論文 - 大槻　知明

SAR image change detection based on saliency region guidance and SIFT keypoint extraction

Wang L., Sun B., Zhao C., Mazhar S., Ohtsuki T., Takis Mathiopoulos P., Adachi F.

Pattern Recognition 172 2026年04月

ISSN  00313203

　概要を見る

Synthetic Aperture Radar (SAR) can operate under all-weather, all-day conditions, playing a crucial role in regional change detection (CD). However, due to its unique imaging principles, SAR images contain significant speckle noise and blurred boundary and detail features, which reduces the detection accuracy and leads to missed detection and false detection. To address these issues, this paper proposes a SAR image CD method based on saliency region guidance and Scale-Invariant Feature Transform (SIFT) keypoint extraction to reduce the interference of speckle noise. First, a saliency region guidance method is introduced to analyze the saliency of local features in SAR images, extracting potentially changed regions and reducing the interference of speckle noise. Second, the SIFT is employed to extract keypoints in regions significantly different from the background in the difference map, leveraging its robustness to speckle noise. By extracting keypoints, the approximate location and extent of the changed regions are determined. These are, then, fused with the saliency region information, enhancing the saliency weights of pixels around keypoints for more extraction of change regions. Finally, a Vision Transformer (ViT) detection network is used for SAR image CD, utilizing the combined saliency information from the original saliency map and SIFT keypoints. This approach effectively integrates SIFT's stable description of local features with ViT's modeling capability for global features, improving the model's accuracy and robustness.

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Depthwise-Attentive Hierarchical Cross-Modal Knowledge Distillation Network for Rail Surface Defect Detection

Zhou X., Guan X., Peng Y., Zhang Z., Zhou X., Chen H., Ohtsuki T., Han Z.

IEEE Internet of Things Journal 13 （ 6 ） 10915 - 10928 2026年03月

　概要を見る

Accurate detection of surface defects on railway tracks is critical for safe railway operation. Most existing models rely solely on red-green-blue (RGB) images, limiting their ability to capture structural information. Incorporating depth features provides richer spatial cues, significantly improving detection accuracy. However, current RGB and depth (RGB-D) dual-stream models suffer from high computational complexity and hardware dependencies, making them impractical for real-world deployment. To address these limitations, we propose DAHNet, an asymmetric knowledge distillation model with a teacher-student architecture. DAHNet-T serves as the teacher network, taking RGB-D inputs and integrating a cross-modal attention feature enhancer (CAFE) module to capture contextual information, along with a depthwise feature interaction block (DFIB) for efficient cross-modal fusion. DAHNet-S is the student network, a lightweight single-stream RGB model employing depthwise separable convolutions to reduce computation. We introduce a multilevel distillation strategy with dynamic temperature scaling to balance coarse-grained and fine-grained knowledge transfer, while incorporating contrastive learning and structural loss to improve pixel-level accuracy. Extensive experiments on the NEU RSDDS-AUG dataset demonstrate that our distilled model DAHNet-KD outperforms state-of-the-art methods. Compared to DAHNet-T, the number of parameters is reduced from 87.72 to 13.97 MParams, and the computational cost decreases from 19.79 to 5.41 GFLOPs. The proposed model achieves superior performance across various evaluation metrics and also generalizes well on other public datasets. Therefore, the model provides a lightweight and high-accuracy solution for deployment on mobile devices in real-world industrial scenarios.

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MuECNet: A Lightweight Multiuser Enhanced Convolutional Architecture for Robust CSI-Based Human Activity Recognition in Real-World IoT Environments

Miao F., Takyu O., Shan L., Yin Y., Zhao O., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 6 ） 12209 - 12219 2026年03月

　概要を見る

Wi-Fi channel state information (CSI)-based human activity recognition (HAR) leverages rich channel propagation characteristics to enable nonintrusive, privacy-preserving, and device-free sensing. In multiuser wireless environments, however, HAR faces significant challenges, including multipath interference, signal overlap, label ambiguity, cross-domain channel variability, and constraints imposed by real-time deployment on resource-limited edge devices. This article presents multiuser enhanced convolutional network (MuECNet), a lightweight and modular deep learning framework designed to operate under realistic multiuser, multiactivity CSI sensing conditions. MuECNet integrates three key components: 1) an enhanced convolutional encoding module (ECEM) for fine-grained temporal-spectral feature extraction that preserves channel propagation signatures; 2) a branch-wise feature normalization (BFN) module for user-specific channel representation learning; and 3) an adaptive decision module for multilabel activity inference. To improve robustness under diverse and dynamic channel conditions, we introduce MixUp-based data augmentation to emulate activity overlap and reduce label ambiguity. Evaluations on the WiMANS dataset show that MuECNet achieves 64.46% (2.4 GHz) and 64.15% (5 GHz) accuracy with only 1.19-M parameters, 1.74-G FLOPs, and 0.28-s inference latency, outperforming baseline models such as ABLSTM and THAT while reducing model size by up to 75%. Ablation studies confirm the contribution of each module, with accuracy drops of up to 6.41% when removed. These results demonstrate that MuECNet provides a robust and communication-efficient solution for integrating CSI-based sensing into future wireless networks and Internet of Things (IoT) systems.

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Synthetic aperture radar image change detection based on multi-scale deep adaptive convolution and spatial-frequency dual-domain feature extraction

Wang L., Jiahui E., Zhao C., Gao W., Mathiopoulos P., Ohtsuki T.

Expert Systems with Applications 301 2026年03月

ISSN  09574174

　概要を見る

Synthetic Aperture Radar (SAR) change detection is the process of identifying image changes by comparing SAR images of the same area captured at different time intervals. Considering the interference of speckle noise and the lack of spatial adaptability, this paper proposes a spatial-frequency domain feature fusion cooperative detection framework. This method extracts local structural features in the spatial domain while incorporating high-frequency components from the frequency domain to enhance edge feature representation and improve the discrimination of change regions. Additionally, a multi-region dynamic selection mechanism is designed to adaptively capture multi-scale contextual information through cascaded convolutional layers, overcoming the limitations of fixed receptive fields in complex dynamic scenarios. Furthermore, by integrating an attention mechanism with the Swish-Gated Linear Unit (SWiGLU), the network’s feature representation capability is optimized, effectively enhancing the precision of subtle change detection and significantly reducing the impact of speckle noise. This further improves the stability and reliability of SAR change detection. Through performance evaluation on five datasets, we compare the proposed method with state-of-the-art SAR change detection techniques. The results demonstrate that our method exhibits significant advantages in terms of accuracy and kappa coefficient.

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Clock Asynchronous Traffic Signal Timing for Multi-Intersections Based on a Joint Traffic Prediction and Control Method

Wang T., Yang G., Wang L., Mathiopoulos P.T., Ohtsuki T., Ouyang M.

IEEE Transactions on Vehicular Technology 75 （ 1 ） 1111 - 1125 2026年01月

ISSN  00189545

　概要を見る

One major challenge in improving traffic efficiency for multi-intersection signal timing is the synchronous iteration across all intersections. To deal with this problem, in this paper, a novel clock asynchronous traffic signal timing method for multi-intersections based on a joint traffic prediction and control method is proposed and its performance is analyzed. To improve traffic signal timing efficiency, each intersection is viewed as an agent which by following this joint approach designs, in a flexible way, actions, states and rewards. Using traffic prediction and control data, each intersection first computes a value which corresponds to a change in the vehicle position in its lane under control and then adds up this value to other values obtained from all the lanes under control to produce the reward. Traditional methods cannot effectively coordinate multi-intersections with different signal timing time. This has led to the introduction of a novel clock consistency mechanism the operation of which is implemented through a Multi-Agent Coordinated Reinforcement Learning (MACRL) algorithm. For any traffic intersection, the mechanism can estimate, within the duration of the previous action, the impact of adjacent intersections. This is accomplished by considering the previous intersections' reward information and quantify it as its joint reward. The proposed MACRL algorithm optimizes multi-intersections traffic by jointly considering these rewards. Using real traffic data, extensive simulations show that the proposed method outperforms existing schemes under the same conditions in traffic efficiency. The method also delivers more accurate traffic signal timing, reducing congestion and improving overall efficiency.

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A Comprehensive Survey on Self-Supervised Learning for Specific Emitter Identification

Liu C., Gui G., Wang Y., Ohtsuki T., Niyato D., Shen X.

IEEE Communications Surveys and Tutorials 28   1749 - 1775 2026年

　概要を見る

The rapid proliferation of the Internet of Things (IoT) has intensified the need for strong authentication mechanisms to ensure the integrity and reliability of connected devices. Recent advancements in Deep Learning (DL)-based Specific Emitter Identification (SEI) have demonstrated significant potential in leveraging unique Radio Frequency Fingerprints (RFF) for accurate device identification and authentication. However, the efficacy of these DL-based SEI methods is critically dependent on the availability of extensive labeled datasets, which are often scarce and expensive to obtain in practical applications. To address this limitation, Self-Supervised Learning (SSL) becomes a promising solution, capable of harnessing unlabeled data to learn effective representations. Furthermore, current surveys and reviews on SEI are generally summarized from a high-level perspective, lacking a detailed discussion of SEI methods under label-limited scenarios. This article comprehensively surveys SSL-based SEI, including its motivation, definition, paradigms, related work, challenges, and future direction combined with large models. To help readers quickly engage with this field, this paper also undertakes two specific efforts: collecting and organizing currently available open-source datasets with download links and comparing various SSL-based SEI methods with related codes.

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Adaptive Task Offloading for Edge Computing in Internet of Energy via Retrieval-Augmented Generation

Zhou X., Guan X., Wang N., Chen H., Ohtsuki T., Zhang Y.

IEEE Network 2026年

ISSN  08908044

　概要を見る

Traditional task offloading in Internet of Energy (IoE) edge environments struggles to accommodate heterogeneous user tasks and dynamic system conditions. Although Large Language Models (LLMs) offer potential solutions, their deployment is hindered by the dynamic resource constraints and stringent Quality of Service (QoS) requirements of IoE. Retrieval-augmented generation (RAG) enables LLM to overcome these limitations. In this paper, we propose an RAG-empowered adaptive task offloading framework. First, the framework transforms raw IoE-collected data into LLM-comprehensible textual inputs. Second, we design an intelligent preference-matching module that guides RAG to retrieve relevant knowledge and cases using key QoS features. Third, we build a composite knowledge vector database with both positive and negative experiences. We introduce a failure severity value to quantify the importance of counterexamples and enhance decision-making robustness. Finally, we design a hybrid knowledge prompt generation module. It utilizes a hierarchical filtering strategy and optimizes the ranking of retrieved knowledge for comprehensive prompts. Experimental results demonstrate that our proposed framework significantly outperforms baselines, increasing cumulative reward by 48.4% while reducing latency and energy consumption by 44.8% and 50.9%, respectively.

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Beyond Contact: An Open-Set Biometric Identification System Using Radar-Extracted Heart Signals

Xing Z., Bouazizi M., Ohtsuki T.

IEEE Journal of Biomedical and Health Informatics 30 （ 3 ） 2718 - 2731 2026年

ISSN  21682194

　概要を見る

This paper proposes a novel radar-based framework for non-contact biometric identification through heart signal extraction, targeting secure and privacy-conscious identification scenarios. Traditional biometric methods, such as fingerprint and facial recognition, face challenges including privacy concerns, vulnerability to spoofing, and the requirement for close proximity or direct line-of-sight. Our framework addresses these issues by reconstructing electrocardiogram (ECG) signals from radar-extracted cardiac motion data and implementing an open-set person identification system. Specifically, the framework integrates ECGReconNet, a specialized deep learning model for reconstructing ECG signals from human chest wall displacement, the InceptionTime model enhanced with fixed-Class Anchor Clustering (fixed-CAC) loss for robust feature anchoring, and a hypersphere-based delineation method to differentiate known from unknown individuals. Experimental results on a public dataset demonstrate state-of-the-art performance, achieving 99.61% accuracy in closed-set identification (27 subjects) and 93.97% accuracy under challenging open-set conditions (14 known and 13 unknown subjects). However, the proposed approach exhibits limitations, including sensitivity to abrupt body movements and environmental noise, potential performance degradation under severe cardiac irregularities, and reduced efficacy with increased numbers of unknown identities.

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Toward Robust Radio Frequency Fingerprint Identification: A Federated Learning Framework With Feature Alignment

Wang Y., Cai Z., Tang T., Ohtsuki T., Gui G.

IEEE Transactions on Information Forensics and Security 21   2641 - 2651 2026年

ISSN  15566013

　概要を見る

With the growing adoption of Internet of Things (IoT) devices, ensuring the security of wireless communications has become increasingly critical. Radio frequency fingerprint identification (RFFI) has shown promise in this regard due to its capability of uniquely identifying devices. Although deep learning (DL) approaches have significantly improved RFFI performance, they typically rely on large-scale centralized data. This poses challenges in terms of privacy preservation and heterogeneous data distributions. To address the performance degradation caused by non-independent and identically distributed (non-IID) data in cross-receiver scenarios, this paper proposes a feature alignment strategy based on federated learning (FL) for RFFI. In such scenarios, due to differences in receiver hardware characteristics, deployment locations, and channel conditions, the signals captured by different receivers often exhibit distribution shifts, resulting in misaligned feature spaces across clients. The proposed method guides each client to learn aligned intermediate feature representations during local training, effectively mitigating the resulting adverse impact on model generalization. Experiments conducted on a real-world RF dataset demonstrate that the proposed method achieves higher identification accuracy and improved stability compared with representative federated baselines, including FedAvg and FedProx. The highest identification accuracy reaches 90.83%, and the performance gains are accompanied by generally reduced variance across different client configurations, highlighting the robustness and generalization capability of the proposed approach in heterogeneous wireless environments.

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Sum-Rate Maximization in Irregular STAR-RIS-Driven V2X Systems

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Wireless Communications Letters 15   1504 - 1508 2026年

ISSN  21622337

　概要を見る

The reconfigurable intelligent surface (RIS) plays a crucial role in enabling sustainable communication infrastructure for 6G networks. This letter presents a long-term net weighted sum-rate maximization problem with queue stability for irregular simultaneous transmission and reflection RIS (ISTAR-RIS)-assisted vehicular networks. The model incorporates time-varying and mobility-induced channel dynamics. A joint optimization problem of the road side unit (RSU) beamforming vector, topological selection and the phase shift matrix of ISTAR-RIS elements is formulated. The simulation results demonstrate the effectiveness of the proposed approach in maximizing the sum-rate while comparing with existing benchmark methods in ISTAR-RIS-aided wireless and device-to-device (D2D) communication.

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Ship Classification in SAR Images Based on Full-Attention ViT and Image Reconstruction

Wang L., Zhao C., Wang T., Sun B., Qi Y., Qi B., Ohtsuki T.

Lecture Notes in Electrical Engineering 1366 LNEE   501 - 509 2026年

ISSN  18761100

　概要を見る

Classification of ships in synthetic aperture radar (SAR) images plays a critical role in maritime search and rescue as well as national defense security. Over the past few years, the swift advancement rapid of deep learning has opened up innovative technological directions for precise classification of SAR ship images. However, the unique imaging mechanism of SAR images, resulting in a lack of ship samples and insufficient feature information, continues to make accurate classification of SAR ships challenging. In this study, we propose an improved vision transformer (ViT) method using a full attention (FA) mechanism, incorporating channel attention after Self-Attention, dynamically selecting channels through reweighting to enhance comprehensive feature representation. To address the issue of insufficient training samples, we designed an image reconstruction module based on SRGAN, which generates high-resolution and detailed image data through adversarial learning, effectively compensating for the sample quantity in the dataset. Ultimately, the results section demonstrates that the method introduced delivers enhanced classification accuracy relative to current methods.

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Self-Supervised Federated Learning for UAV-IoT Systems With Dynamic Non-IID Data via Model Correlation

Li Z., Bouazizi M., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 4 ） 5827 - 5841 2026年

　概要を見る

Federated learning (FL) offers significant advantages in preserving data privacy and enhancing communication efficiency, making it especially suitable for Internet of Things (IoT) networks supported by uncrewed aerial vehicles (UAVs). However, most existing FL approaches rely on assumptions of uniformly distributed, well-labeled, and large-scale datasets—conditions that rarely hold in practical UAV-based IoT scenarios. These environments typically feature small-scale, nonindependent and identically distributed (non-IID), and dynamically changing data. To address these challenges, we propose a novel self-supervised federated unsupervised learning (FUL) framework tailored for UAV-assisted IoT systems. The proposed framework comprises three key components: 1) a realistic UAV data collection model that considers limited onboard storage and mobility constraints; 2) a robust local training strategy that incorporates self-supervised regularization and a centered kernel alignment (CKA)-based similarity loss to mitigate the effects of data heterogeneity and rapid distribution shifts; and 3) an importance-aware hybrid normalized aggregation method at the global server, which leverages model divergence-based metrics to evaluate local model reliability and integrates both current and historical gradient information for stable model updates. Experimental results demonstrate that our framework achieves classification accuracies of 30.5%, 62.8%, and 70.5% under memory constraints of 500, 1000, and 2000 samples, respectively—outperforming the best baseline by 18.1%, 26.5%, and 9.1% under the same conditions. These results highlight the effectiveness of the proposed FUL framework in handling data heterogeneity and dynamic sample variations inherent in realistic UAV-enabled IoT applications.

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R2D-SLAM: A Real-Time and Robust Visual SLAM Framework for Autonomous Navigation in Dynamic Environments

Sun J., Xutian Y., Shen X., Yin Y., Zhao H., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2026年

ISSN  00189545

　概要を見る

Classical Simultaneous Localization and Mapping (SLAM) algorithms, fundamental to autonomous navigation, often assume a static environment, an assumption that fails in many real-world scenarios, resulting in significant trajectory drift. This creates a long-standing trade-off in dynamic SLAM: high-accuracy methods are typically too slow for real-time deployment, while real-time systems often suffer from reduced robustness. To address this challenge, we propose R²D-SLAM, a novel real-time visual SLAM framework that achieves both high accuracy and efficiency in dynamic environments. Our system integrates a lightweight and effective dynamic object detector, Detection Transformer (DETR), with an enhanced matching strategy, termed DEIM_Dyna, into the robust ORB-SLAM3 framework. A dedicated parallel thread performs dynamic region detection concurrently with the main SLAM pipeline, enabling early rejection of contaminated features during tracking. This ensures that pose estimation is conducted on a purified, static feature set, significantly improving robustness and accuracy. Extensive experiments on the challenging TUM and BONN dynamic datasets show that R²D-SLAM achieves new state-of-the-art performance in terms of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), while maintaining real-time processing speeds exceeding 25 FPS. R²D-SLAM effectively bridges the gap between accuracy and efficiency, offering a practical and robust solution for dynamic scene perception in autonomous vehicles and Unmanned Aerial Vehicles (UAVs).

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Optimal Carbon Emission Reduction Modeling Considering Energy Consumer Satisfaction in Cyber-Physical Energy System

Wang C., Guan X., Wang N., Chen H., Ohtsuki T., Han Z.

IEEE Internet of Things Journal 13 （ 4 ） 6508 - 6521 2026年

　概要を見る

Renewable energy has become a viable alternative to fossil fuels owing to its environmental benefits. However, its inherent uncertainty pose significant challenges. Demand response (DR) mechanisms have been developed to address these issues, facilitating renewable energy integration through consumer-side flexible resources. However, these mechanisms often affect consumer satisfaction, necessitating precise measurement and control of these impacts. In this article, we propose a two-stage electricity trading and load dispatch optimization model aimed at reducing carbon emission by promoting renewable energy accommodation, and the proposed optimization model takes into account multicategory energy consumer satisfaction. We begin by classifying consumers into distinct categories and designing tailored satisfaction functions that reflect their unique power consumption pReferences. The electricity trading and load dispatch processes are formulated as a two-stage optimization problem, which is then transformed into Markov decision processes (MDPs). A model-free framework applying two state-of-the-art deep reinforcement learning (DRL) algorithms is proposed to solve the optimization problem without requiring complex environmental modeling and prior knowledge. Numerical results demonstrate that the proposed framework outperforms benchmark algorithms regarding both consumer satisfaction preservation and carbon emission reduction.

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LRGD: Low-Rank Guided Diffusion for Robust Image Transmission in Semantic Communication

Zhao Z., Wu C., Lin Y., Zhong L., Ji Y., Ohtsuki T., Bennis M.

IEEE Transactions on Cognitive Communications and Networking 12   2439 - 2454 2026年

　概要を見る

Semantic communication has gained significant attention in modern data-intensive applications due to its efficient information transmission capabilities. While recent advancements in large language models and diffusion models have enabled generative image semantic communication in text-based and multimodal settings, existing approaches struggle with visual fidelity due to the inherent limitations of textual descriptions and the lack of dedicated design in multimodal methods. To address these issues, we propose a compressed sensing-driven multimodal semantic communication framework that transmits saliency-adaptive sparse sampling, edge contours, and textual descriptions of images at the transmitter and reconstructs images at the receiver using guided diffusion models. We innovatively design a low-rank guided diffusion (LRGD) model to adapt to this framework, employing a dynamic smoothing low-rank guidance process in the latent space using low-quality reconstructed images. This approach enables highly accurate restoration of visual details under a low sampling ratio without additional training. Compared to state-of-the-art methods, our framework uses model-independent and interpretable universal semantic representations for communication, significantly enhancing cross-model compatibility without model sharing between the transmitter and receiver. Extensive experiments on multiple datasets demonstrate that the proposed method achieves robust and superior image reconstruction performance across various noisy environments while compressing data to 5.7% of the original image size.

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Long-Term Sum-Rate Maximization in Irregular RIS-Aided V2X Communications Using Lyapunov Optimization

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Open Journal of the Communications Society 7   2446 - 2463 2026年

　概要を見る

In emerging smart cities and intelligent transportation systems (ITS) ecosystems, sixth-generation (6G) vehicular networks demand sustainable wireless environments capable of adapting to high mobility and dynamic propagation conditions. In dynamic vehicular networks, irregular reconfigurable intelligent surface (IRIS) with non-uniformly distributed elements over a larger surface offer improved coverage, reduced interference, and lower power consumption. This article undertakes a rigorous investigation into IRIS-assisted downlink vehicular communication in the context of Doppler shift, with the objective of optimizing the long-term net weighted average sum-rate under both perfect and imperfect channel state information (CSI). This task entails a comprehensive joint optimization of IRIS topology selection, phase-shift matrix, and beamforming vectors, while ensuring queue stability. To address this stochastic network optimization problems involving time-varying system dynamics, a Lyapunov optimization-based framework is proposed. This framework transforms the stochastic problem into a deterministic form, ensuring queue stability without requiring knowledge of future channel conditions. Subsequently, a joint optimization strategy based on the block coordinate descent (BCD) algorithm is applied to make optimal decisions of IRIS topology selection, phase-shift matrix, and beamforming vectors in each time slot. To validate the effectiveness of the proposed framework, extensive simulations were conducted and benchmarked against two established schemes: one employing conventional IRIS configurations in wireless networks and the other utilizing non-orthogonal multiple access (NOMA) techniques. The results consistently demonstrate that the proposed IRIS-assisted Lyapunov optimization framework significantly outperforms both benchmarks in terms of achievable sum-rate and maintains queue stability.

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Lightweight Regularized Network for Multilabel Indoor HAR in Multiuser CSI Environments With Uncertainty Quantification

Miao F., Liu C., Lu Z., Shan L., Takyu O., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 4 ） 6475 - 6484 2026年

　概要を見る

Human activity recognition (HAR) with Wi-Fi channel state information (CSI) is attractive for privacy-preserving, device-free sensing; yet, real deployments still struggle with three coupled issues: robustness across rooms and bands, efficiency on edge hardware, and unified support for multiple tasks. We present UN-2DCNN, a lightweight 2-D convolutional neural network (CNN) pipeline tailored to indoor, multiuser CSI sensing. The design reduces temporal redundancy via a simple temporal skipping augmentation, learns a compact 128-D representation with a small CNN + global average pooling (GAP) backbone, and injects reliability feedback through uncertainty-aware feature scaling (UAFS): Stage-1 predictive entropy is mapped to a gating weight that rescales features before a second decision head. A channel-attention MLP further suppresses spurious subcarrier responses. Evaluated on a recent multiuser CSI benchmark across classrooms, meeting rooms, and empty environments at 2.4/5 GHz, UN-2DCNN consistently outperforms competitive recurrent neural network (RNN)/Transformer baselines while using only ~1M parameters and maintaining sub-2-s test-time latency. Beyond higher accuracy, the model exhibits faster, smoother convergence and improved calibration (fewer overconfident errors). Ablations confirm that removing attention, UAFS, or the second-stage head yields consistent drops, and simple temporal skipping on the data side complements model-side selectivity. These results indicate that reliability-aware, lightweight designs can deliver practical accuracy–efficiency tradeoffs for CSI-based perception on edge/IoT platforms.

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Integrated Deployment and Resource Allocation in Multi-Layer UAV-Enabled NOMA Wireless Caching Networks

Yin Y., Bouazizi M., Hu B., Gui G., Ohtsuki T.

IEEE Internet of Things Journal 13 （ 7 ） 14898 - 14911 2026年

　概要を見る

Conventional multi-unmanned aerial vehicle (UAV) assisted non-orthogonal multiple access (NOMA) wireless caching networks (WCNs) usually operate in a distributed and non-collaborative manner, where each UAV serves users independently without coordination or relay support. When UAVs move beyond the communication range of ground base stations (BSs), backhaul disruption occurs, leading to high user latency and limited system scalability. To address these issues, we propose a multi-layer UAV-assisted NOMA WCN architecture, where a primary UAV (PUAV) communicates with the BS and cooperates with multiple secondary UAVs (SUAVs). The PUAV not only acts as a control and coordination hub but also serves as a relay for content transmission to SUAVs when necessary. To minimize user transmission latency, we propose a joint iterative algorithm that integrates user clustering, user pairing, power allocation, and UAV deployment. First, we develop an Advanced Balanced K-Means++ (ABKM) algorithm to ensure that each cluster contains a balanced number of users and to reduce the distance between users and their serving SUAVs. Next, we derive the NOMA power allocation factor that minimizes user transmission latency, ensuring efficient resource distribution among all paired users. Furthermore, we analyze the impact of PUAV and SUAV placement on user latency and propose a two-stage particle swarm optimization (PSO)-based algorithm to iteratively optimize the deployment of all UAVs. Finally, the user pairs and power allocation are jointly optimized based on the updated deployment of all UAVs to further reduce user latency. Simulation results show that, compared with a single-layer UAV architecture and benchmark schemes, the proposed multi-layer design with joint optimization achieves lower user latency. Additionally, comparisons with the optimal power allocation search method confirm the validity of the derived NOMA power allocation factor.

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Handover Optimization for UAV-Assisted LEO Satellite Networks Based on IPPO and Three-Sided Matching Theory

Yang F., Li M., Wang K., Si P., Ohtsuki T., Richard Yu F.

IEEE Internet of Things Journal 13 （ 1 ） 152 - 166 2026年

　概要を見る

Due to the triple mobility of mobile users (MUs), unmanned aerial vehicle (UAV) relays, and low Earth orbit (LEO) satellites, handover becomes a critical and challenging issue for maintaining the continuity and quality of communication services in UAV-assisted LEO satellite networks. This article proposes a distributed handover decision-making process aimed at improving scalability and reducing communication overhead. The handover problem is modeled as a decentralized Markov decision process (DEC-MDP) with the objective of maximizing the total end-to-end (E2E) throughput. We design an independent proximal policy optimization-based distributed intelligent handover (IPPO-DIH) algorithm within a centralized training with decentralized execution framework to solve the DEC-MDP. To analyze the theoretical optimal E2E throughput, we eliminate the correlation between handover decisions at different time steps. A three-sided matching algorithm with theoretical convergence guarantees is designed to obtain a stable matching among MUs, UAV relays, and LEO satellites at each time step. These stable matchings are combined to provide a theoretical performance benchmark for the handover algorithms. Simulation results validate the convergence of the proposed IPPO-DIH and three-sided matching algorithms. Additionally, the total E2E throughput achieved by the IPPO-DIH algorithm approaches the theoretical performance benchmark and outperforms typical handover algorithms.

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Generative Model-Aided Continual Learning for CSI Feedback in FDD mMIMO-OFDM Systems

Liu G., Cao Y., Ohtsuki T., He J., Mumtaz S.

IEEE Signal Processing Letters 33   1013 - 1017 2026年

ISSN  10709908

　概要を見る

Deep autoencoder (DAE) frameworks have demonstrated their effectiveness in reducing channel state information (CSI) feedback overhead in massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) systems. However, existing CSI feedback models struggle to adapt to dynamic environments caused by user mobility, requiring retraining when encountering new CSI distributions. Moreover, returning to previously encountered environments often leads to performance degradation due to catastrophic forgetting. Continual learning involves enabling models to incorporate new information while maintaining performance on previously learned tasks. To address these challenges, we propose a generative adversarial network (GAN)-based learning approach for CSI feedback. By using a GAN generator as a memory unit, our method preserves knowledge from past environments and ensures consistently high performance across diverse scenarios without forgetting. Simulation results show that the proposed approach enhances the generalization capability of the DAE framework while maintaining low memory overhead. Furthermore, it can be seamlessly integrated with other advanced CSI feedback models, highlighting its robustness and adaptability.

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