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9th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2020

作者： He, Xiaoyuan Deng, Li Beijing University of Posts and Telecommunications Beijing Lab. of Advanced Information Networks Beijing Key Lab. of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China

ISBN: (数字)9781728198057

ISBN: (纸本)9781728198057

For achieving more multiplexing degrees, a method to generate and multiplex dual-polarized electromagnetic waves carrying orbital angular momentum(OAM) by a single reflective metasurface is proposed in this paper. Base on this method, a dual-polarized reflective metasurface that can generate eight OAM beams combined into a coaxial beam in terahertz regime is properly designed and simulated to verify this method. According to the simulation results, the proposed method provides an efficient way for a metasurface to generate and multiplex dual-polarized electromagnetic waves carrying OAM. © 2020 IEEE.

关键词： Electromagnetic wave polarization

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DQ-RM: Deep Reinforcement Learning-based Route Mutation Scheme for Multimedia Services 16

DQ-RM: Deep Reinforcement Learning-based Route Mutation Sche...

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16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020

作者： Zhang, Tao Xu, Changqiao Zhang, Bingchi Kuang, Xiaohui Wang, Yue Yang, Shujie Muntean, Gabriel-Miro State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of International Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Science and Technology on Information System Security Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Performance Engineering Lab. School of Electronic Engineering Dublin City University Dublin Ireland

ISBN: (纸本)9781728131290

Increasingly growing various multimedia services (e.g., interactive live video and so on) have brought tremendous pressure on existing static defense techniques. To cope with inherent drawback of static defense techniques, network Moving Target Defense (NMTD) such as route mutation (RM) was proposed. What's more, applying reinforcement learning (RL) into RM has been proved feasible in our previous work. But two main problems still need to be considered in this combination of RL with RM: 1) It lacks the consideration of multiple flows situation. 2) With the state-action space grow larger, current solution can't handle efficiently. In this paper, we propose a deep Q-learning method for RM (DQ-RM) to solve above two problems. Firstly, benefited from the satisfiability module theory, we formalize RM space considering single flow and multiple flows concurrently. Then we further propose a deep reinforcement learning-based RM scheme based on our previous work, which is suitable for large-scale state-action space. Finally, extensive experimental results highlight the improvement of DQ-RM in defense performance and convergence speed compared to the representative solution. © 2020 IEEE.

关键词： Reinforcement learning

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A Metasurfaced Circularly-Polarized Antenna for Electronic Toll Collection system 3

A Metasurfaced Circularly-Polarized Antenna for Electronic T...

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3rd IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2020

作者： Chen, Junlong Feng, Botao Chung, Kwok L. Deng, Li College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Civionics Research Laboratory Qingdao University of Technology Qingdao266033 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781728190457

In this paper, a metasurfaced circularly-polarized (CP) antenna with a double-layer structure is proposed for the electronic toll collection (ETC) system. The antenna composed of dual layer of printed circuit board (PCB) with an overall height of 6 mm. The top-layer (Layer 2) is a metasurface that made with 4×4 unit-cells of chamfered square patches. The driven patch is printed with a U-slot at the bottom layer (Layer 1). The addition of metasurface helps to achieve narrow beam width with a high gain. The simulation results demonstrate that the proposed metasurfaced CP antenna exhibits a mandated right-hand narrow beamwidth in the desired ETC frequency band, whereas its broadside gain is obtained greater than 6.5 dBic. In addition, both the achieved impedance bandwidth and axial ratio bandwidth are sufficient to cover the entire ETC band (5.75 GHz-5.9 GHz). © 2020 IEEE.

关键词： Bandwidth

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Leveraging multiple legacy Wi-Fi links for human behavior sensing

arXiv

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arXiv 2021年

作者： Guo, Lingchao Lu, Zhaoming Wen, Xiangming Wang, Liming Gesbert, David Han, Zijun Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Communications Systems Department EURECOM Sophia Antipolis France

Taking advantage of the rich information provided by Wi-Fi measurement setups, Wi-Fi-based human behavior sensing leveraging Channel State Information (CSI) measurements has received a lot of research attention in recent years. The CSI-based human sensing algorithms typically either rely on an explicit channel propagation model or, more recently, adopt machine learning so as to robustify feature extraction. In most related work, the considered CSI is extracted from a single dedicated Access Point (AP) communication setup. In this paper, we consider a more realistic setting where a legacy network of multiple APs is already deployed for communications purposes and leveraged for sensing benefits using machine learning. The use of legacy network presents challenges and opportunities as many Wi-Fi links can present with richer yet unequally useful data sets. In order to break the curse of dimensionality associated with training over a too large dimensional CSI, we propose a link selection mechanism based on Reinforcement Learning (RL) which allows for dimension reduction while preserving the data that is most relevant for human behavior sensing. The method is based on a sequential state decision-making process in which the CSI is modeled as a part of the state. From actual experiment results, our method is shown to perform better than state-of-the-art approaches in a scenario with multiple available Wi-Fi links. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel state information

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Learning multiscale correlations for human motion prediction

arXiv

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arXiv 2021年

作者： Zhou, Honghong Guo, Caili Zhang, Hao Wang, Yanjun Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China China Telecom Dict Application Capability Center China

In spite of the great progress in human motion prediction, it is still a challenging task to predict those aperiodic and complicated motions. We believe that capturing the correlations among human body components is the key to understand the human motion. In this paper, we propose a novel multiscale graph convolution network (MGCN) to address this problem. Firstly, we design an adaptive multiscale interactional encoding module (MIEM) which is composed of two sub modules: scale transformation module (STM) and scale interaction module (SIM) to learn the human body correlations. Secondly, we apply a coarse-to-fine decoding strategy to decode the motions sequentially. We evaluate our approach on two standard benchmark datasets for human motion prediction: Human3.6M and CMU motion capture dataset. The experiments show that the proposed approach achieves the state-of-the-art performance for both short-term and long-term prediction especially in those complicated action category. We make codes publicly available at https://***/zhouhongh/MGCN. © 2021, CC BY-NC-ND.

关键词： Motion estimation

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Co-deployment of UAV and Ground Base Station for Data Collection in Wireless Sensor networks

Co-deployment of UAV and Ground Base Station for Data Collec...

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IEEE International Conference on Communications (ICC)

作者： Xinying Hu Yijun Guo Jianjun Hao Chenxi Li Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In wireless sensor networks (WSNs) enabled by fixed ground infrastructure, sink nodes located at the margin of the coverage area often suffer from bad communication channels. In this paper, we use UAV-carried aerial base station (ABS) to assist ground base station (GBS) for data collection from sink nodes in WSN, and focus on the co-deployment of ABS and GBS from aspects of coverage partition and bandwidth resource allocation, aiming at minimizing the total energy consumption for uplink transmission of all the sensor nodes in WSN. In particular, we first derive the estimated energy consumption with the sink nodes' density instead of their exact positions. Then, to tackle the non-convexity of the original problem, we decomposing it into two subproblems, which are alternately solved to iteratively improve the sum energy consumption. Numerical results show that the proposed design significantly reduces the energy consumption of all sink nodes as compared to benchmark schemes.

关键词： Wireless sensor networks Energy consumption Bandwidth Data collection Base stations Unmanned aerial vehicles Uplink

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Exploration of Simulation Teaching Mode for Intelligent Information network Course

Exploration of Simulation Teaching Mode for Intelligent Info...

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International Conference on networking and network Applications (NaNA)

作者： Yue He Hui Li Yiming Li Qiang Wu Yuefeng Ji Rentao Gu Shaobin Fan Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications (BUPT) Beijing China School of Information and Communication Engineering BUPT Beijing China Beijing Key Laboratory of Network System Architecture and Convergence BUPT Beijing China China Unicom China

With the rapid development of information technology, the society’s demand for information technology talents has become particularly high; how colleges and universities devote themselves to cultivating professional information technology talents is an important direction at present. However, the teacher’s evaluation method stays at the exam-oriented education. In the university education, in order to help students with basic professional knowledge to learn more professional knowledge and skills, and to help teachers understand the characteristics of students more objectively, this article discusses how to improve the existing teaching environment and simulation teaching mode. A simulation teaching platform for the “Intelligent Information network” course is designed with multiple modules for complex network construction. Through the online network simulation teaching platform and the feedback obtained from the platform’s use, different students’ different problems can be found objectively, and the combination of theory and engineering, the combination of new and old knowledge, and the simplicity of complex software can be realized.

关键词： Knowledge engineering Education Complex networks Software Information technology

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Deep learning for channel decoding under correlated noise

Deep learning for channel decoding under correlated noise

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2019 International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2019

作者： Zhang, Xiangxiang Luo, Tao Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

ISBN: (纸本)9789897583575

Traditional Viterbi decoding algorithm degrades performance under correlated noise. Inspired by the powerful learning from data of deep neural networks, we apply denoising convolutional neural network (DnCNN) to channel decoding under correlated noise. In this paper, we propose a DnCNN hard decision Viterbi decoder (short for DHDV decoder) architecture to enhance the performance of convolutional codes under correlated noise. The architecture applies DnCNN to denoise and improves the decoding SNR (signal to noise ratio) through the correlation of noise correlation. Then, the Viterbi Algorithm decoder decodes from the denoised data. DHDV decoder obtains greater BER performance improvement under stronger noise correlation, and it is robust under different convolutional codes and correlated noise models. Comparing the complexity of DnCNN network and matrix multiplication whitening method, DnCNN network complexity is lower when the code length is longer. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Viterbi algorithm

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Performance Analysis of Joint NOMA and JT-CoMP Based on Stienen Model

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5114-5128页

作者： Chen, Yunpei Haenggi, Martin Zhu, Qi Guo, Caili Yuan, Yifei Hu, Zhuhua Li, Xiaohui Hainan Tropical Ocean University School of Marine Information Engineering Yazhou Bay Innovation Institute Sanya572022 China Nanjing University of Posts and Telecommunications School of Communication and Information Engineering Nanjing210003 China China Information Consulting and Designing Institute Company Ltd Chief Engineer's Office Nanjing210000 China University of Notre Dame Department of Electrical Engineering Notre DameIN46556 United States Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China China Mobile Research Institute Future Mobile Technology Lab Beijing100053 China Hainan University School of Information and Communication Engineering Haikou570228 China

For fifth-generation wireless networks to transition to sixth-generation wireless networks, the integration of coordinated multipoint (CoMP) and non-orthogonal multiple access (NOMA) techniques is expected to overcome new challenges and enhance performance compared to the CoMP or NOMA scheme. The joint-transmission CoMP (JT-CoMP) technique is a typical technical implementation of the CoMP scheme. In this study, we investigate a downlink network with a joint JT-CoMP-NOMA scheme. Based on the generalized Stienen model from stochastic geometry, we divide far and near NOMA user equipment (UE) and develop a theoretical framework to analyze the system performance. Expressions for the coverage probabilities and average achievable rates of two types of UEs (named CoMP and non-CoMP UEs) are derived. By comparing analytical results with Monte Carlo simulations, we show that the approximations in the analytical derivations are tight. The impact of certain network parameters, such as the power allocation coefficient, on the system performance is also studied. Notably, the developed transmission scheme is shown to outperform the NOMA-only and the JT-CoMP-only schemes. © 2002-2012 IEEE.

关键词： Monte Carlo methods

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Dual Vote Confirmation based Consensus Design for Blockchain integrated IoT

Dual Vote Confirmation based Consensus Design for Blockchain...

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IEEE Symposium on network Operations and Management

作者： Zheng Qiu Jianjun Hao Yijun Guo Yi Zhang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728149738

ISBN: (纸本)9781728149745

Blockchain technology is adopted by many research to provide underlying security protection for the booming Inter-net of Things (IoT) industry. Nonetheless, due to some aspects of IoT, such as resource-constrained devices, heterogeneous mass devices, and real-time transactions, the traditional blockchain consensus algorithm is not suitable for the scene of blockchain-IoT integration. This paper proposes a Dual Vote Confirmation based Consensus (DVCC) mechanism for the Blockchain-IoT integration (BlockIoT). First, we adopt a role division approach to solve the problem of limited resources of devices in IoT. Second, multi-dimensional credit score, dual confirmation mechanism, classified random selection mechanism, and credit score based incentivization are designed to improve the security and fairness of the consensus. Third, a voting mechanism is proposed to improve the delay performance and avoid energy waste. Finally, the experimental results show that DVCC gains better delay and throughput performance on a large scale while guaranteeing the security and fairness of the consensus.

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