start-ver=1.4 cd-journal=joma no-vol=E107-B cd-vols= no-issue=3 article-no= start-page=339 end-page=348 dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=202403 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Low Complexity Overloaded MIMO Non-Linear Detector with Iterative LLR Estimation en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=DennoSatoshi en-aut-sei=Denno en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=MakabeShuhei en-aut-sei=Makabe en-aut-mei=Shuhei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=HouYafei en-aut-sei=Hou en-aut-mei=Yafei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=3 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= en-keyword=overloaded MIMO kn-keyword=overloaded MIMO en-keyword=non-linear detector kn-keyword=non-linear detector en-keyword=soft-input decoding kn-keyword=soft-input decoding en-keyword=noise cancellation kn-keyword=noise cancellation en-keyword=ordering kn-keyword=ordering en-keyword=complexity reduction kn-keyword=complexity reduction END start-ver=1.4 cd-journal=joma no-vol=12 cd-vols= no-issue= article-no= start-page=28201 end-page=28211 dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240212 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=WLAN Channel Status Duration Prediction for Audio and Video Services Using Probabilistic Neural Networks en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=HouYafei en-aut-sei=Hou en-aut-mei=Yafei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=DennoSatoshi en-aut-sei=Denno en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= en-keyword=Wireless LAN kn-keyword=Wireless LAN en-keyword=Wireless communication kn-keyword=Wireless communication en-keyword=Media streaming kn-keyword=Media streaming en-keyword=Wireless sensor networks kn-keyword=Wireless sensor networks en-keyword=Resource management kn-keyword=Resource management en-keyword=Probability distribution kn-keyword=Probability distribution en-keyword=Channel allocation kn-keyword=Channel allocation en-keyword=Audio-visual systems kn-keyword=Audio-visual systems en-keyword=Data processing kn-keyword=Data processing en-keyword=Predictive models kn-keyword=Predictive models en-keyword=Neural networks kn-keyword=Neural networks en-keyword=Channel status duration prediction kn-keyword=Channel status duration prediction en-keyword=WLAN audio/video traffic kn-keyword=WLAN audio/video traffic en-keyword=data predictability analysis kn-keyword=data predictability analysis en-keyword=probabilistic neural network (PNN) kn-keyword=probabilistic neural network (PNN) END start-ver=1.4 cd-journal=joma no-vol=E105.B cd-vols= no-issue=10 article-no= start-page=1258 end-page=1267 dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20221001 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Adaptive Resource Allocation Based on Factor Graphs in Non-Orthogonal Multiple Access en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=YamagamiTaichi en-aut-sei=Yamagami en-aut-mei=Taichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=DennoSatoshi en-aut-sei=Denno en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=HouYafei en-aut-sei=Hou en-aut-mei=Yafei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= en-keyword=non-orthogonal multiple access kn-keyword=non-orthogonal multiple access en-keyword=message passing algorithm kn-keyword=message passing algorithm en-keyword=factor graphs kn-keyword=factor graphs en-keyword=log-likelihood ratio kn-keyword=log-likelihood ratio END start-ver=1.4 cd-journal=joma no-vol=E105.B cd-vols= no-issue=9 article-no= start-page=1087 end-page=1096 dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=202291 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Asynchronous Periodic Interference Signals Cancellation in Frequency Domain en-subtitle= kn-subtitle= en-abstract= kn-abstract=This paper proposes a novel interference cancellation technique that prevents radio receivers from degrading due to periodic interference signals caused by electromagnetic waves emitted from high power circuits. The proposed technique cancels periodic interference signals in the frequency domain, even if the periodic interference signals drift in the time domain. We propose a drift estimation based on a super resolution technique such as ESPRIT. Moreover, we propose a sequential drift estimation to enhance the drift estimation performance. The proposed technique employs a linear filter based on the minimum mean square error criterion with assistance of the estimated drifts for the interference cancellation. The performance of the proposed technique is confirmed by computer simulation. The proposed technique achieves a gain of more than 40dB at the higher frequency part in the band. The proposed canceler achieves such superior performance, if the parameter sets are carefully selected. The proposed sequential drift estimation relaxes the parameter constraints, and enables the proposed cancellation to achieve the performance upper bound. en-copyright= kn-copyright= en-aut-name=DENNOSatoshi en-aut-sei=DENNO en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HOUYafei en-aut-sei=HOU en-aut-mei=Yafei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= END start-ver=1.4 cd-journal=joma no-vol=10 cd-vols= no-issue= article-no= start-page=21109 end-page=21119 dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=2022 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Two-Dimensional RSSI-Based Indoor Localization Using Multiple Leaky Coaxial Cables With a Probabilistic Neural Network en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=ZhuJunjie en-aut-sei=Zhu en-aut-mei=Junjie kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HouPengcheng en-aut-sei=Hou en-aut-mei=Pengcheng kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NagayamaKenta en-aut-sei=Nagayama en-aut-mei=Kenta kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HouYafei en-aut-sei=Hou en-aut-mei=Yafei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=DennoSatoshi en-aut-sei=Denno en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=FerdianRian en-aut-sei=Ferdian en-aut-mei=Rian kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=5 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=6 en-affil=Faculty of Information Technology, Andalas University kn-affil= en-keyword=Leaky coaxial cable(LCX) kn-keyword=Leaky coaxial cable(LCX) en-keyword=localization kn-keyword=localization en-keyword=RSSI kn-keyword=RSSI en-keyword=neural network kn-keyword=neural network END start-ver=1.4 cd-journal=joma no-vol=9 cd-vols= no-issue= article-no= start-page=85795 end-page=85812 dt-received= dt-revised= dt-accepted= dt-pub-year=2021 dt-pub=2021 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Modeling and Predictability Analysis on Channel Spectrum Status Over Heavy Wireless LAN Traffic Environment en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=HouYafei en-aut-sei=Hou en-aut-mei=Yafei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=WebberJulian en-aut-sei=Webber en-aut-mei=Julian kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=YanoKazuto en-aut-sei=Yano en-aut-mei=Kazuto kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KawasakiShun en-aut-sei=Kawasaki en-aut-mei=Shun kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=DennoSatoshi en-aut-sei=Denno en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=SuzukiYoshinori en-aut-sei=Suzuki en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Natural Science and Technology, Institute of Academic and Research, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Engineering Science, Osaka University kn-affil= affil-num=3 en-affil=Wave Engineering Laboratory, Advanced Telecommunications Research Institute International kn-affil= affil-num=4 en-affil=Natural Science and Technology, Institute of Academic and Research, Okayama University kn-affil= affil-num=5 en-affil=Natural Science and Technology, Institute of Academic and Research, Okayama University kn-affil= affil-num=6 en-affil=Wave Engineering Laboratory, Advanced Telecommunications Research Institute International kn-affil= en-keyword=Wireless LAN kn-keyword=Wireless LAN en-keyword=Wireless communication kn-keyword=Wireless communication en-keyword=Predictive models kn-keyword=Predictive models en-keyword=Data models kn-keyword=Data models en-keyword=Analytical models kn-keyword=Analytical models en-keyword=Rail transportation kn-keyword=Rail transportation en-keyword=Protocols kn-keyword=Protocols en-keyword=Spectrum usage model kn-keyword=Spectrum usage model en-keyword=heavy WLAN traffic environment kn-keyword=heavy WLAN traffic environment en-keyword=cognitive radio kn-keyword=cognitive radio en-keyword=predictability theory kn-keyword=predictability theory en-keyword=auto-regressive predictor kn-keyword=auto-regressive predictor en-keyword=data categorization kn-keyword=data categorization END start-ver=1.4 cd-journal=joma no-vol=9 cd-vols= no-issue= article-no= start-page=15929 end-page=15938 dt-received= dt-revised= dt-accepted= dt-pub-year=2021 dt-pub=20210120 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Capacity Loss From Localization Error in MIMO Channel Using Leaky Coaxial Cable en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=ZhuJunjie en-aut-sei=Zhu en-aut-mei=Junjie kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HouYafei en-aut-sei=Hou en-aut-mei=Yafei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NagayamaKenta en-aut-sei=Nagayama en-aut-mei=Kenta kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=DennoSatoshi en-aut-sei=Denno en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= affil-num=1 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= en-keyword=Leaky coaxial cable (LCX) kn-keyword=Leaky coaxial cable (LCX) en-keyword=LCX-MIMO kn-keyword=LCX-MIMO en-keyword=positional information kn-keyword=positional information en-keyword=channel capacity kn-keyword=channel capacity en-keyword=power allocation kn-keyword=power allocation END start-ver=1.4 cd-journal=joma no-vol=9 cd-vols= no-issue= article-no= start-page=e20 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=20200925 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A study for 2-D indoor localization using multiple leaky coaxial cables en-subtitle= kn-subtitle= en-abstract= kn-abstract=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. en-copyright= kn-copyright= en-aut-name=ZhuJunjie en-aut-sei=Zhu en-aut-mei=Junjie kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HouPengcheng en-aut-sei=Hou en-aut-mei=Pengcheng kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=HouYafei en-aut-sei=Hou en-aut-mei=Yafei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=DennoSatoshi en-aut-sei=Denno en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=OkadaMinoru en-aut-sei=Okada en-aut-mei=Minoru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= affil-num=1 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=5 en-affil=Division of Information Science, Nara Institute of Science and Technology kn-affil= en-keyword=Leaky coaxial cable kn-keyword=Leaky coaxial cable en-keyword=Capacity kn-keyword=Capacity en-keyword=MIMO kn-keyword=MIMO END