IEEE Acta Medica Okayama 2158-4001 2025 A Linear Search Algorithm for Resource Allocation in Frequency Domain Non-Orthogonal Multiple Access EN Satoshi Denno Faculty of Environmental, Life, Natural Science and Technology, Okayama University Yuto Ohba Faculty of Environmental, Life, Natural Science and Technology, Okayama University Yafei Hou Faculty of Environmental, Life, Natural Science and Technology, Okayama University This paper proposes a linear search algorithm for resource allocation in frequency domain non-orthogonal multiple access based on the low-density signature (LDS). Although the proposed linear search enables the non-orthogonal multiple access to achieve superior transmission performance, the proposed linear search makes the resource allocation implemented with lower and fixed computational complexity. The performance of the non-orthogonal access based on the proposed linear search is evaluated by computer simulation. The proposed linear search algorithm makes the non-orthogonal multiple access achieve a gain of about 6 dB at the BER of 10–5 when the overloading ratio is set to 2. The complexity of the non-orthogonal access based on the proposed linear search algorithm is approximately half as much as that of the conventional low complexity resource allocation when the overloading ratio is 2, if the complexity is evaluated in terms of the number of additions. No potential conflict of interest relevant to this article was reported.

Institute of Electrical and Electronics Engineers (IEEE) Acta Medica Okayama 1745-1345 E108-B 1 2024 Overloaded MIMO Spatial Multiplexing Independent of Antenna Setups 1 13 EN Satoshi Denno Faculty of Environmental, Life, Natural Science and Technology, Okayama University Takumi Sugimoto Graduate School of Natural Science and Technology, Okayama University Koki Matoba Graduate School of Environmental, Life, Natural Science and Technology, Okayama University Yafei Hou Faculty of Environmental, Life, Natural Science and Technology, Okayama University This paper proposes overloaded MIMO spatial multiplexing that can increase the number of spatially multiplexed signal streams despite of the number of antennas on a terminal and that on a receiver. We propose extension of the channel matrix for the spatial multiplexing to achieve the superb multiplexing performance. Precoding based on the extended channel matrix plays a crucial role in carrying out such spatial multiplexing. We consider three types of QR-decomposition techniques for the proposed spatial multiplexing to improve the transmission performance. The transmission performance of the proposed spatial multiplexing is evaluated by computer simulation. The simulation reveals that the proposed overloaded MIMO spatial multiplexing can implement 6 stream-spatial multiplexing in a 2�~2 MIMO system, i.e., the overloading ratio of 3.0. The superior transmission performance is achieved by the proposed overloaded MIMO spatial multiplexing with one of the QR-decomposition techniques. No potential conflict of interest relevant to this article was reported.

Institute of Electrical and Electronics Engineers Acta Medica Okayama 2169-3536 12 2024 MUSIC Spectrum Based Interference Detection, Localization, and Interference Arrival Prediction for mmWave IRS-MIMO System 142592 142605 EN Yafei Hou Faculty of Environmental, Life, Natural Science and Technology, Okayama University Kazuto Yano Wave Engineering Laboratories, Advanced Telecommunications Research Institute International Norisato Suga Wave Engineering Laboratories, Advanced Telecommunications Research Institute International Julian Webber Wave Engineering Laboratories, Advanced Telecommunications Research Institute International Satoshi Denno Faculty of Environmental, Life, Natural Science and Technology, Okayama University Toshikazu Sakano Wave Engineering Laboratories, Advanced Telecommunications Research Institute International For a millimeter wave (mmWave) intelligent re-configurable surface (IRS)-MIMO system, if it can correctly detect the interference occurrence and their locations, the patterns of interference signal can be collected and learned using machine learning for the prediction of interference arrival. With the information of interference location and activity pattern, the capacity of the system can be largely improved using many techniques such as beamforming, interference cancellation, and transmission scheduling. This paper aims to detect interference occurrence using a low-complexity MUSIC (MUSIC: multiple signal classification) spectrum-based method, and then localize their sources for mmWave IRS-MIMO system. The MUSIC spectrum of wireless system can be regarded as somehow the 'signature' related to the signals transmitted from different users or interference. We utilize such property to detect the occurrence of interference, and then localize their sources in a low-complexity way. Finally, the pattern of interference occurrence can be learned to predict the interference arrival from the collected data. This paper also proposed an efficient probabilistic neural network (PNN)-based predictor for the interference arrival prediction and showed its prediction accuracy. From simulated results, our proposed method can achieve the correct results with the accuracy near to 100% when the fingerprint samples is over 10. In addition, the localization error can be within 1 m with more than 65% and 43% for Y-axis and X-axis, respectively. Finally, based on the results of the interference occurrence, the proposed PNN-based predictor for the interference arrival prediction can capture correctly the similar distribution function of the coming continuous idle status. No potential conflict of interest relevant to this article was reported.

Institute of Electrical and Electronics Engineers (IEEE) Acta Medica Okayama 1745-1345 E107-B 3 2024 Low Complexity Overloaded MIMO Non-Linear Detector with Iterative LLR Estimation 339 348 EN Satoshi Denno Faculty of Environmental, Life, Natural Science and Technology, Okayama University Shuhei Makabe Faculty of Environmental, Life, Natural Science and Technology, Okayama University Yafei Hou Faculty of Environmental, Life, Natural Science and Technology, Okayama University This paper proposes a non-linear overloaded MIMO detector that outperforms the conventional soft-input maximum likelihood detector (MLD) with less computational complexity. We propose iterative log-likelihood ratio (LLR) estimation and multi stage LLR estimation for the proposed detector to achieve such superior performance. While the iterative LLR estimation achieves better BER performance, the multi stage LLR estimation makes the detector less complex than the conventional soft-input maximum likelihood detector (MLD). The computer simulation reveals that the proposed detector achieves about 0.6 dB better BER performance than the soft-input MLD with about half of the soft-input MLD's complexity in a 6 �~ 3 overloaded MIMO OFDM system. No potential conflict of interest relevant to this article was reported.

Institute of Electrical and Electronics Engineers Acta Medica Okayama 2169-3536 12 2024 WLAN Channel Status Duration Prediction for Audio and Video Services Using Probabilistic Neural Networks 28201 28211 EN Yafei Hou Faculty of Environmental, Life, Natural Science and Technology, Okayama University Satoshi Denno Faculty of Environmental, Life, Natural Science and Technology, Okayama University Due to massive increase in wireless access from smartphones, IoT devices, WLAN is aiming to improve its spectrum efficiency (SE) using many technologies. Some interesting techniques for WLAN systems are flexible allocation of frequency resource and cognitive radio (CR) techniques which expect to find more useful spectrum resource by modeling and then predicting of channel status using the captured statistics information of the used spectrum. This paper investigates the prediction accuracy of busy/idle duration of two major wireless services: audio service and video service using neural network based predictor. We first study the statistics distribution of their time-series busy/idle (B/I) duration, and then analyze the predictability of the busy/idle duration based on the predictability theory. Then, we propose a data categorization (DC) method which categorizes the duration of recent B/I duration according the their ranges to make the duration of next data be distributed into several streams. From the predictability analysis of each stream and the prediction performance using the probabilistic neural network (PNN), it can be confirmed that the proposed DC can improve the prediction accuracy of time-series data in partial streams. No potential conflict of interest relevant to this article was reported.

Institute of Electronics, Information and Communications Engineers (IEICE) Acta Medica Okayama 0916-8516 E105.B 10 2022 Adaptive Resource Allocation Based on Factor Graphs in Non-Orthogonal Multiple Access 1258 1267 EN Taichi Yamagami Graduate School of Natural Science and Technology, Okayama University Satoshi Denno Graduate School of Natural Science and Technology, Okayama University Yafei Hou Graduate School of Natural Science and Technology, Okayama University In this paper, we propose a non-orthogonal multiple access with adaptive resource allocation. The proposed non-orthogonal multiple access assigns multiple frequency resources for each device to send packets. Even if the number of devices is more than that of the available frequency resources, the proposed non-orthogonal access allows all the devices to transmit their packets simultaneously for high capacity massive machine-type communications (mMTC). Furthermore, this paper proposes adaptive resource allocation algorithms based on factor graphs that adaptively allocate the frequency resources to the devices for improvement of the transmission performances. This paper proposes two allocation algorithms for the proposed non-orthogonal multiple access. This paper shows that the proposed non-orthogonal multiple access achieves superior transmission performance when the number of the devices is 50% greater than the amount of the resource, i.e., the overloading ratio of 1.5, even without the adaptive resource allocation. The adaptive resource allocation enables the proposed non-orthogonal access to attain a gain of about 5dB at the BER of 10^-4. No potential conflict of interest relevant to this article was reported.

IEEE Acta Medica Okayama 2169-3536 10 2022 Two-Dimensional RSSI-Based Indoor Localization Using Multiple Leaky Coaxial Cables With a Probabilistic Neural Network 21109 21119 EN Junjie Zhu Graduate School of Natural Science and Technology, Okayama University Pengcheng Hou Graduate School of Natural Science and Technology, Okayama University Kenta Nagayama Graduate School of Natural Science and Technology, Okayama University Yafei Hou Graduate School of Natural Science and Technology, Okayama University Satoshi Denno Graduate School of Natural Science and Technology, Okayama University Rian Ferdian Faculty of Information Technology, Andalas University Received signal strength indicator (RSSI) based indoor localization technology has its irreplaceable advantages for many location-aware applications. It is becoming obvious that in the development of fifth-generation (5G) and future communication technology, indoor localization technology will play a key role in location-based application scenarios including smart home systems, manufacturing automation, health care, and robotics. Compared with wireless coverage using conventional monopole antenna, leaky coaxial cables (LCX) can generate a uniform and stable wireless coverage over a long-narrow linear-cell or irregular environment such as railway station and underground shopping-mall, especially for some manufacturing factories with wireless zone areas from a large number of mental machines. This paper presents a localization method using multiple leaky coaxial cables (LCX) for an indoor multipath-rich environment. Different from conventional localization methods based on time of arrival (TOA) or time difference of arrival (TDOA), we consider improving the localization accuracy by machine learning RSSI from LCX. We will present a probabilistic neural network (PNN) approach by utilizing RSSI from LCX. The proposal is aimed at the two-dimensional (2-D) localization in a trajectory. In addition, we also compared the performance of the RSSI-based PNN (RSSI-PNN) method and conventional TDOA method over the same environment. The results show the RSSI-PNN method is promising and more than 90% of the localization errors in the RSSI-PNN method are within 1 m. Compared with the conventional TDOA method, the RSSI-PNN method has better localization performance especially in the middle area of the wireless coverage of LCXs in the indoor environment. No potential conflict of interest relevant to this article was reported.

IEEE-Inst Electrical Electronics Engineers Inc Acta Medica Okayama 2169-3536 9 2021 Modeling and Predictability Analysis on Channel Spectrum Status Over Heavy Wireless LAN Traffic Environment 85795 85812 EN Yafei Hou Natural Science and Technology, Institute of Academic and Research, Okayama University Julian Webber Graduate School of Engineering Science, Osaka University Kazuto Yano Wave Engineering Laboratory, Advanced Telecommunications Research Institute International Shun Kawasaki Natural Science and Technology, Institute of Academic and Research, Okayama University Satoshi Denno Natural Science and Technology, Institute of Academic and Research, Okayama University Yoshinori Suzuki Wave Engineering Laboratory, Advanced Telecommunications Research Institute International Using the real wireless spectrum occupancy status in 2.4 and 5 GHz bands collected at a railway station as representative of a heavy wireless LAN (WLAN) traffic environment, this paper studies the modeling of durations of busy/idle (B/I) status and its predictability based on predictability theory. We first measure and model the channel status in the heavy traffic environment over almost all of the WLAN channels at 2.4 GHz and 5 GHz bands in a busy (rush hour) period and non-busy period. Then, using two selected channels at 2.4 GHz and 5 GHz bands, we analyze the upper bound (UB) and lower bound (LB) of predictability of the busy/idle durations based on predictability theory. The analysis shows that the LB predictability of durations can be easily increased by changing their probability distribution. Based on this property, we introduce the data categorization (DC) method. By categorizing the busy/idle durations into different streams, the proposed data categorization can improve the prediction performance of some streams with large LB predictability, even if it employs a simple low-complexity auto-regressive (AR) predictor. No potential conflict of interest relevant to this article was reported.

Institute of Electrical and Electronics Engineers Acta Medica Okayama 2169-3536 9 2021 Capacity Loss From Localization Error in MIMO Channel Using Leaky Coaxial Cable 15929 15938 EN Junjie Zhu Graduate School of Natural Science and Technology, Okayama University Yafei Hou Graduate School of Natural Science and Technology, Okayama University Kenta Nagayama Graduate School of Natural Science and Technology, Okayama University Satoshi Denno Graduate School of Natural Science and Technology, Okayama University Leaky coaxial (LCX) cable has been employed as antennas for wireless traffic over many linear-cell scenarios such as railway station, tunnels and shopping malls. In addition, LCX can be used for user localization and wireless power transfer (WPT). Compared with the equal power allocation method, the power allocation method for LCX system using positional information (PI) can improve its capacity with the same level of computational complexity. In this paper, we will investigate the level of capacity loss on the 2.4 GHz and 5 GHz band for the conventional equal power (EP) allocation method, the water-filling (WF) based power allocation, and our proposed low-complexity power allocation method for LCX system with PI. The results show that LCX system with our proposed method using PI can reduce the capacity loss due to localization error than that of others. No potential conflict of interest relevant to this article was reported.

Cambridge University Press Acta Medica Okayama 2048-7703 9 2020 A study for 2-D indoor localization using multiple leaky coaxial cables e20 EN Junjie Zhu Graduate School of Natural Science and Technology, Okayama University Pengcheng Hou Graduate School of Natural Science and Technology, Okayama University Yafei Hou Graduate School of Natural Science and Technology, Okayama University Satoshi Denno Graduate School of Natural Science and Technology, Okayama University Minoru Okada Division of Information Science, Nara Institute of Science and Technology Indoor localization technology, which can provide the location information of the target object or stochastic things, is becoming essential requirement for many applications and services such as Internet-of-Things (IoT), real-time control in the development of Fifth-generation (5G) technology. Leaky coaxial cable which can be used as antennas is able to detect the location of the user in a simple way due to its potential property. In this paper, we proposes a simple method to improve the localization accuracy of 2-D indoor localization using multiple LCX cables. In addition, we also evaluate the channel capacity loss due to the localization error of the LCX-MIMO using our proposed method. No potential conflict of interest relevant to this article was reported.