論文 - 大槻　知明

Adaptive DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems

Gao J., Bouazizi M., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Accessing the accurate downlink channel state information (CSI) is essential to take full advantage of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its weak channel reciprocity. Meanwhile, great computational burdens will happen, which is accompanied by continuous CSI feedback. The existing compressive sensing (CS)-based and deep learning (DL)-based methods try to solve such problems, but do not achieve desired effect to get ideal CSI feedback or decrease the overhead. An adaptive deep neural network (DNN)-based CSI feedback method is proposed in this paper to address this. A classification block of the compression ratio is adopted and modified to apply to a more complex channel model named Clustered-Delay-Line (CDL), which helps decrease the computational overhead of the network. Besides, the reconstruction accuracy of the CSI feedback is further improved by proposing a new structure of the encoder. Quantization and dequantization modules are also applied to make the whole network more robust and effectively minimize the quantization distortion in the real communication scenario, respectively. The simulation results show that the proposed method performs better than the conventional ones on the CSI reconstruction accuracy in terms of normalized mean square error (NMSE), even though the quantization module is added.

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A Semi-Supervised Federated Learning Scheme via Knowledge Distillation for Intrusion Detection

Zhao R., Yang L., Wang Y., Xue Z., Gui G., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2022-May   2688 - 2693 2022年

ISSN  15503607

　概要を見る

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. However, most of the current FL-based intrusion detection methods still suffer from three limitations: (1) model parameters transmitted in each round may be used to recover private data which leads to security risks, (2) not independent and identically distributed (non-IID) private data seriously adversely affects the training of FL (especially distillation-based FL), and (3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this paper develops an intrusion detection method based on semi-supervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a CNN-based model for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, discriminator is designed to improve the quality of each client's predicted labels, to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of hard-label strategy and voting mechanism further reduces communication overhead. Experimental results on the real-world traffic dataset show that our proposed method can achieve better classification performance as well as lower communication overhead than state-of-the-art methods.

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A Multi-Layer Hybrid Network With Its Application in Fetal Heart Rate Monitoring

Wang L., Ohtsuki T., Owada K., Honma N., Hayashi H.

IEEE Signal Processing Letters （IEEE Signal Processing Letters）  29   1207 - 1211 2022年

ISSN  10709908

　概要を見る

Fetal heart rate monitoring is an enormous challenge since the observed fetal electrocardiography (ECG) signal is typically characterized by a very low signal-to-noise ratio (SNR). In this letter, we aim to improve the accuracy of heartbeat detection by proposing an adaptive template for removing the maternal cycle. The template is formed by a matrix, each row of which consists of an abdominal recorded signal (ADS). It can be updated by integrating the incoming cycle while removing the contribution of the previous recording. This process is conducted by considering a discriminator to adapt the non-stationarity of each incoming cycle. Furthermore, to suppress the morphological change caused by noise, we propose a novel multi-layer hybrid network to reconstruct the chest maternal ECG (chest mECG) morphology from a set of templates. The approach has a deep structure of each layer consisting of a reservoir layer and an encoder layer. The reservoir layer explores multi-scale dynamics by transforming the input series into a high-dimensional space. The encoder layer achieves the collection of the encoder features from the output of the reservoir layer. Once the model is built, the output weight of a direct connection is trained by solving a regression problem. Experimental results show that the proposed method has a better performance compared with some classical approaches.

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Fetal Arrhythmia Detection based on Deep Learning using Fetal ECG Signals

Nakatani S., Yamamoto K., Ohtsuki T.

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     2266 - 2271 2022年

ISSN  23340983

　概要を見る

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmia. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results showed that the proposed method could outperform the conventional methods with heartbeat detection and feature detection in terms of accuracy, specificity, and recall.

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Unsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment

Shi X., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     2254 - 2259 2022年

ISSN  23340983

　概要を見る

The Doppler ultrasound (DUS) transducer has been widely used for fetal heart rate (FHR) monitoring. However, the fetal DUS signals from the transducers can be corrupted by several interference sources such as maternal and fetal movements, which makes FHR estimation using fetal DUS signals challenging. Fetal DUS signal quality assessment (SQA) can help to remove or interpolate unreliable FHRs estimated from noisy signals to improve the accuracy of FHR estimation. There are some existing approaches for fetal DUS SQA, and most of these approaches with high accuracy are based on supervised learning-based algorithms and human-defined properties. Nonetheless, the fetal DUS datasets with quality-level annotations are limited, and human-defined properties place a limitation on mining more deep information related to signal quality in fetal DUS signals. In this paper, we propose an unsupervised representation learning-based fetal DUS SQA for the improvement of FHR estimation performance. We firstly learn representations of pre-processed fetal DUS data from variational autoencoder (VAE) and then combine these representations as one signal quality index (SQI) using a self-organizing map (SOM). Finally, we apply the combined SQI and a Kalman filter (KF) to estimate fetal RR intervals (FRRI) for reducing the errors of FHR estimation. The experimental results showed that our proposed method could reduce the averaged root mean squared error (RMSE) of FRRI and averaged absolute error (AAE) of FHR.

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Two-Step Dynamic Cell Optimization Algorithm for HAPS Mobile Communications

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Access （IEEE Access）  10   68085 - 68098 2022年

　概要を見る

High altitude platform stations are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Multi-cell configurations can increase communication capacity in a wide area. Conventional work optimizes the cell configuration for a multi-cell configuration based on a genetic algorithm (GA). This method identifies an optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution. However, user distributions differ depending on location; thus, cell configuration optimization is also required for non-uniform user distributions. The problem is an increased number of parameters because each cell requires different antenna parameters for non-uniform user distribution compared to a uniform user distribution. This increases the difficulty of optimization, even when using a GA in some cases. Therefore, we propose a GA-based two-step cell optimization algorithm that comprises both search space reduction and antenna parameter optimization steps to address this problem. The proposed method employs the concept of co-evolution, i.e., a divide-and-conquer method. The proposed method divides multiple cells into several groups to reduce the number of optimized parameters and optimizes each group in order. In addition, the search space is reduced based on a marginal histogram obtained via optimization with a large step size. Simulation results demonstrate that this technique can reduce the number of combinations compared to the case without sub-area division. In addition, there are cases where the search space reduction further reduces the number of combinations without degrading the sum of the square root of throughput.

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Social Robot Detection Using RoBERTa Classifier and Random Forest Regressor with Similarity Analysis

Chen Y., Bouazizi M., Ohtsuki T.

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     6433 - 6438 2022年

ISSN  23340983

　概要を見る

Twitter has skyrocketed over the past few years and has become a major social media platform. At the same time, the number of social robots on Twitter has also increased significantly. These bot accounts imitate the speeches of normal users to manipulate public opinions, affect the normal communication of users. Therefore, bot account detection came into being. Despite extensive research efforts, bots on Twitter are still evolving to evade detection. Most of the current bot detection methods have a single structure and cannot detect and identify different types of bot accounts well. In this paper, we propose a new system for social robot detection that uses a RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach) classifier and a random forest regressor with similarity analysis. In particular, the system considers the similarity of tweets and uses a voting system in addition to a set of features extracted from the user profile information and the tweets themselves. We conduct experiments using the largest dataset of bots available and show that the accuracy of our system is up to 0.8588, which is higher than that of all the other baseline methods.

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Smartphone-Aided Human Activity Recognition Method using Residual Multi-Layer Perceptron

Shi S., Wang Y., Dong H., Gui G., Ohtsuki T.

INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops （INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops）  2022年

　概要を見る

Human activity recognition (HAR) has received intensely attention in many applications, such as healthcare, human-computer interaction, and smart home. Existing HAR methods based on deep learning (DL) have been proposed in the last several years. However, these DL-based HAR methods are hard to balance between performance and cost, which truly limited the applications in practical scenarios. To solve this problem, this paper proposes a smartphone-aided HAR method using the residual multi-layer perceptron (Res-MLP). It composes of two linear layers and Gaussian error linear unit (GELU) activation function, and obtains Res-MLP network through residual. Experimental results show that the proposed HAR method can achieve a high classification accuracy of 96.72% based on the public UCI HAR dataset.

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Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?

Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  10   55958 - 55987 2022年

　概要を見る

Sarcasm is a sophisticated form of speech used to convey a message other than the apparent one. To date, there are numerous papers that have discussed the idea of automatic sarcasm detection and how it could be used for sentiment analysis improvement. The objective of this paper is to provide non-experts with a comprehensive overview of the state of research in this field and the main findings regarding sarcasm detection. Therefore, in this paper, we survey the state-of-the-art work done in this field, we recapitulate the research effort done, with focus on the more recent works, and we present the expected performance out of the proposed works. Nevertheless, we study in detail how this form of speech is used in different platforms, and how the way we express it evolves over time. We also discuss the proposition that suggests that sarcasm is a polarity switcher for sentiment analysis. To achieve these goals, we run some experiments on 3 different data sets, collected from 3 different platforms, and compare how sarcasm is employed in each. These platforms are Twitter, Reddit, and some news websites. Our experiments show that the way sarcasm is expressed is highly dependent on language mastery and the platform used. For instance, in the Twitter data set, whose users vary widely in age, language mastery, and understanding what sarcasm means, the overall precision of detection of sarcastic statement reaches 89.31%. In the reddit data set, the precision of detection of such statements is about 55.33%, and in the news data set, the precision reaches over 96.67%. Our experiments also show that, to a great extent, it is safe to affirm that sarcasm, when employed, switches the polarity of a given piece of text: for the 3 platforms presented above, sarcasm has been a polarity switcher for 89.3%, 89.1%, and 92.0% of their respective instances.

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Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach

Shi X., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   1296 - 1299 2022年

ISSN  1557170X

　概要を見る

The non-invasive fetal electrocardiogram (FECG) derived from abdominal surface electrodes has been widely used for fetal heart rate (FHR) monitoring to assess fetal well-being. However, the accuracy of FECG-based FHR estimation heavily depends on the quality of FECG signal itself, which can generally be affected by several interference sources such as maternal heart activities and fetal movements. Hence, FECG signal quality assessment (SQA) is an essential task to improve the accuracy of FHR estimation by removing or interpolating low-quality FECG signals. In recent research, various SQA methods based on supervised learning have been proposed. Although these methods could perform accurate SQA, they require large labeled datasets. Nevertheless, the labeled datasets for the FECG SQA are very limited. In this paper, to address this limitation, we propose an unsupervised learning-based SQA method for identifying high and low-quality FECG signal segments. Specifically, a fully convolutional network (FCN)-based autoencoder (AE) is trained for reconstructing a spectrogram derived from FECG. An AE-based feature related to reconstruction error is then calculated to identify high and low-quality FECG segments. In addition, entropy-based features, statistical features, and ECG signal quality indices (SQIs) are also extracted. The high and low-quality segments are identified by feeding the extracted features into self-organizing map (SOM). The experimental results showed that our proposal achieved an accuracy of 98% in high and low-quality signal classification.

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Non-contact Blood Pressure Estimation Method Based on Blood Pressure Category Classification

Ishizaka S., Yamamoto K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   2676 - 2679 2022年

ISSN  1557170X

　概要を見る

In recent years, non-contact Blood Pressure (BP) measurement has been attracting attention for measuring our health status in daily life. A Doppler radar can observe pulse waves caused by chest wall displacement due to heartbeat. BP can be estimated by constructing a BP estimation model using BP-related features obtained from the pulse wave. However, compared to when modeling for each subject, the BP esti-mation accuracy deteriorates significantly when modeling with multiple subjects including the testing subject. To deal with this limitation, BP category classification has been introduced into PhotoPlethysmoGraphy (PPG)-based BP estimation. In this paper, we develop a Doppler radar-based BP estimation method based on BP category classification. In the proposed method, the pulse waves extracted from a Doppler radar are classified into three categories, 'Low BP', 'Normal BP', and 'High BP' by k-Nearest Neighbor (kNN) based on the features that correlate with BP. The SBP estimation model is trained for each BP category. After the BP category classification, SBP is then estimated by using the model corresponding to the classified BP category. The experimental results showed that the proposed method with BP category classification estimated SBP accurately, compared to without BP category classification.

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Multibeam Doppler Sensor-Based Non-Contact Heartbeat Detection Using Beam Diversity

Kitagawa T., Yamamoto K., Endo K., Ohtsuki T.

IEEE Access （IEEE Access）  10   16242 - 16253 2022年

　概要を見る

Heartbeat detection could enable various applications in the medical and health care fields. In particular, non-contact heartbeat detection can be acceptable for those who have difficulty wearing devices, such as burn patients and infants. A Doppler sensor could be a key device to realize heartbeat detection without any wearable devices. Many researches in recent years have focused on heartbeat detection using a Doppler sensor with a single beam Doppler sensor. However, when the SNR (Signal-to-Noise Ratio) of heartbeat components is low for the beam direction, the heartbeat detection accuracy is likely to degrade. In this paper, for more accurate heartbeat detection, we propose a heartbeat detection method based on beam diversity using a multibeam Doppler sensor. Through the preliminary experiments, we clarified that the SNRs of heartbeat components differ from one beam to another. This means that when the SNR is low for one beam, the SNR could be high for the other beams. Inspired by this fact, the proposed method extracts heartbeat components from all peaks detected by the multi-beam signals. To verify the heartbeat detection accuracy of our method, we conducted the experiments for different detection ranges. The obtained results show that compared to the conventional methods using a single beam, our proposed method using multibeam detected heartbeat more accurately. This indicates the benefit of exploiting beam diversity, i.e., the heartbeat detection accuracy can be improved by utilizing beam diversity.

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Max-Min Fair 3D Trajectory Planning for Solar-Powered UAV-Assisted Data Collection

Sun C., Xiong X., Ni W., Ohtsuki T., Wang X.

2022 IEEE/CIC International Conference on Communications in China, ICCC 2022 （2022 IEEE/CIC International Conference on Communications in China, ICCC 2022）     610 - 615 2022年

　概要を見る

This paper presents a new solar-powered fixed-wing unmanned aerial vehicle (UAV)-assisted data collection technique, where a fixed-wing UAV harvests solar energy to fly and collect data from smart devices (SDs). The minimum of the data upload-ed from any of the SDs is maximized by optimizing the UAV's three-dimensional (3D) trajectory. The key idea is that we develop variable substitution and successive convex approximation (SCA) techniques to convexify the nonconvex data transmission, UAV energy consumption and mobility constraints, as well as the nonconvex energy harvesting constraint. The proposed algorithm guarantees a locally optimal solution satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that an effective set of approximation parameters are identified for accurate approximation of the solar energy harvesting at relatively low altitudes, contributing to the design of efficient 3D trajectories balancing energy harvesting and data collection. Numerical results show that the 3D flight increases uploaded data by 59% as compared to the two-dimensional (2D) flight.

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Joint Weighted and Truncated Nuclear Norm Minimization for Matrix Completion-Assisted mmWave MIMO Channel Estimation

Li Y., Liu J., Chen C., Gui G., Ohtsuki T., Sari H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-June 2022年

ISSN  15502252

　概要を見る

Matrix completion-assisted channel estimation is considered one of promising techniques in millimeter wave (mmWave) massive multiple input multiple output (MIMO) system by exploiting the low-rank property of channel matrix in the angle domain. However, existing channel estimation approaches are hard to achieve high accuracy due to the inevitable bias solution caused by nuclear norm based minimization (NNM). To address this problem, this paper proposes a novel matrix completion-assisted mmWave massive MIMO channel estimation method. We employ an effective and flexible rank function named joint weighted and truncated nuclear norm as relaxation of nuclear norm, and then construct an novel matrix completion model for channel estimation problem. Moreover, a popular framework of alternating direction method of multipliers (ADMM) is derived for minimization of the resulting optimization problem. Simulation results are provided to verify the proposed method that can flexibly and effectively improve the channel estimation accuracy with reliable convergence.

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Heartbeat Detection Using 3D Lidar and MIMO Doppler Radar

Bouazizi M., Yamamoto K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2022-May   3040 - 3045 2022年

ISSN  15503607

　概要を見る

Non-contact measurement of the heart rate (HR) refers to the usage of wireless sensors (e.g., Doppler sensors) to identify the heart-related faint movements and to reconstruct the R-peaks or estimate the HR. Conventional work in the field requires the chest of the subject being monitored to be in a specific position in front of the sensor. In our previous work, we have proposed to use a Multiple-Input Multiple-Output (MIMO) Doppler Radar to perform the detection of heartbeats even when the Signal-to-Noise Ratio (SNR) is not very high. By exploiting the fact that different beams have different SNR of heartbeat components, our previous method detects heartbeats by aggregating several received signals. In general, it is essential to use beam directions toward the a subject's chest. However, the chest location estimation is challenging, when there exist several subjects or other moving objects. In the current paper, to deal with this issue, we narrow down the direction of beams to use by using a system composed of a MIMO Doppler sensor and a Light Detection and Ranging (LiDAR) device. By means of transfer learning and automatic annotation of data, the LiDAR is trained to identify the relative chest angle and distance to the subject from the devices, while the MIMO Doppler Radar can adjust the beam towards the identified direction. Throughout experiments, we show the effectiveness of the combination of both the Lidar and radar in detecting the direction and distance to the subject as well as the heartbeat.

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A Reinforcement Learning-based Fire Warning and Suppression System Using Unmanned Aerial Vehicles

F.H. Panahi, F.H. Panahi, and T. Ohtsuki

IEEE Transactions on Instrumentation and Measurement 2022年

研究論文（学術雑誌）, 共著, 最終著者

Graph Learning Empowered Situation Awareness in Internet of Energy with Graph Digital Twin

L. Sui, X. Guan, C. Cui, H. Jiang, H. Pan and T. Ohtsuki

in IEEE Transactions on Industrial Informatics 2022年

研究論文（学術雑誌）, 共著, 最終著者

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

K. Endo, K. Yamamoto, and T. Ohtsuki

Sensors 2022 2022年

研究論文（学術雑誌）, 共著, 最終著者

Downlink CSI Feedback Algorithm with Deep Transfer Learning for FDD Massive MIMO Systems

Zeng J., Sun J., Gui G., Adebisi B., Ohtsuki T., Gacanin H., Sari H.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  7 （ 4 ） 1253 - 1265 2021年12月

　概要を見る

In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by fine-tuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a model-agnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE).

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Joint UL/DL Resource Allocation for UAV-Aided Full-Duplex NOMA Communications

Shi W., Sun Y., Liu M., Xu H., Gui G., Ohtsuki T., Adebisi B., Gacanin H., Adachi F.

IEEE Transactions on Communications （IEEE Transactions on Communications）  69 （ 12 ） 8474 - 8487 2021年12月

ISSN  00906778

　概要を見る

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different directions in FD-NOMA systems. Firstly, a joint optimization problem is formulated for the uplink and downlink resource allocation of transceivers and UAV relay. The receiver determination is performed using an access-priority method. Based on the results of the receiver determination, the initial power of ground users (GUs), UAV, and base station is calculated. According to the minimum sum of the uplink transmission power, the Hungarian algorithm is utilized to pair the users. Secondly, the subchannels are assigned to the paired GUs and the UAV by a message-passing algorithm. Finally, the transmission power of the GUs and the UAV is jointly fine-tuned using the proposed access control methods. Simulation results confirm that the proposed method achieves higher performance than state-of-the-art orthogonal frequency division multiple-access method in terms of spectrum efficiency, energy efficiency, and access ratio of the ground users.

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