論文 - 大槻　知明

Secure information sharing with mirroring null steering through untrusted relay with two antennas

Noguchi T., Takyu O., Fujii T., Ohtsuki T., Sasamori F., Handa S.

IEEE Radio and Wireless Symposium, RWS （IEEE Radio and Wireless Symposium, RWS）  2018-January   203 - 205 2018年02月

ISSN  9781538607091

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© 2018 IEEE. This paper proposes the novel secure information sharing among wireless nodes by the physical layer security based on multi-antenna null steering. For secure wireless communication, the secure information like the common key of cypher should be shared among nodes before data transmission. However, in wireless adhoc network, the untrusted relay node may steal the data under relay process. In the proposed sharing, a source node and two sink nodes send the information conveying signal and the artificial noises to relay station, simultaneously for avoiding decoding the information in the untrusted relay. After amplifying and forwarding relay, the two nodes do not decode the information from the received signal without removing artificial noises. The untrusted relay constructs the null steering with two antennas for removing the artificial noise. Since the channel transfer functions through which the two artificial noises are passed are common, the null points to two sink nodes can be constructed. As a result, the two nodes can remove the artificial noises. This null steering is mirroring null steering. The computer simulation shows the effect of proposed information sharing in terms of secure capacity.

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Design of privacy-preserving mobile bitcoin client based on γ-deniability enabled bloom filter

Kanemura K., Toyoda K., Ohtsuki T.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC （IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC）  2017-October   1 - 6 2018年02月

ISSN  9781538635315

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© 2017 IEEE. Bitcoin is a decentralized currency system that does not need any central authorities. All transactions issued by users have been recorded in the common ledger, called blockchain, which is shared by all users. In Bitcoin, an SPV (Simplified Payment Verification) client, which is a lightweight client that does not possess the entire blockchain, are developed for storage constrained devices such as a mobile phone. For an SPV client to check if there are transactions related to it, a Bloom filter where their Bitcoin addresses are involved is sent to a full client that possesses the entire blockchain. The full client only transfers transactions of which Bitcoin addresses are positive on the received Bloom filter. However, it is necessary to preserve the privacy of SPV clients when designing a Bloom filter because SPV clients' Bitcoin addresses will be identified by a full client with high probability. In this paper, we propose a privacy-preserving Bloom filter design for SPV clients based on γ-Deniability. γ-Deniability is a privacy metric that shows how much true positive Bitcoin addresses are hidden by the false positives in a Bloom filter. Furthermore, in order to design a Bloom Filter that satisfies a certain γ-Deniability, it is necessary to know the number of unique Bitcoin addresses that appear for the first time since the queried time. Based on our manual inspection, we propose to estimate it based on the linear regression. We show that our scheme achieves good estimation accuracy and γ through the simulation with a real Bitcoin blockchain.

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Green heterogeneous networks via an intelligent power control strategy and D2D communications

Panahi F., Panahi F., Hattab G., Ohtsuki T., Cabric D.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC （IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC）  2017-October   1 - 8 2018年02月

ISSN  9781538635315

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© 2017 IEEE. Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

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Driver's blink detection using doppler sensor

Yamamoto K., Toyoda K., Ohtsuki T.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC （IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC）  2017-October   1 - 6 2018年02月

ISSN  9781538635315

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© 2017 IEEE. Blink is a physiological signal that reflects drowsiness and concentration. It is important to detect driver's blinks without any wearable devices. For this purpose, a Doppler sensor has been used and several blink detection methods where a subject sits in front of such sensor have been proposed. However, it is challenging to detect driver's blinks because of face and body movement. In this paper, we propose a Doppler sensor-based driver's blink detection method in existence of face and body movement in a car. In the proposed method, blinks are detected through two steps: pre-detection and classification. In the first step which we call pre-detection, the time candidates of subject's blinks are detected based on spectrograms calculated from a received signal. Then, in the second one which we call classification, a set of features are calculated from a spectrogram and are fed into a supervised machine learning classifier to identify which time candidates are truly blinks. We leverage the fact that the distribution of the energy on a spectrogram differs between a blink and non-blink. Specifically, features are extracted based on the distribution of energy on a spectrogram. We conducted a series of experiments for the evaluation in the situation where a subject drives a real car in public road. As a result, we confirmed our method outperforms the conventional one in terms of F-measure calculated from recall rate and precision rate.

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Antenna parameters optimization in self-organizing networks: Multi-armed bandits with Pareto search

Dhahri C., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2017-September   1 - 5 2018年02月

ISSN  9781509059355

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© 2017 IEEE. With the huge increases in traffic volumes and subscribers, diverse devices, and rich media applications, manual management of mobile network becomes highly challenging in terms of optimization and management. Self-organizing networks (SON) has been introduced to optimize the network in an automatic manner. In this paper, we address the coverage and capacity joint optimization (CCO) by adaptively and simultaneously adjusting both antenna tilt and power. To this end, we propose: · a multi-player multi-armed bandit (MAB) framework (decentralized restless upper confidence bound (RUCB) algorithm) with a change point detection test based on Page-Hinkley (PH) statistics used to decide whether some change has occurred in the environment. Then, the strategy is designed to deal with such a change. · a central unit to deal with simultaneous conflicting actions when many cells decide to start the optimization process at the same time. · a Pareto search framework to deal with multi-objective optimization (CCO). To evaluate our work, we compared our proposal with the fixed antenna parameter scheme and with the linear scalarization function that transforms the multi-objective optimization problem into a scalar function. Simulation results show that the proposed method could improve user experience in terms of cell-center capacity and cell-edge coverage compared to different conventional methods and under different number of users.

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Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection

大槻 知明

IEEE Access （IEEE Access）  6   13825 - 13835 2018年

研究論文（学術雑誌）,  ISSN  2169-3536

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© 2018 IEEE. With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds has become more direct, resulting in more and more 'cyber' conflicts between these people. Consequently, hate speech is used more and more, to the point where it has become a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. In this paper, we propose an approach to detect hate expressions on Twitter. Our approach is based on unigrams and patterns that are automatically collected from the training set. These patterns and unigrams are later used, among others, as features to train a machine learning algorithm. Our experiments on a test set composed of 2010 tweets show that our approach reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or not (binary classification), and an accuracy equal to 78.4% on detecting whether a tweet is hateful, offensive, or clean (ternary classification).

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Superposed Multicarrier Transmission

大槻 知明

ITE Transactions on Media Technology and Applications ６ （ 1 ） 91 - 97 2018年

研究論文（学術雑誌）, 単著, 査読有り

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A closed-form expression for performance optimization of subcarrier intensity QAM signals-based relay-added FSO systems with APD

Trung H., Tien Hoa N., Huu Trung N., Ohtsuki T.

Physical Communication （Physical Communication）  31   203 - 211 2018年

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  18744907

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© 2018 Elsevier B.V. This study characterizes the overall bit-error-rate (BER) performance optimization of avalanche photodiode (APD) receiver-based amplify-and-forward (AF) relay-added free-space optical (FSO) communication system. The system of interest uses subcarrier intensity quadrature amplitude modulation (QAM) signals and subjects to scintillation due to optical turbulence. A weak atmospheric turbulence (clear-air condition) scenario is considered, for which the received signal intensity fluctuation can be modeled as a log-normal random process. A closed-form expression of the overall BER is derived by taking into account the impact of atmospheric loss, turbulence strength, thermal-noise and shot-noise limited conditions. It can be inferred from the numerical results that using AF relay nodes can extend the transmission distance and minimize BER performance of FSO system significantly compared with the direct transmission. Moreover, the selection of APD's gain values is indispensable to minimize the system's BER performance. Hence, the designed system could achieve the minimal BER by selecting an optimal APD gain value appropriately. In addition, the optimal gain values of APD significantly depend on various conditions, such as noise temperature, link distance, and the number of relay nodes.

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Signal Restoration Based on Temporal Structure and Multi-Layer Architecture

Wang L., Ohtsuki T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

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© 2018 IEEE. Signal restoration involves the removal or minimization of degradation such as attenuation, interference, and noise. Blind signal restoration is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. In this paper, we present a novel approach to tackle the ill-posedness of the nonlinear blind source separation problem. The derivation of our algorithm is inspired by the idea of an efficient layer-by-layer representation to approximate the nonlinearity. Once such representations are built, a final output layer is constructed by solving a convex optimization problem. Thus, the projected data can break a nonlinear problem down into the version of generalized joint diagonalization problem in the feature space. Importantly, the parameters and forms of polynomials depend solely on the input data, which guarantee the robustness of the structure. We thus address the general problem without being restricted to any specific mixture or parametric model. Experimental results show that the proposed algorithm is able to recover the nonlinear mixture with higher separation accuracy on audio datasets from the real world.

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Non-Contact Heartbeat Detection by MUSIC with Discrete Cosine Transform-Based Parameter Adjustment

Yamamoto K., Toyoda K., Ohtsuki T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

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© 2018 IEEE. Heartbeat detection are receiving a lot of attention in the field of health care, since cardiac activity reflects various information of a subject, e.g., the stress. Many Doppler sensor-based heartbeat detection methods have been proposed so far. As one of such methods, the MUSIC (MUltiple SIgnal Classification)-based HR (Heart Rate) estimation method has been proposed. However, the conventional MUSIC-based HR estimation method not only needs a long time window, but also requires to know the number of sinusoidal signals composing the analyzed signals, P, in advance of applying MUSIC to the analyzed signal, which is challenge. In this paper, we propose a MUSIC-based HR estimation method with the DCT (Discrete Cosine Transform)-based parameter P selection. In the proposed method, the analyzed signal is firstly decomposed by DCT. The inverse DCT is then performed based on only components that might be related with heartbeats. The number of components used in the inverse DCT is selected as P. Through the experiments, we confirmed that our method outperformed the conventional one by the estimation accuracy of the HR and the stress indexes such as CVI (Cardiac Vagal Index) and CSI (Cardiac Sympathetic Index).

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Learning-Based Optimal Channel Selection in the Presence of Jammer for Cognitive Radio Networks

Li A., Panahi F., Ohtsuki T., Han G.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

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© 2018 IEEE. Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

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Fall Detection Using UHF Passive RFID Based on the Neighborhood Preservation Principle

Kamoi H., Toyoda K., Ohtsuki T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

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© 2018 IEEE. Injuries caused by falls are one of the major threats for the elderly. Thus, the demand for a fall detection system without requiring a user to wear or equip any devices is rapidly increasing. In this paper, a fall detection system using Ultra-High Frequency (UHF) passive Radio-Frequency IDentification (RFID) is presented. With the RFID reader on the ceiling and multiple RFID tags on the floor, anomaly scores are computed based on the neighborhood preservation principle. This focuses only on the correlation between RSSI fluctuations between a pair of tags. Therefore, unlike the conventional methods, our method manages to detect falls without requiring a large number of reference data. To detect falls in several areas of the room, our system only requires a simple reference data with one subject walking around the room. Experiments are conducted in an indoor environment with two different setups to show the effectiveness of our method. Our method improves the conventional one, and achieves a high Area Under Curve (AUC) of 0.98.

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Enhanced Channel Hopping Algorithm for Heterogeneous Cognitive Radio Networks

Li A., Han G., Ohtsuki T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

　概要を見る

© 2018 IEEE. In Cognitive Radio Networks (CRNs), the available channels for the unlicensed Secondary Users (SUs) may be varying. When SUs want to communicate with each other, they must first access the same channel simultaneously. The process of accessing the same channel is referred to as a rendezvous process, by which SUs can exchange control information for establishing data transmission link. Channel Hoping (CH) is one of the most representative techniques for letting SUs rendezvous with each other. At the beginning of each time slot, SUs access available channels according to their CH Sequences (CHSs) generated by the CH algorithm. In our previous work, we have proposed a Heterogeneous Radio Rendezvous (HRR) algorithm to address the rendezvous problem for heterogeneous CRNs, where SUs may be equipped with different numbers of radios. In this paper, we propose an Enhanced HRR (EHRR) algorithm, which can further shorten the length of period for the CHSs. Compared with the HRR algorithm, the EHRR algorithm lowers the upper bounds of Maximum Time To Rendezvous (MTTR). Moreover, the upper bounds of MTTR for the EHRR algorithm are derived by theoretical analysis. In addition, the performance of the EHRR algorithm in terms of MTTR is evaluated by simulation. Simulation results show the superiority of the EHRR algorithm compared with the HRR algorithm in terms of MTTR.

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A Low-Complexity High-Accuracy AR Based Channel Prediction Method for Interference Alignment

Ozawa M., Ohtsuki T., Panahi F., Jiang W., Takatori Y., Nakagawa T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

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© 2018 IEEE. Interference alignment (IA) is a technique that can suppress interference with a small number of antennas by aligning interference signals using transmit weights. These weights are designed based on the channel state information (CSI) fed back from each receiver, however, under the timevarying channel, the estimated CSI can be delayed/outdated, which will result in an imperfect IA. Therefore, IA with channel prediction has attracted much attention. The auto regressive (AR) model is known as a prediction method that predicts a future state based on only the past states. In the conventional channel prediction based IA method, the past channels are used directly for prediction. Therefore, the number of calculations for prediction can be too large. In this paper, based on the AR model, we describe a low complexity and high accuracy channel prediction method for IA. To predict the future channel, we only use the differences of channels between adjacent times instead of using the past channels directly. This will lead to a very low channel prediction error. Simulations show that the proposed method improves prediction accuracy and requires less calculation than the conventional one. Moreover, the IA with the proposed channel prediction method will achieve a higher transmission rate.

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A Smart City Based on Ambient Intelligence

大槻 知明

IEICE TRANSACTIONS ON COMMUNICATIONS E100B （ 9 ） 1547 - 1553 2017年09月

研究論文（学術雑誌）,  ISSN  0916-8516

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Impact of Pointing Errors on the Error Performance of Intersatellite Laser Communications

大槻 知明

JOURNAL OF LIGHTWAVE TECHNOLOGY 35 （ 14 ） 3082 - 3091 2017年07月

研究論文（学術雑誌）,  ISSN  0733-8724

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Improvements of femto-base station resource utilisation and ABS assignment convergence for dynamic ABS assignment

大槻 知明

IET COMMUNICATIONS 11 （ 3 ） 370 - 376 2017年02月

研究論文（学術雑誌）,  ISSN  1751-8628

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Low Computational Complexity Direction-of-Arrival Estimation of Wideband Signal Sources Based on Squared TOPS

大槻 知明

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E100A （ 1 ） 219 - 226 2017年01月

研究論文（学術雑誌）,  ISSN  1745-1337

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A Lower Bound on Secrecy Capacity for MIMO Wiretap Channel Aided by a Cooperative Jammer With Channel Estimation Error

大槻 知明

IEEE ACCESS 5   4636 - 4645 2017年

研究論文（学術雑誌）,  ISSN  2169-3536

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Exploiting Interference for Capacity Improvement in Software-Defined Vehicular Networks

大槻 知明

IEEE ACCESS 5   10662 - 10673 2017年

研究論文（学術雑誌）,  ISSN  2169-3536

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