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International Conference on wireless communications and Signal Processing (WCSP)

作者： Yueyue Zhang Yunchi Shi Yihuan Pang Fei Shen Feng Yan Lianfeng Shen School of Information Science and Engineering Southeast University Nanjing Jiangsu P. R. China Shanghai Aerospace Electronic Technology Institute Shanghai China Key Lab for Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China

With the development of space communication technology, space network is gradually enhanced by distributed spacecraft. This novel architecture of space network brings new challenges on self-organization and self-adaptation to inter spacecraft communication and packet routing From the perspective of delay tolerant network. In This paper, we propose an efficient and flexible routing algorithm by taking consideration of the constraint of the limited payload resource. In order to meet the requirement of complex interactive information exchanged among the spacecraft, a path selection algorithm is designed based on cascade fuzzy neural network (FNN) scheme, which mainly consists of a FNN processing module with four parts, ie. fuzzification, inference, defuzzification and rule database. Therefore, the information concerning the relative movement velocity, distance and storage capacity of each satellite can be formulated as a multidimensional vector to describe the characteristics of the space network. Additionally, the stability of each link could be derived via the first-level FNN, and then the probability of successful transmission measuring each link would be obtained through the second-level FNN. Finally, the performance of the proposed algorithm is analyzed and verified by the simulation.

关键词： Fuzzy neural networks Space vehicles Routing Heuristic algorithms Encoding Convolutional codes Decoding

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QoE-driven distributed media services in D2D communications underlaying cellular networks

QoE-driven distributed media services in D2D communications ...

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2017 IEEE International Conference on communications Workshops, ICC Workshops 2017

作者： Chen, Mingkai Lei, Wang Chen, Jianxin Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education China

ISBN: (纸本)9781509015252

Device-to-device (D2D) communication has been widely studied to improve network performance and been considered as a potential technological component for the next generation communication. As Quality of Experience (QoE) is the major determining factor for the media service, we aim at promoting user's QoE Mean of Score (MOS) value for dynamic cellular networks. In this paper, we focus on the problem of heterogeneous media service distribution in D2D communications underlaying cellular networks. Firstly, we propose an innovative distributed media service scheme based on QoE models that jointly solve the massive media content dissemination issue for cellular networks. Moreover, our work maximizes users' QoE satisfaction by jointly considering media-aware, popularity function and priority function. Then, we propose a novel resource allocation algorithm based on the popularity function to optimize the total users' QoE satisfaction and avoid the influence of D2D interference. Numerical simulation results demonstrate the effectiveness of our proposed schemes. © 2017 IEEE.

关键词： Quality of service

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Energy Efficiency Optimization Based on Context Awareness in Wearable Computing

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Procedia Computer Science 2018年 129卷 291-297页

作者： Feng Tian Yongan Guo Wei Liu Dapeng Li Zhen Yang Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications) Ministry of Education Nanjing 210003 China National Mobile Communications Research Laboratory Southeast University Nanjing 210096 China

The motion capture system of the wireless wearable device can achieve identification and classification of human motion through various classifiers, but wireless data transmission consumes most of the energy of the sensor node. An adaptive wireless transmission control algorithm based on context awareness is proposed realize energy efficiency optimization based on single classifier and multiple classifiers fusion. Firstly, adaptive wireless transmission control method based on fixed single classifier can effectively improve the overall energy efficiency under the limit of classification accuracy. Secondly, adaptive transmission control method of multi-classifier fusion is proposed with adjusting decision threshold based on various classifiers and their characteristics. The experimental results validate our proposed algorithm.

关键词： Wearable computing energy efficiency adaptive transmission control context awareness multiple classifiers fusion

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Hybrid Indoor Localization Method Based on Signal Subspace Fingerprint Database 17

Hybrid Indoor Localization Method Based on Signal Subspace F...

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2017 17th IEEE International Conference on communication technology (ICCT 2017)

作者： Weigang Wang Wenrui Wang Kexue Sun School of Electronic Science and Engineering Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and sensor network technology ministry of education

ISBN: (纸本)9781509039456;9781509039449

To solve the localization problem of multipath propagation in complex indoor circumstance, a localization method of signal subspace matching based on fingerprint database is proposed by using small antenna array in the indoor environment. Compared to the RSSI, the signal subspace fingerprint can obtain better effect by utilizing more space information. The received signal from each array is firstly processed with self-correlation and it's eigenvalue decomposed to create the signal subspace fingerprint. Location is then determined by the smallest angle between the received signal subspace and the fingerprint database. Simulation results show that the proposed algorithm has made a great improvement in localization accuracy.

关键词： indoor localization fingerprint database subspace matching hybrid localization

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A Deep Learning Framework with Spatial-Temporal Attention Mechanism for Cellular Traffic Prediction

A Deep Learning Framework with Spatial-Temporal Attention Me...

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IEEE Globecom Workshops

作者： Yun Gao Xin Wei Liang Zhou Haibing Lv College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center for Communication and Information Technology (NUPT) Nanjing China Key Laboratory of Broadband Wireless Communications and Sensor Network Technology Ministry of Education (NUPT) Nanjing China Wireline Product Planning Dept Wireline Product Operation ZTE Corporation Nanjing China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Currently, traffic prediction in cellular communication has become an important way to relieve traffic congestion, and then guarantee users' quality of experience (QoE) in multimedia service. However, when concerning traffic prediction in the context of big data, traditional deep learning models only have limited memory lengths and thus do not satisfy high prediction accuracy demand. In this paper, we design a new deep learningbased framework, which is built on our proposed novel spatialtemporal attention mechanism, to further increase the prediction accuracy of cellular traffic resource. Specifically, we firstly design a big data hierarchical architecture with four levels to extract meaningful information. Then, considering that cellular traffic resource from base stations characterizes spatial-temporal dependencies, we integrate conventional temporal attention mechanism for target area with that from spatial neighbor areas, proposing a novel spatial-temporal attention mechanism. Finally, we utilize this spatial-temporal attention mechanism based LSTM (STaLSTMs) to predict cellular traffic resource from base stations. Experimental results demonstrate that our proposed framework has better performances in cellular traffic prediction than other competing models. Importantly, in some application scenarios, this framework can also maintain high accuracy but only with 60% data.

关键词： Big Data Machine learning Data models Predictive models Base stations Quality of experience Time series analysis

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Multi-copy entanglement purification with practical spontaneous parametric down conversion sources

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Chinese Physics B 2017年第6期26卷 53-60页

作者：张帅帅祁舒周澜盛宇波 Key Laboratory of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts and Telecommunications Bell Honors School Nanjing University of Posts and Telecommunications College of Mathematics & Physics Nanjing University of Posts and Telecommunications

Entanglement purification is to distill the high quality entanglement from the low quality entanglement with local operations and classical communications. It is one of the key technologies in long-distance quantum communication. We discuss an entanglement purification protocol（EPP） with spontaneous parametric down conversion（SPDC） sources, in contrast to previous EPP with multi-copy mixed states, which requires ideal entanglement sources. We show that the SPDC source is not an obstacle for purification, but can benefit the fidelity of the purified mixed state. This EPP works for linear optics and is feasible in current experiment technology.

关键词： quantum communication entanglement entanglement purification

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lednet: A lightweight encoder-decoder network for real-time semantic segmentation

arXiv

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arXiv 2019年

作者： Wang, Yu Zhou, Quan Liu, Jia Xiong, Jian Gao, Guangwei Wu, Xiaofu Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts & Telecommunications China Key Lab. of Broadband Wireless Communications and Sensor Network Technology Nanjing University of Posts & Telecommunications China Institute of Advanced Technology Nanjing University of Posts & Telecommunications China Department of Computer and Information Sciences Temple University Philadelphia United States

The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks. In this paper, we present a lightweight network to address this problem, namely LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation. More specifically, the encoder adopts a ResNet as backbone network, where two new operations, channel split and shuffle, are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy. On the other hand, an attention pyramid network (APN) is employed in the decoder to further lighten the entire network complexity. Our model has less than 1M parameters, and is able to run at over 71 FPS in a single GTX 1080Ti GPU. The comprehensive experiments demonstrate that our approach achieves state-of-the-art results in terms of speed and accuracy trade-off on CityScapes dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique

arXiv

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arXiv 2019年

作者： Huang, Hongji Yang, Yuchun Wang, Hong Ding, Zhiguo Sari, Hikmet Adachi, Fumiyuki Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China Sequans Communications Colombes92700 France Jilin University Changchun130012 China School of Electrical and Electronic Engineering University of Manchester ManchesterM13 9PL United Kingdom Tohoku University Sendai980-8577 Japan

Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest in the research community. However, the current UAV navigation schemes are unable to capture the UAV motion and select the best UAV-ground links in real-time, and these weaknesses overwhelm the UAV navigation performance. To tackle these fundamental limitations, in this paper, we merge the state-of-the-art deep reinforcement learning with the UAV navigation through massive multiple input multiple output (MIMO) technique. To be specific, we carefully design a deep Q-network (DQN) for optimizing the UAV navigation by selecting the optimal policy, and then we propose a learning mechanism for processing the DQN. The DQN is trained so that the agent is capable of making decisions based on the received signal strengths for navigating the UAVs with the aid of the powerful Q-learning. Simulation results are provided to corroborate the superiority of the proposed schemes in terms of the coverage and convergence compared with that of other schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Corrigendum to “3D shape segmentation via shape fully convolutional networks” [Computers & Graphics 70 (2018) 128–139]

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Computers and Graphics (Pergamon) 2018年 76卷 181-181页

作者： Wang, Pengyu Gan, Yuan Shui, Panpan Yu, Fenggen Zhang, Yan Chen, Songle Sun, Zhengxing State Key Lab for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China

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ZF-Based Beamforming for wireless Powered Cognitive Satellite-Terrestrial networks

ZF-Based Beamforming for Wireless Powered Cognitive Satellit...

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IEEE Global communications Conference

作者： Zhi Lin Min Lin Tomaso de Cola Benoit Champagne A. Lee Swindlehurst College of Communications Engineering Army Engineering University of PLA Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Institute of Communications and Navigation German Aerospace Center (DLR) Germany Department of Electrical and Computer Engineering McGill University Montreal Canada Center for Pervasive Communications and Computing University of California Irvine Irvine USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, we propose a novel zero-forcing (ZF)-based beamforming (BF) scheme for a wireless powered cognitive satellite-terrestrial network (CSTN) operated in the millimeter wave band. Assuming that the satellite and base station are equipped with multiple antennas, we aim at maximizing the sum rate of the CSTN while satisfying the signal-to-interference-plus-noise-ratio requirements for both the information receivers (IRs) and earth stations, the energy harvesting requirements of the energy receivers (ERs), and the secrecy constraints at the ERs. Since the resulting optimization problem is mathematically intractable, we propose a novel multi-beam-based ZF BF scheme to generate beamforming vectors to serve the IRs and ERs. Specifically, the original nonconvex problem is decomposed into two independent subproblems. The first subproblem, which features beam orthogonality constraints, leads to closed form solutions for the beamforming vectors. The second subproblem, aiming at finding the optimal power allocation, is solved via the S-procedure. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results.

关键词： Erbium Satellite broadcasting wireless communication Satellite antennas Array signal processing Energy harvesting Optimization

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