paper, we introduce a promising technology named as mobile edge computing (MEC), and propose a performance optimization framework with MEC for M2M communications network based on deep reinforcement learning (DRL). According to dynamic decision process by DRL, the appropriate access networks and the computing servers can be determined and selected with the minimum system cost, which includes lower network cost, time cost and energy consumption for data transmission and computing tasks execution. Extensive simulation results with different system parameters show that our proposed framework can effectively improve the system performance for M2M communications compared to the existing schemes.

关键词： Servers Machine-to-machine communications Task analysis Cloud computing Logic gates Network architecture Data communication

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An Improved Non-Geometrical Stochastic Model for Non-WSSUS Vehicle-to-Vehicle Channels

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ZTE Communications 2019年第4期17卷 62-71页

作者： HUANG Ziwei CHENG Xiang ZHANG Nan State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Wireless Algorithm Department Product Research and Development SystemZTE CorporationShanghai 201203China

A novel non-geometrical stochastic model(NGSM)for non-wide sense station ary uncorrelated scattering(non-WSSUS)vehicle-to-vehicle(V2V)channels is *** model is based on a conventional NGSM and employs a more accurate method to reproduce the realistic characteristics of V2V channels,which successfully extends the existing NGSM to include the line-of-sight(LoS)***,the statistical properties of the proposed model in different scenarios,including Doppler power spectral density(PSD),power delay profile(PDP),and the tap correlation coefficient matrix are simulated and compared with those of the existing ***,the simulation results dem onstrate not only the utility of the proposed model,but also the correctness of our theoreti cal derivations.

关键词： vehicle-to-vehicle non-WSSUS channels non-geometrical stochastic model LoS component statistical properties

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A delay and traffic balancing sensitive scheme for user association in limited fronthaul H-CRANs 15

A delay and traffic balancing sensitive scheme for user asso...

引用

15th International Symposium on Wireless Communication Systems, ISWCS 2018

作者： Liu, Shaobo Zhang, Biling Yu, Jung-Lang Han, Zhu School of Network Education Beijing University of Posts and Telecommunications China State Key Laboratory of Integrated Services Networks Xidian University China Department of Electrical Engineering Fu Jen Catholic University Taiwan Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of

ISBN: (纸本)9781538650059

In order to fully exploit the potentials of the heterogeneous cloud radio access networks (H-CRANs), how to associate the user equipments (UEs) with the appropriate low power nodes (LPNs) is a very essential problem. However, when the fronthaul constraint is limited, the UEs are sensitive to delay, and the LPNs are concerned about the traffic balancing, such a problem has not gained enough attention. In this paper, the aforementioned user association problem is investigated and formulated as a bidirectional matching game, where the UEs are seeking to access the most preferred LPN while the LPNs are seeking to accept the most preferred UEs. To find the solution to the proposed game, an iteration algorithm is proposed, and its computational complexity and convergence are proved as well. Simulation results show that our proposed user association scheme is effective and outperforms the existing schemes-in terms of achieved data rates, delay, traffic balancing and overall utility. © 2018 IEEE.

关键词： Iterative methods

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Signal demodulation with machine learning methods for physical layer visible light communications: Prototype platform, open dataset and algorithms

arXiv

引用

arXiv 2019年

作者： Ma, Shuai Dai, Jiahui Lu, Songtao Li, Hang Zhang, Han Du, Chun Li, Shiyin School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an710071 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

In this paper, we investigate the design and implementation of machine learning (ML) based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN based demodulator contains three restricted Boltzmann machines (RBMs) to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the knearest neighbor (KNN) algorithm. These three demodulators are trained and tested by our online open dataset. Experimental results show that the demodulation accuracy of the three datadriven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Adaptive boosting

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New construction of complementary sequence (or Array) sets and complete complementary codes (II)

arXiv

引用

arXiv 2019年

作者： Wang, Zilong Ma, Dongxu Xue, Erzhong Gong, Guang Budišin, Srdjan State Key Laboratory of Integrated Service Networks Xidian University Xi'an710071 China Department of Electrical and Computer Engineering University of Waterloo Waterloo ONN2L 3G1 Canada RT-RK Novi Sad21000 Serbia

Previously, we have presented a framework to use the para-unitary (PU) matrix-based approach for constructing new complementary sequence set (CSS), complete complementary code (CCC), complementary sequence array (CSA), and complete complementary array (CCA). In this paper, we introduce a new class of delay matrices for the PU construction. In this way, generalized Boolean functions (GBF) derived from PU matrix can be represented by an array of size 2 × 2 × · · · × 2. In addition, we introduce a new method to construct PU matrices using block matrices. With these two new ingredients, our new framework can construct an extremely large number of new CSA, CCA, CSS and CCC, and their respective GBFs can be also determined recursively. Furthermore, we can show that the known constructions of CSSs, proposed by Paterson and Schmidt respectively, the known CCCs based on Reed-muller codes are all special cases of this new framework. In addition, we are able to explain the bound of PMEPR of the sequences in the part of the open question, proposed in 2000 by Paterson. Copyright © 2019, The Authors. All rights reserved.

关键词： Hadamard matrices

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