Updated on 2022/05/19

写真a

 
WEI, Bo
 
Affiliation
Faculty of Science and Engineering, Waseda Research Institute for Science and Engineering
Job title
Junior Researcher(Assistant Professor)

Education

  • 2015.04
    -
    2019.02

    Waseda University   Graduate School of Fundamental Science and Engineering  

  • 2012.09
    -
    2015.01

    Tianjin University   Graduate School of Computer Science and Communicaiton Engineering  

  • 2008.09
    -
    2012.06

    Tianjin University   School of Electrical and Electronic Engineering  

Research Experience

  • 2019.04
    -
    Now

    Waseda University   Faculty of Science and Engineering   Assistant Professor

  • 2020.02
    -
    2020.03

    University of Washington

Professional Memberships

  •  
     
     

    IEEE

  •  
     
     

    IEICE

 

Research Areas

  • Information network

  • Perceptual information processing

  • High performance computing

Research Interests

  • Computer Networks

  • Multimedia Communications

  • IoT

  • Deep learning

  • Quantum Computing

Papers

  • High-QoE DASH live streaming using reinforcement learning

    Bo Wei, Hang Song, Jiro Katto

    2021 IEEE/ACM International Symposium on Quality of Service (IWQoS)    2021.06

  • FRAB: A Flexible Relaxation Method for Fair, Stable, Efficient Multi-user DASH Video Streaming

    Bo Wei, Hang Song, Jiro Katto

    2021 IEEE International Conference on Communications (ICC)    2021.06

  • Performance Analysis of Adaptive Bitrate Algorithms for Multi-user DASH Video Streaming

    Bo Wei, Hang Song, Shangguang Wang, Jiro Katto

    2021 IEEE Wireless Communications and Networking Conference (WCNC)    2021.03

  • Blockchain-based data collection with efficient anomaly detection for estimating battery state-of-health

    Ruochen Jin, Bo Wei, Yongmei Luo, Tao Ren, Ruoqian Wu

    IEEE Sensors Journal    2021

     View Summary

    The number of electric vehicles in various countries has shown exponential growth so that the related industries to face the tremendous pressure of power batteries disposal. Efficient secondary use and recycling of power batteries require effective collection of battery data and reasonable estimation of battery state-of-health (SOH). In this paper, we propose a framework to collect battery charging data from different stakeholders with an anomaly detection method based on Isolation Forest with two features. Besides a score-based mechanism is adopted to do data screening and capture the data with good quality. Unlike prior works, our proposed method can exploit crowdsourced data to reduce the significant effort of battery data sensing and provide a data source scoring mechanism based on blockchain to improve the data quality and meet the requirement of reasonable estimation. In order to verify the effectiveness of the proposed collection method, a charge data test set is constructed based on the NASA battery data set. The simulation results indicate that the method increases the F-measure criteria up to 25.65% compared to the well-known anomaly detection algorithms. In addition, the proposed collection method outperforms the traditional method up to 10.9% in reducing the relative error when being used for SOH estimation.

    DOI

  • WiEps: Measurement of Dielectric Property with Commodity WiFi Device - An Application to Ethanol/Water Mixture

    Hang Song, Bo Wei, Qun Yu, Xia Xiao, Takamaro Kikkawa

    IEEE Internet of Things Journal   7 ( 12 ) 11667 - 11677  2020.12

     View Summary

    WiFi signal has become accessible everywhere, providing high-speed data transmission experience. Besides the communication service, channel state information (CSI) of the WiFi signals is widely employed for numerous Internet-of-Things (IoT) applications. Recently, most of these applications are based on the analysis of the microwave reflections caused by the physical movement of the objective. In this article, a novel contactless wireless sensing technique named WiEps is developed to measure the dielectric properties of the material, exploiting the transmission characteristics of the WiFi signals. In WiEps, the material under test is placed between the transmitter antenna and receiver antenna. A theoretical model is proposed to quantitatively describe the relationship between CSI data and dielectric properties of the material. During the experiment, the phase and amplitude of the transmitted WiFi signals are extracted from the measured CSI data. The parameters of the theoretical model are calculated using measured data from the known materials. Then, WiEps is utilized to estimate the dielectric properties of unknown materials. The proposed technique is first applied to the ethanol/water mixtures. Then, additional liquids are measured for further verification. The estimated permittivities and conductivities show good agreement with the actual values, with the average error of 4.0% and 8.9%, respectively, indicating the efficacy of WiEps. By measuring the dielectric property, this technique is promising to be applied to new IoT applications using ubiquitous WiFi signals, such as food engineering, material manufacturing process monitoring, and security check.

    DOI

  • Field Experiments of 28 GHz Band 5G System at Indoor Train Station Platform

    Mayuko Okano, Yohei Hasegawa, Kenji Kanai, Bo Wei, Jiro Katton

    2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020    2020.01

     View Summary

    Recently, a fifth-generation cellular system (5G) is widely expected to provide plenty of wireless network resources (i.e., broadband capacity). In this paper, to validate 5G system performances, such as physical-layer and TCP-layer throughputs, we carry out a field trial at an actual indoor train station, named Haneda International Airport Terminal Station. In the field trial, we deploy the prototype 5G system (Central Unit, Distribution Unit, Radio Unit and 5G UE (tablet)) on the train station platform and evaluate mobile 5G downlink throughputs. Through the actual measurements, the results confirm that the prototype 5G system can achieve mobile broadband capacity (more than 1 Gbps) even when the UE is located anywhere at the indoor train station platform.

    DOI

  • QoE Evaluation of Adaptive Video Streaming Algorithms in Multi-user Networks

    Bo Wei, Koji Kawakami, Hang Song, Bo Gu

    Proceedings - 2019 IEEE International Symposium on Multimedia, ISM 2019     235 - 236  2019.12

     View Summary

    Adaptive bitrate control (ABR) is an important technique for video streaming. This technique selects the video quality adaptively according to various network conditions, to ensure the quality of experience (QoE) for users. In the previous works, the ABR methods are mainly tested in single user environment. In this paper, an emulation testbed is constructed for QoE performance evaluation in multi-user networks. The state-of-the-art ABR methods are incorporated into the proposed environment. Emulation experiments are carried out to evaluate the performance of the methods. Preliminary results show that in FESTIVE, which has the least QoE variation in the six-user experiments, the user QoE of the worst case is only 27.5% of that of best case, demonstrating that the state-of-the-art ABR methods are not effective enough to optimize the QoE for all users under multi-user condition. Future design of the ABR method should take factors such as the fairness and resource allocation into consideration.

    DOI

  • TCP throughput characteristics over 5G millimeterwave network in indoor train station

    Mayuko Okano, Yohei Hasegawa, Kenji Kanai, Bo Wei, Jiro Katto

    IEEE Wireless Communications and Networking Conference, WCNC   2019-April  2019.04

     View Summary

    To realize highly reliable video surveillance and provide ultrahigh-definition/immersive video streaming, it is planned to adopt the 5G cellular system using millimeter-wave (mmWave) as the wireless-network infrastructure. However, mmWave communication has a challenging issue: mmWave communication is extremely sensitive to obstacles, such as walls, pillars, and even human bodies, and this issue easily increases the packet loss rates and round trip time (RTT) (or disconnection from the base station) due to a no line of sight (NLOS) environment. Therefore, in this work, 5G throughput performances were evaluates in an indoor train station by considering the effect of an NLOS environment caused by blockage by human bodies. In addition, to improve the robustness of TCP transmission in a high-RTT and high-packet-loss environment (e.g., an NLOS environment), a state-of-the-art TCP, TCP-FSO, was used. In the evaluations, the MATLAB 5G library was used to simulate the 5G environment, and a Linux software-based network emulator, Traffic Control, was used to emulate the 5G network. From the evaluations, it the 5G mobile throughput characteristics were confirmed in three different crowded patterns (low, middle, and high density), and the TCP-FSO advantage against CUBIC-TCP was validated.

    DOI

  • A highly accurate transportation mode recognition using mobile communication quality

    Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto

    IEICE Transactions on Communications   E102B ( 4 ) 741 - 750  2019.04

     View Summary

    To recognize transportation modes without any additional sensor devices, we demonstrate that the transportation modes can be recognized from communication quality factors. In the demonstration, instead of using global positioning system (GPS) and accelerometer sensors, we collect mobile TCP throughputs, received-signal strength indicators (RSSIs), and cellular base-station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming. In accuracy evaluations, we conduct two different field experiments to collect the data in six typical transportation modes (static, walking, riding a bicycle, riding a bus, riding a train and riding a subway), and then construct the classifiers by applying a support-vector machine (SVM), k-nearest neighbor (k-NN), random forest (RF), and convolutional neural network (CNN). Our results show that these transportation modes can be recognized with high accuracy by using communication quality factors as well as the use of accelerometer sensors.

    DOI

  • Evaluation of throughput prediction for adaptive bitrate control using trace-based emulation

    Bo Wei, Hang Song, Shangguang Wang, Kenji Kanai, Jiro Katto

    IEEE Access   7   51346 - 51356  2019

     View Summary

    Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.

    DOI

  • Methods for adaptive video streaming and picture quality assessment to improve QoS/QoE performances

    Kenji Kanai, Bo Wei, Zhengxue Cheng, Masaru Takeuchi, Jiro Katto

    IEICE Transactions on Communications   E102B ( 7 ) 1240 - 1247  2019

     View Summary

    This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.

    DOI

  • Parameter identification and state-of-charge estimation for Li-ion batteries using an improved tree seed algorithm

    Weijie Chen, Ming Cai, Xiaojun Tan, Bo Wei

    IEICE Transactions on Information and Systems   E102D ( 8 ) 1489 - 1497  2019

     View Summary

    Accurate estimation of the state-of-charge is a crucial need for the battery, which is the most important power source in electric vehicles. To achieve better estimation result, an accurate battery model with optimum parameters is required. In this paper, a gradient-free optimization technique, namely tree seed algorithm (TSA), is utilized to identify specific parameters of the battery model. In order to strengthen the search ability of TSA and obtain more quality results, the original algorithm is improved. On one hand, the DE/rand/2/bin mechanism is employed to maintain the colony diversity, by generating mutant individuals in each time step. On the other hand, the control parameter in the algorithm is adaptively updated during the searching process, to achieve a better balance between the exploitation and exploration capabilities. The battery state-of-charge can be estimated simultaneously by regarding it as one of the parameters. Experiments under different dynamic profiles show that the proposed method can provide reliable and accurate estimation results. The performance of conventional algorithms, such as genetic algorithm and extended Kalman filter, are also compared to demonstrate the superiority of the proposed method in terms of accuracy and robustness.

    DOI

  • Throughput Prediction Using Recurrent Neural Network Model

    Bo Wei, Mayuko Okano, Kenji Kanai, Wataru Kawakami, Jiro Katto

    2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018     88 - 89  2018.12

     View Summary

    To ensure good quality of experience for user when transmitting video content, throughput prediction can contribute to the selection of proper bitrate. In this paper, we propose a throughput prediction method with recurrent neural network (RNN) model. Experiments are conducted to evaluate the methods, and the results indicate that proposed method can decrease the prediction error by a maximum of 29.39% compared with traditional methods.

    DOI

  • Time synchronization with multiple-access data transmission protocol in underwater sensor networks

    Zhigang Jin, Ting Wu, Yishan Su, Shuo Li, Bo Wei

    2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018    2018.12

     View Summary

    Time synchronization of underwater acoustic sensor networks is an important prerequisite for the normal operation of multiple access transmission. However, the separated research of the two can easily lead to repeated sending of control packets, resulting in the waste of time and energy resources. Therefore, we propose time synchronization with multiple-access data transmission protocol (TSMP) in underwater sensor networks. In this protocol, time synchronization and multiple-access data transmission are combined to form an effective underwater data transmission system, reducing exchanged message and saving time and energy. In addition, there are no reference nodes as standard time. We are pursuing local time synchronization that only nodes that communicate with each other are synchronized. The time synchronization of TSMP is based on the Doppler method to calculate the relative speed between nodes. Considering the effect of the relative movement between nodes on the propagation delay, the accuracy of transmission delay is improved. Then, the receiver adds the local time to the value of total transmission delay. This allows the receiver and the sender to synchronize. Simulation results show TSMP has a better performance in terms of the packet delivery fraction, synchronization errors and energy efficiency.

    DOI

  • A deployment optimization mechanism using depth adjustable nodes in underwater acoustic sensor networks

    Zhigang Jin, Zhihua Ji, Yishan Su, Shuo Li, Bo Wei

    2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018    2018.12

     View Summary

    The past few years have witnessed a significant progress in the research of underwater acoustic sensor networks. However, most studies on network deployment can only achieve excellent coverage with isolated connectivity or excellent connectivity with coverage holes in the monitoring area. Both coverage and connectivity are important to guarantee the quality of monitoring in Underwater Acoustic Sensor Networks (UASNs). To achieve the joint optimization of network coverage and connectivity, we proposed a deployment optimization mechanism using depth adjustable nodes in UASNs. The sink nodes are evenly distributed on the water surface and act as cluster heads. The sensor nodes in a cluster are adjusted vertically to form the topology with the sink node as the root node so that all nodes in the network are connected. Network deployment is optimized by finding the optimal location of node, with the network topology remains the same as a constraint. Simulation results show that our proposed deployment mechanism can achieve higher coverage with all nodes connected.

    DOI

  • Machine Learning Based Transportation Modes Recognition Using Mobile Communication Quality

    Wataru Kawakami, Kenii Kanai, Bo Wei, Jiro Katto

    Proceedings - IEEE International Conference on Multimedia and Expo   2018-July  2018.10

     View Summary

    In order to recognize the transportation modes without any additional sensor devices, we propose a recognition method by using communication quality factors. In the proposed method, instead of Global Positioning System (GPS) and accelerometer sensors, we collect mobile TCP throughputs, Received Signal Strength Indicators (RSSIs), and cellular base station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming service. In accuracy evaluations, we conduct two different field experiments to collect the data in five typical transportation modes (static, walking, riding a bicycle, a bus and a train,) and then construct the classifiers by applying Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest (RF). Results conclude that these transportation modes can be recognized by using communication quality factors with high accuracy as well as the use of accelerometer sensors.

    DOI

  • HOAH: A hybrid TCP throughput prediction with Autoregressive Model and Hidden Markov Model for mobile networks

    Bo Wei, Kenji Kanai, Wataru Kawakami, Jiro Katto

    IEICE Transactions on Communications   E101B ( 7 ) 1612 - 1624  2018.07

     View Summary

    Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.

    DOI

  • TRUST: A TCP Throughput Prediction Method in Mobile Networks

    Bo Wei, Wataru Kawakami, Kenji Kanai, Jiro Katto, Shangguang Wang

    2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings    2018

     View Summary

    Throughput prediction is essential for ensuring high quality of service for video streaming transmissions. However, current methods are incapable of accurately predicting throughput in mobile networks, especially for moving user scenarios. Therefore, we propose a TCP throughput prediction method TRUST using machine learning for mobile networks. TRUST has two stages: user movement pattern identification and throughput prediction. In the prediction stage, the long short-term memory (LSTM) model is employed for TCP throughput prediction. TRUST takes all the communication quality factors, sensor data and scenario information into consideration. Field experiments are conducted to evaluate TRUST in various scenarios. The results indicate that TRUST can predict future throughput with higher accuracy than the conventional methods, which decreases the throughput prediction error by maximum 44% under the moving bus scenario.

    DOI

  • A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network

    Bo Wei, Wataru Kawakami, Kenji Kanai, Jiro Katto

    Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017   2017-January   374 - 375  2017.12

     View Summary

    Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput prediction method incorporating communication quality features using SVR (Support Vector Regression). By taking history of communication quality features such as historical throughput and Received Signal Strength Indication (RSSI) into consideration, the throughput prediction error can be decreased. We conduct experiments with the proposed method and compare the prediction accuracy with a variety of methods in different scenarios of various moving modes of users. Results show that the proposed model could predict throughput effectively in various scenarios and decrease throughput prediction errors by a maximum of 26.47% compared with other methods.

    DOI

  • Accuracy evaluations of human moving pattern using communication quality based on machine learning

    Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto

    2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017   2017-January   1 - 2  2017.12

     View Summary

    In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.

    DOI

  • History-based throughput prediction with Hidden Markov Model in mobile networks

    Bo Wei, Kenji Kanai, Jiro Katto

    2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016    2016.09

     View Summary

    Throughput prediction contributes a lot to adaptive bitrate control, adjusting the quality of video streaming accordingly to offer smooth media transmission and save energy at the same time. To solve the problem of throughput prediction for real time communication, this paper puts forward a new history-based throughput prediction method applying Hidden Markov Model in mobile networks. The main purpose of this method is to predict future throughput for real time communication in mobile network. Our novel approach utilizes Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM) to deal with history time series of throughput and judge fluctuation factor with total variance when predicting future throughput. By conducting experiments with the new methodology, we compare the accuracy of the proposed method with three other conventional prediction methods. Results show our proposed method could identify data fluctuation effectively and predict future 100s throughput with high accuracy in various situations.

    DOI

  • Chinese word segmentation algorithm based on pair coding

    Bingyi Zhang, Bo Wei, Jiancheng Chen, Jie Wei, Guozheng Rao

    Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology   38 ( 4 ) 526 - 530  2014.08

     View Summary

    To improve the segmentation velocity and storage efficiency of the Chinese word segmentation algorithm, this paper proposes a characteristic matching algorithm based on pair coding. The characteristic value is extracted from the Chinese character position. This method can support fuzzy matching and don't need match multi-character Chinese words, so the characteristic value extraction is extracted from the adjacent Chinese character position. In addition, the data compression method can contribute to reduce storage space and improve the performance of Chinese word segmentation.

  • ES-VBF: An energy saving routing protocol

    Bo Wei, Yong Mei Luo, Zhigang Jin, Jie Wei, Yishan Su

    Lecture Notes in Electrical Engineering   210 LNEE   87 - 97  2013

     View Summary

    Limited Energy is a challenge in Underwater Sensor Network (UWSN). To solve the energy problem in UWSN, this paper puts forward a new energy-aware routing algorithm, called Energy-Saving Vector Based Forwarding Protocol (ES-VBF). The main purpose of the new routing protocol is saving energy. ES-VBF puts both residual energy and localization information into consideration while calculating desirableness factor. By simulation, it shows that the ES-VBF algorithm increases the residue energy, reduces value of mean square error and prolongs the lifetime of network without worsening the packet reception ratio (PRR) apparently. © 2013 Springer-Verlag.

    DOI

  • P2P Botnets detection based on user behavior sociality and traffic entropy function

    Jin Zhigang, Wang Ying, Wei Bo

    2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet 2012 - Proceedings     1953 - 1955  2012

     View Summary

    Monitoring data of recent years from Symantec Company shows Botnet is becoming the base of all network crime. As P2P is being more widely used these days, some new Bots use P2P protocols to construct command and control system. This paper introduces Botnet detection methods, studies detection mechanism towards P2P Botnets based on user behavior, and proposes a new case to identify P2P Botnet. To test and verify function of the method we provide, a simple experiment platform is designed and implemented. © 2012 IEEE.

    DOI

  • SIP-based WIMAX wireless video surveillance system

    Yong Mei Luo, Bo Wei, Zhi Gang Jin

    Proceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012     1170 - 1173  2012

     View Summary

    This paper analyzes network video surveillance technology, and designs a wireless video surveillance system. A new SIP protocol is proposed for wireless network. Based on the new SIP, system interconnect is done. The video capture terminal is designed and implemented on TI DM365. Under WIMAX network, the wireless IP video surveillance system could work properly. Deployment and on site running of the system results show that it could work properly and robustly. © 2012 IEEE.

    DOI

▼display all

Misc

  • BS-6-28 Throughput prediction based on stochastic model of mobile network(BS-6.Network and service Design, Control and Management)

    Wei Bo, Kanai Kenji, Takenaka Sakiko, Katto Jiro

    Proceedings of the Society Conference of IEICE   2015 ( 2 ) "S - 68"-"S-69"  2015.08

    CiNii

Awards

  • The World Economic Forum Global Future Council

    2019   Fellow

  • Women Techmakers Scholar

    2018   Google  

  • Best Paper Award of English Session in IEICE Technical Committee on Information and Communication Management

    2016   IEICE  

  • Otsuki Memorial Scholarship for Asia and Africa Students

    2016   Japan  

  • Best Paper Award of English Session in IEICE Technical Committee on Information and Communication Management

    2015   IEICE  

  • China National Scholarship

    2014   China Ministry of Education  

▼display all

Research Projects

  • Adaptive bitrate control strategy for ensuring high-QoE and fair video streaming in multi-user networks

    JSPS 

Specific Research

  • Intelligent DASH video streaming in Wi-Fi networks assisted by the physical environment sensing technique with channel state information

    2021   甲藤二郎

     View Summary

    With the COVID19 pandemic, the live video streaming becomes more and more common in daily life such as live meeting and live video call, it is an urgent task to ensure high-quality and low-delay live video streaming service. Through this project, adaptive rate control method was proposed using reinforcement learning technique to control the live video streaming. Experiment results proved that the proposal shows the best performance with highest QoE compared with conventional methods in three network conditions. Another strategy was proposed based on Ising machine by using the quadratic unconstrained binary optimization (QUBO) method. Experiment results show that the proposed QUBO-based method outperforms the existing methods. To improve throughput and mitigate pilot contamination, an annealing-based pilot allocation method was proposed. Experiment results show that the proposed method can realize optimal pilot allocation and mitigate pilot contamination with higher minimum achievable rate and SINR, especially when the numbers of users and cells are large.

  • Intelligent DASH video streaming in Wi-Fi networks assisted by the physical environment sensing technique with channel state information

    2021   甲藤二郎

     View Summary

    With the COVID19 pandemic, the live videostreaming becomes more and more common in daily life such as live meeting andlive video call, it is an urgent task to ensure high-quality and low-delay livevideo streaming service. Through this project, adaptive rate control method was proposed using reinforcementlearning technique to control the live video streaming. Experiment results provedthat the proposal shows the best performance with highest QoE compared withconventional methods in three network conditions. Another strategy was proposedbased on Ising machine by using the quadratic unconstrained binary optimization (QUBO) method. Experiment results show thatthe proposed QUBO-based method outperforms the existing methods. To improve throughput and mitigatepilot contamination, an annealing-based pilot allocation method was proposed. Experiment results show that the proposed method canrealize optimal pilot allocation and mitigate pilot contamination with higher minimumachievable rate and SINR, especially when the numbers of users and cells arelarge.

  • Adaptive bitrate control strategy for ensuring high-QoE and fair video streaming in multi-user networks

    2020   Jiro Katto

     View Summary

    With the increasing video demand in dailynetwork traffic, it is an urgent task to develop effective algorithms tofacilitate high-quality content delivery service. Recently, numerous adaptivestreaming algorithms have been proposed to improve the user perceivedexperience. We tested the performance of current adaptation methods inmulti-user network. It is found that current algorithms perform inconsistentlyin various network scenarios. In the excessive user and limited bandwidthcases, machine learning and scheduling techniques show superiority in providinghigh and equal QoE for all users. While in the high-delay case, thebuffer-based approaches show robust performance. We also proposed a new client-sideABR control method, flexible relaxation assisted by buffer (FRAB), to achievefair, stable and efficient video streaming among different users. FRAB isevaluated in real experiments under different network conditions and comparedwith conventional multi-user ABR algorithms. The experiment resultsdemonstrated that the proposed method has superior performances in multi-userDASH video streaming.

 

Committee Memberships

  • 2021.03
    -
    Now

    IEEE CTSoc MDT Technical Committee  Member

Academic Activities

  • IEEE Internet of Things Journal

  • IEEE Transactions on Vehicular Technology

  • China Communications

  • IEEE Access