The maximum network delay needs to be estimated for maintaining the stability of delay-sensitive systems, such as remote-control applications. Conventional estimation approaches, including network digital twin technologies, typically rely on complete network configuration information. However, when such information is incomplete or unidentified, these approaches cannot be applied. In this study, we propose a method to estimate the maximum delay by generating a pseudo-network whose delay characteristics closely resemble those of the target network. Candidate networks with different configurations are created, and the pseudo-network is selected by comparing their delay distributions with that of the target network. Experimental results show that a pseudo-network with similar delay characteristics to the target network can be found, even when its configuration differs from that of the target network. This result holds for multiple target networks. These findings support the potential applicability of our pseudo-network-based approach to unidentified networks.
{"title":"Proposal of Maximum Delay Estimation Method for Unidentified Networks by Generating Pseudo-Network","authors":"Ryohei Yamada;Hideaki Kimura;Takashi Nakanishi;Tatsuya Shimada","doi":"10.23919/comex.2025XBL0140","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0140","url":null,"abstract":"The maximum network delay needs to be estimated for maintaining the stability of delay-sensitive systems, such as remote-control applications. Conventional estimation approaches, including network digital twin technologies, typically rely on complete network configuration information. However, when such information is incomplete or unidentified, these approaches cannot be applied. In this study, we propose a method to estimate the maximum delay by generating a pseudo-network whose delay characteristics closely resemble those of the target network. Candidate networks with different configurations are created, and the pseudo-network is selected by comparing their delay distributions with that of the target network. Experimental results show that a pseudo-network with similar delay characteristics to the target network can be found, even when its configuration differs from that of the target network. This result holds for multiple target networks. These findings support the potential applicability of our pseudo-network-based approach to unidentified networks.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 3","pages":"37-41"},"PeriodicalIF":0.3,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359605","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.23919/comex.2025XBL0136
Tien H. Do;Thang V. Nguyen;Hoang D. Le;Ngoc T. Dang
This letter addresses the transmit power minimization challenge in unmanned aerial vehicle (UAV)-assisted hybrid free-space optical (FSO)/terahertz (THz) systems integrated with incremental redundancy hybrid automatic repeat request (IR-HARQ), a cornerstone for 6G's ultra-reliable low-latency communications (URLLC). We propose a deep reinforcement learning (DRL)-driven framework, leveraging proximal policy optimization (PPO), to adapt power allocation across retransmissions via an agent-learned policy dynamically. This ensures reliable packet delivery under stringent delay bounds while accounting for channel impairments, including atmospheric attenuation, scintillation fading, and beam pointing errors. The system model incorporates SNR-based FSO/THz switching, with FSO as the primary link and THz as backup, evaluated through outage probabilities tailored to IR-HARQ, chase combining HARQ (CC-HARQ), and automatic repeat request (ARQ). Simulations across diverse environmental conditions reveal the proposed DRL-IR-HARQ hybrid achieves up to 0.7 dBm savings over THz-only baselines and conventional HARQ protocols, underscoring its robustness for energy-efficient 6G aerial backhauls and disaster-resilient networks.
{"title":"DRL-Based Power Optimization for Hybrid FSO/THz-Enabled UAV Systems Using IR-HARQ","authors":"Tien H. Do;Thang V. Nguyen;Hoang D. Le;Ngoc T. Dang","doi":"10.23919/comex.2025XBL0136","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0136","url":null,"abstract":"This letter addresses the transmit power minimization challenge in unmanned aerial vehicle (UAV)-assisted hybrid free-space optical (FSO)/terahertz (THz) systems integrated with incremental redundancy hybrid automatic repeat request (IR-HARQ), a cornerstone for 6G's ultra-reliable low-latency communications (URLLC). We propose a deep reinforcement learning (DRL)-driven framework, leveraging proximal policy optimization (PPO), to adapt power allocation across retransmissions via an agent-learned policy dynamically. This ensures reliable packet delivery under stringent delay bounds while accounting for channel impairments, including atmospheric attenuation, scintillation fading, and beam pointing errors. The system model incorporates SNR-based FSO/THz switching, with FSO as the primary link and THz as backup, evaluated through outage probabilities tailored to IR-HARQ, chase combining HARQ (CC-HARQ), and automatic repeat request (ARQ). Simulations across diverse environmental conditions reveal the proposed DRL-IR-HARQ hybrid achieves up to 0.7 dBm savings over THz-only baselines and conventional HARQ protocols, underscoring its robustness for energy-efficient 6G aerial backhauls and disaster-resilient networks.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 3","pages":"33-36"},"PeriodicalIF":0.3,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.23919/comex.2025XBL0148
Xin Du
The split-step parabolic equation (SSPE) and the mirror Kirchhoff approximation (MKA) are fast methods for predicting the shadowing effect at millimeter waves (mmWaves). This letter proposes an algorithm for designing unequal intervals for an arbitrarily shaped scatterer in a three-dimensional (3D) environment, aiming to reduce computational time. The 3D SSPE/MKA with proposed unequal intervals is validated for the scattering problem of a lossy dielectric sphere at mmWaves (40 GHz–100 GHz). The results show that the proposed method achieves good accuracy with a low root-mean-square error of less than 1 dB compared to the exact solution. In addition, the results show that the proposed method out-performs the conventional equal interval in terms of fast calculation speed by approximately 21%–51%.
{"title":"Design of Unequal Intervals for Split-Step Parabolic Equation and Mirror Kirchhoff Approximation in Three-Dimensional Environment","authors":"Xin Du","doi":"10.23919/comex.2025XBL0148","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0148","url":null,"abstract":"The split-step parabolic equation (SSPE) and the mirror Kirchhoff approximation (MKA) are fast methods for predicting the shadowing effect at millimeter waves (mmWaves). This letter proposes an algorithm for designing unequal intervals for an arbitrarily shaped scatterer in a three-dimensional (3D) environment, aiming to reduce computational time. The 3D SSPE/MKA with proposed unequal intervals is validated for the scattering problem of a lossy dielectric sphere at mmWaves (40 GHz–100 GHz). The results show that the proposed method achieves good accuracy with a low root-mean-square error of less than 1 dB compared to the exact solution. In addition, the results show that the proposed method out-performs the conventional equal interval in terms of fast calculation speed by approximately 21%–51%.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 3","pages":"42-45"},"PeriodicalIF":0.3,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.23919/comex.2025XBL0142
Wataru Ota;Masaki Bandai
Point cloud streaming has emerged as an increasingly prominent research topic in recent years. Point clouds enable the generation of highly immersive visual experiences. However, point cloud streaming requires high bandwidth due to its large data size. So under the constrained-bandwidth environment, it is important to reduce the data size of point cloud or conducting adaptive streaming. In this paper, we propose a method to reduce the data size of RGB information and the coordinates of point clouds, and to use these point clouds for adaptive streaming. We implement the proposed method in real-world system and evaluate its performance in terms of processing time, communication bitrate, and visual quality.
{"title":"Adaptive Point Cloud Streaming with Color Reduction","authors":"Wataru Ota;Masaki Bandai","doi":"10.23919/comex.2025XBL0142","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0142","url":null,"abstract":"Point cloud streaming has emerged as an increasingly prominent research topic in recent years. Point clouds enable the generation of highly immersive visual experiences. However, point cloud streaming requires high bandwidth due to its large data size. So under the constrained-bandwidth environment, it is important to reduce the data size of point cloud or conducting adaptive streaming. In this paper, we propose a method to reduce the data size of RGB information and the coordinates of point clouds, and to use these point clouds for adaptive streaming. We implement the proposed method in real-world system and evaluate its performance in terms of processing time, communication bitrate, and visual quality.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 3","pages":"59-62"},"PeriodicalIF":0.3,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This letter presents the validation of a low-permittivity $(varepsilon_{r}= 1.88)$ PCB substrate for compact, 28-GHz phased-array antennas. A grounded-coplanar-waveguide (CPWG) feed and a three-layer stack-up were verified using a TRL calibration board, confirming stable performance up to 60 GHz. A single via-fed patch antenna on the new laminate achieved 7.1 dBi gain, 3.2 dB higher than that on Megtron6 $(varepsilon_{r}=3.7)$. A $1times 4$ array was implemented on the same stack-up achieved 9.2 dBi broadside gain and up to 0.8 dB lower scan loss at 60° compared with Megtron6. These results demonstrate the importance of substrate selection and highlight the potential of this laminate for PCB-based millimeter-wave antennas.
{"title":"Validation of a Low-Permittivity PCB Substrate for Compact 28-GHz Phased Array Antennas","authors":"Carrel da Gomez;Weichu Chen;Dongwon You;Xi Fu;Ibrahim Abdo;Shimpei Yakuwa;Naoki Nagaoka;Yasuto Ishimaru;Atsushi Shirane;Kenichi Okada","doi":"10.23919/comex.2025XBL0158","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0158","url":null,"abstract":"This letter presents the validation of a low-permittivity <tex>$(varepsilon_{r}= 1.88)$</tex> PCB substrate for compact, 28-GHz phased-array antennas. A grounded-coplanar-waveguide (CPWG) feed and a three-layer stack-up were verified using a TRL calibration board, confirming stable performance up to 60 GHz. A single via-fed patch antenna on the new laminate achieved 7.1 dBi gain, 3.2 dB higher than that on Megtron6 <tex>$(varepsilon_{r}=3.7)$</tex>. A <tex>$1times 4$</tex> array was implemented on the same stack-up achieved 9.2 dBi broadside gain and up to 0.8 dB lower scan loss at 60° compared with Megtron6. These results demonstrate the importance of substrate selection and highlight the potential of this laminate for PCB-based millimeter-wave antennas.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 3","pages":"46-49"},"PeriodicalIF":0.3,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the application of Internet of Things (IoT) technologies has advanced in agriculture, forestry, and fisheries. In this paper, we focus on the hatchery phase of oyster farming and consider a wireless multi-hop network to realize a system that monitors water quality and ambient temperature/humidity in real time using sensors. We design a fully waterproof, integrated device that combines a water quality sensor with a wireless communication module, enabling direct submersion in water for flexible and practical use. However, since radio signals significantly attenuate underwater, we first conduct preliminary experiments using actual hardware to evaluate the affect quantitatively. We then propose to introduce a cylindrical container as a waveguide to improve communication range. Finally, we prototype a wireless multi-hop network and confirm its feasibility.
{"title":"Practical Node Design for Wireless Multi-Hop Network Applications in Oyster Farming","authors":"Kazuhiko Kinoshita;Kyohei Yamamoto;Alberto Gallegos Ramonet;Akinori Tsuji;Kensuke Iwamoto","doi":"10.23919/comex.2025XBL0162","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0162","url":null,"abstract":"In recent years, the application of Internet of Things (IoT) technologies has advanced in agriculture, forestry, and fisheries. In this paper, we focus on the hatchery phase of oyster farming and consider a wireless multi-hop network to realize a system that monitors water quality and ambient temperature/humidity in real time using sensors. We design a fully waterproof, integrated device that combines a water quality sensor with a wireless communication module, enabling direct submersion in water for flexible and practical use. However, since radio signals significantly attenuate underwater, we first conduct preliminary experiments using actual hardware to evaluate the affect quantitatively. We then propose to introduce a cylindrical container as a waveguide to improve communication range. Finally, we prototype a wireless multi-hop network and confirm its feasibility.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 3","pages":"63-66"},"PeriodicalIF":0.3,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.23919/comex.2025XBL0141
Katsushige Harima;Ifong Wu;Kaoru Gotoh
In a reverberation chamber, a random electromagnetic field is generated by mechanically rotating a metal stirrer. Although the spatial electric field distribution in an ideal reverberation chamber can be represented as a probability density function using a statistical approach, this method does not account for the size of the chamber. The electric field distribution in a well-stirred reverberation chamber can be approximated using the plane wave integration method, which is based on the vector sum of plane waves from multiple directions. In this study, the electric field distribution within the working volume of the reverberation chamber was calculated using the plane wave integration method. Then, by comparing the calculation results with the theoretical values obtained through the statistical method, the size of the working volume at which the electric field distribution could be considered ideal was estimated.
{"title":"Estimation of Effective Working Volume Within Ideal Reverberation Chamber Using Plane Wave Integration Method","authors":"Katsushige Harima;Ifong Wu;Kaoru Gotoh","doi":"10.23919/comex.2025XBL0141","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0141","url":null,"abstract":"In a reverberation chamber, a random electromagnetic field is generated by mechanically rotating a metal stirrer. Although the spatial electric field distribution in an ideal reverberation chamber can be represented as a probability density function using a statistical approach, this method does not account for the size of the chamber. The electric field distribution in a well-stirred reverberation chamber can be approximated using the plane wave integration method, which is based on the vector sum of plane waves from multiple directions. In this study, the electric field distribution within the working volume of the reverberation chamber was calculated using the plane wave integration method. Then, by comparing the calculation results with the theoretical values obtained through the statistical method, the size of the working volume at which the electric field distribution could be considered ideal was estimated.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 3","pages":"50-53"},"PeriodicalIF":0.3,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11359564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
5G systems are expected to satisfy diverse Quality of Service (QoS) requirements through RAN slicing. Adaptive RAN function placement (RFP) control is crucial, but deep reinforcement learning (DRL) methods like Ape-X require substantial training time and data, limiting their practical use. This study applies two black-box optimization methods, Factorization Machine with Annealing (FMA) and Bayesian Optimization with Dictionaries (BODi), to the RFP control problem and compares them with Ape-X under different data conditions. Results show that FMA achieves higher QoS satisfaction with limited data and gradually matches or surpasses Ape-X as more data accumulates, despite longer decision times.
{"title":"Comparative Evaluation of Black-Box Optimization Methods for RAN Function Placement Problem","authors":"Shun Furusawa;Chihiro Dogo;Kazuhiro Saito;Yuya Seki;Shuta Kikuchi;Shu Tanaka","doi":"10.23919/comex.2025XBL0131","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0131","url":null,"abstract":"5G systems are expected to satisfy diverse Quality of Service (QoS) requirements through RAN slicing. Adaptive RAN function placement (RFP) control is crucial, but deep reinforcement learning (DRL) methods like Ape-X require substantial training time and data, limiting their practical use. This study applies two black-box optimization methods, Factorization Machine with Annealing (FMA) and Bayesian Optimization with Dictionaries (BODi), to the RFP control problem and compares them with Ape-X under different data conditions. Results show that FMA achieves higher QoS satisfaction with limited data and gradually matches or surpasses Ape-X as more data accumulates, despite longer decision times.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 2","pages":"21-24"},"PeriodicalIF":0.3,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, network analysis has increasingly used packet data analysis for machine learning. However, packet data accumulation has issues, including difficulties in data accumulation and insufficient diversity in attack patterns. To address these issues, researchers have increasingly turned to synthetic data generation methods as an alternative to data accumulation approaches for training machine learning models. However, conventional GAN-based synthetic data generation methods have limitations, particularly statistical characteristic inconsistencies due to training instability and lack of correlations both within packet fields and between consecutive packets. This paper proposes an approach that transforms packet data into structured image representations and generates differential image data using a conditional diffusion model on the basis of previous packet data. The proposed differential representation method excludes unchanged fields from learning and focuses specifically on the varying components that capture inter-packet relationships. Evaluation experiments conducted on the CICIDS 2017 dataset demonstrate the proposed approach improves over conventional methods in both statistical distribution similarity metrics and classification difficulty assessments.
{"title":"Diffusion Model-Based Network Packet Synthesis Using Inter-Packet Difference Learning","authors":"Yukito Onodera;Erina Takeshita;Tomoya Kosugi;Takashi Nakanishi;Tatsuya Shimada","doi":"10.23919/comex.2025XBL0132","DOIUrl":"https://doi.org/10.23919/comex.2025XBL0132","url":null,"abstract":"In recent years, network analysis has increasingly used packet data analysis for machine learning. However, packet data accumulation has issues, including difficulties in data accumulation and insufficient diversity in attack patterns. To address these issues, researchers have increasingly turned to synthetic data generation methods as an alternative to data accumulation approaches for training machine learning models. However, conventional GAN-based synthetic data generation methods have limitations, particularly statistical characteristic inconsistencies due to training instability and lack of correlations both within packet fields and between consecutive packets. This paper proposes an approach that transforms packet data into structured image representations and generates differential image data using a conditional diffusion model on the basis of previous packet data. The proposed differential representation method excludes unchanged fields from learning and focuses specifically on the varying components that capture inter-packet relationships. Evaluation experiments conducted on the CICIDS 2017 dataset demonstrate the proposed approach improves over conventional methods in both statistical distribution similarity metrics and classification difficulty assessments.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"15 2","pages":"25-28"},"PeriodicalIF":0.3,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.23919/comex.2025COF0001
IEICE Communications Express (ComEX) has continued to issue a special cluster annually in conjunction with the IEICE General Conferences since 2019. This year's special cluster is based on the papers presented at the IEICE General Conference 2025, which was held at the Setagaya Campus of Tokyo City University in March 2025. Since October 1, 2023, all accepted letters in ComEX have been published in IEEE Xplore to improve the visibility of letters.
{"title":"Special Cluster in Conjunction with IEICE General Conference 2025","authors":"","doi":"10.23919/comex.2025COF0001","DOIUrl":"https://doi.org/10.23919/comex.2025COF0001","url":null,"abstract":"IEICE Communications Express (ComEX) has continued to issue a special cluster annually in conjunction with the IEICE General Conferences since 2019. This year's special cluster is based on the papers presented at the IEICE General Conference 2025, which was held at the Setagaya Campus of Tokyo City University in March 2025. Since October 1, 2023, all accepted letters in ComEX have been published in IEEE Xplore to improve the visibility of letters.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"14 12","pages":"419-419"},"PeriodicalIF":0.3,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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