論文 - 大槻　知明

Enhanced Few-Shot Malware Traffic Classification via Integrating Knowledge Transfer With Neural Architecture Search

Zhang X., Wang Q., Qin M., Wang Y., Ohtsuki T., Adebisi B., Sari H., Gui G.

IEEE Transactions on Information Forensics and Security 19   5245 - 5256 2024年

研究論文（学術雑誌）, 責任著者,  ISSN  15566013

　概要を見る

Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT. In this paper, we propose a few-shot MTC (FS-MTC) method combining knowledge transfer and neural architecture search (i.e. NAS-based FS-MTC) with limited training samples as well as acceptable computational resources, in order to mitigate the identified challenges. Specifically, our proposed method first converts the raw network traffic into traffic images through data pre-processing to serve as input data for the neural network. Second, we use neural architecture search to adaptively search for the effective feature extraction model on the source domain (including Edge-IIoTset, Bot-IoT, and benign USTC-TFC2016). Third, the searched model is pre-trained on source task to achieve the generic feature representation of malware traffic. Finally, we only use few-shot malware traffic samples to fine-tune the pre-trained model to quickly adapt to new types of MTC tasks in realistic network environments. The experimental results show that the proposed NAS-based FS-MTC method has great scalability and classification performance in different FS-MTC tasks, including 5-way K-shot USTC-TFC2016 dataset and 10-way K-shot CIC-IoT dataset. Compared with state-of-the-art methods in the field of malware classification, the proposed NAS-based FS-MTC has higher classification accuracy. Especially in the 1-shot case of the USTC-TFC2016 dataset, its average accuracy is as high as 86.91%.

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Age-Based Federated Learning Approach to In-Network Caching: An Online Scheduling Policy

Cao Y., Maghsudi S., Ohtsuki T.

IEEE International Conference on Communications    1437 - 1442 2024年

ISSN  15503607

　概要を見る

We develop an accurate real-time scheduling framework for federated learning (FL) in wireless caching networks to guarantee the successful delivery of files at a low cost and with a short delay. The following persisting challenges motivated our work: i) Enforcing an excessive number of FL model update per communication round is infeasible due to the limited backhaul spectrum; ii) Naive scheduling policy accounting for FL model update renders service backlogs, thus leading to network parameter staleness and in-network caching utility (ICU) deterioration. Optimal scheduling in FL is challenging, as the mobile users' preferences for content, request patterns, and network traffic are dynamic and unknown. To tackle that challenge, we first formulate an instantaneous ICU optimization problem against the stale FL models. Afterward, based on the concept of age-of-update (AoU), we propose a federated learning with an unsatisfactory set selection (FedUSS) approach capable of executing the multiple-tasks of short-term predictions and making cache replacement decisions at low cost. Theoretical and numerical analyses manifest the effectiveness of our approach.

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A Low-Complexity Clustering-Aided DQN Method for Dynamic Antenna Control in HAPS

Bouazizi M., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

This paper aims to address the issue of low through-put for users, caused by the random movement of High-Altitude Platform Stations (HAPS) due to winds. The proposed solution involves developing an Equal Clustering (EC) approach that groups users into high-density clusters, ensuring an equal num-ber of users in each cluster while maintaining low complexity. To further enhance the system's throughput performance, we fine-tune the antenna parameters using a Deep Q-Network (DQN) and the results of the EC clustering. To evaluate the effectiveness of the proposed method, we compare it with three Reinforcement Learning (RL)-based approaches and a K-Means clustering-based method. Simulation results indicate that both the EC method and the EC-aided DQN method successfully enhance the Cumulative Distribution Function (CDF) performance of throughput distribution when compared to the RL-based method for both rotation and shift scenarios. Furthermore, the EC-aided DQN method outperforms the K-Means clustering-based method in terms of the CDF of throughput performance.

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A CSI-based Cross-subject Transfer Learning Method Using Network Freezing

He Z., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    3515 - 3520 2024年

ISSN  23340983

　概要を見る

Currently, the field of wireless sensing is moving towards non-contact and easy-to-deploy passive sensing technologies. Among these, sensing techniques based on WiFi channel state information (CSI) have emerged as highly promising due to their excellent performance and suitability for current sensing needs. However, despite the potential of WiFi CSI-based sensing technologies, there are still pressing issues that need to be addressed. Traditional WiFi CSI-based methods face challenges such as weak generalization ability and poor robustness, especially when the sensing target and environment change, which hinders the further development. To address this problem, this paper introduces the transfer learning method and combines it with the deep neural network to propose a novel cross-subject WiFi sensing method. Specifically, this method utilizes the source domain-target domain partitioning approach in transfer learning. In the source domain, deep neural networks are trained to obtain the source domain feature model, which is then transferred to the target domain. Fine-tuning is performed using techniques such as network freezing, aiming to meet the faster and more accurate sensing task demands of cross-subject. From the final experimental results, the novel cross-subject transfer learning method proposed in this paper achieved higher recognition accuracy and shorter training time. Moreover, the implementation of network freezing further enhanced the performance and efficiency of cross-subject, achieving up to 86% performance on the dataset used in this paper.

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A Comparative Study of Methods of Person Identification Using Radar-Extracted Heartbeat Signals

Liu K., Bouazizi M., Xing Z., Ohtsuki T.

IEEE Access 12   152196 - 152211 2024年

　概要を見る

In recent years, non-contact biometric identification has garnered significant attention owing to its flexibility and its capacity to ensure privacy and confidentiality. Previous research has predominantly centered on harnessing cardiac radar signals detected by radar systems. This paper aims to compare two methods for person identification utilizing heartbeat signals extracted from radar data. In the first approach, we directly employ the heartbeat time series data as input for a deep learning model called InceptionTime dedicated to person identification. The second method explores the viability of using spectrograms, which are images generated from cardiac radar heartbeat signals, combined with other deep learning models, ResNet and a small convolutional neural network (CNN) model built by us, to achieve the same goal. Additionally, we assessed the robustness of both methods by introducing white Gaussian noise into the signals and evaluating their performance under different noise levels. For the second method, we implemented three image denoising methods: autoencoder, deep image prior (DIP), and Dilated-Residual U-Net (DRU-Net) to denoise the spectrograms after adding noise in the time domain. Experimental results reveal that the accuracy of person identification using spectrograms is 100% without adding noise based on the public dataset. The identification accuracy is the same as that using time series data. Following the introduction of noise, the accuracy of both methods decreases; however, the method based on spectrograms exhibits a more pronounced decline in accuracy compared to the approach using time series data directly especially for the small CNN model we built. The image-based method with DIP image denoiser shows outstanding performance when the value of noise is high compared with InceptionTime.

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3D-LiDAR-Based Fall Detection by Dynamic Non-Linear Mapping with LSTM

Meng X., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications    818 - 823 2024年

ISSN  15503607

　概要を見る

With the aging of the population, the health of the elderly people has become a concern for many families. Monitoring of elderly people and the detection of their activities have become hot research topics. Several approaches have been proposed in the literature to detect such activity (e.g., falls), but their reliance on RGB cameras poses a serious threat to the privacy of the elderly. Therefore, Light Detection and Ranging (LiDAR) has attracted the attention of researchers. In this paper, we propose a novel method for fall detection using transfer learning and dynamic non-linear mapping to extract the human skeletons from depth images and implement a fall detection method using a Long Short-Term Memory (LSTM) neural network. We collected RGB images and depth images using 3D-LiDAR. The RGB images are used as a ground-truth for annotation and evaluation, whereas depth images are used as input to our proposed method. For fall detection, we achieved 98.5% accuracy, 97.0% precision, 100% recall, and 98.5% F1 score.

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Beamforming Design using UE Positions and 3D Terrain-Building in HAPS System

Li Z., Yang S., Yin Y., Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

High Altitude Platform Stations (HAPS) are instrumental in wireless communications, providing enhanced connectivity and extensive coverage by complementing ground-based infrastructure where its expansion is limited. HAPS enhance wireless communications by employing advanced beamforming technology, traditionally based on 2D with free space path loss models. Addressing the inadequacy of these models in different areas, our research introduces a novel beamforming strategy that incorporates detailed 3D geographic and architectural information. This approach models line-of-sight (LOS) and non-line-of-sight (NLOS) conditions with 3D information and optimizes beamforming patterns using Deep Reinforcement Learning (DRL). Our results indicate that by incorporating 3D information, the beamforming performance in terms of average throughput and SINR is markedly enhanced across all user equipments (UEs), compared to traditional 2D approaches. By taking 3D information into account, beamforming in HAPS systems becomes more equitable.

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Toward Building Trust in Machine Learning Models: Quantifying the Explainability by SHAP and References to Human Strategy

Li Z., Bouazizi M., Ohtsuki T., Ishii M., Nakahara E.

IEEE Access 12   11010 - 11023 2024年

　概要を見る

Local model-agnostic Explainable Artificial Intelligence (XAI), such as LIME or SHAP, has recently gained popularity among researchers and data scientists for explaining black box Machine Learning (ML) models. In the industry, practitioners focus not only on how these explanations can validate their models but also on how they can help maintain trust from end-users. Some studies attempted to measure this ability by quantifying what they refer to as the explainability or interpretability of ML models. In this paper, we introduce a new method for measuring explainability with reference to an approximated human model. We develop a human-friendly interface to strategically collect human decision-making and translate it into a set of logical rules and intuitions, or simply annotations. These annotations are then compared with the local explanations derived from common XAI tools. Through a human survey, we demonstrate that it is possible to quantify human intuition and empirically compare it to a given explanation, enabling a practical quantification of explainability. By relying on this new method, we identified several potential flaws in today's ML selection process. Furthermore, we demonstrate how our method can help to better evaluate ML models.

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Source CSI Dataset for Multi-Task CSI Feedback

Inoue M., Ohtsuki T.

Proceedings IEEE Consumer Communications and Networking Conference Ccnc    1042 - 1043 2024年

ISSN  23319860

　概要を見る

The muti-task Channel State Information (CSI) feedback is a downlink CSI feedback approach. It focuses on Clustered Delay Line (CDL) channel models as the channel environments. There are five different CDL channel models, from CDL-A to CDL-E. In this method, the autoencoder (source model) is pre-trained using the CDL-ALL dataset, which is an equal mixture of each CDL channel dataset (source data) rather than a single CDL channel dataset. The decoder at the BS is subsequently fine-tuned using a small amount of CSI data from the target channel environment (target data) to generate a target model. The source model has a significant influence on the reconstruction performance of the target model, the amount of data, and the number of training epochs required to obtain it. This paper investigates the mixing ratio of each CDL channel dataset used as source data to enhance the CSI reconstruction performance of the source model in the muti-task CSI feedback. Furthermore, we explore the possibility of enhancing the CSI reconstruction performance of the target model by employing the improved source model for fine-tuning the target model. To determine the mixing ratio, two distinct criteria for source model performance are employed. The simulation results identified several mixing ratios of source data in these criteria that improve the reconstruction performance of both the source and the target models compared to the use of CDL-ALL as a source dataset in the multi-task CSI feedback.

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Rough-to-Fine Model-based Non-contact Heart Rate Estimation using MIMO FMCW Radar

Shi X., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    920 - 925 2024年

ISSN  23340983

　概要を見る

MIMO FMCW Radar, a non-contact heart rate (HR) monitoring technology, has emerged as one of the superior alternatives to contact-based sensors, providing precise HR estimation without the drawbacks of discomfort or privacy concerns, and excelling in diverse environmental conditions. Recent HR estimation studies using conventional methods have achieved high accuracy but face challenges with lengthy processing times and sensitivity to experimental conditions, affecting real-time application and robustness. A previous Deep learning (DL)-based approach improves on these aspects but is limited by the range resolution of SISO FMCW Radar and the requirement for close positioning of subjects. Additionally, this method has inability to utilize contextual information from adjacent time windows restricts its effectiveness for time series analysis. Thus, our proposed method combines a Curve-Length (CL) approach with a DL rough-to-fine model, addressing the limitations of previous studies and improving HR estimation accuracy and robustness. This integrated process begins with human location detection through a CL-based approach and is followed by a two-phase rough-to-fine HR estimation. The experimental results show significant improvement over the conventional methods.

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Remote Inter-beat Interval Estimation: A Signal Reconstruction Approach with Multi-channel Input and Channel-Wise Attention Mechanism

Wang S., Bouazizi M., Ohtsuki T.

2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 2024年

　概要を見る

This paper presents a radar-based heart rate monitoring and Inter-beat interval (IBI) estimation. Traditional IBI estimation relies on peak detection combined with signal processing techniques. Our approach uses signal reconstruction with a neural network, which significantly improves the accuracy and robustness of IBI estimation compared to conventional methods. By utilizing multi-channel input data, our approach effectively mitigates various sources of noise and interference, resulting in highly accurate and reliable IBI predictions. Instead of relying solely on peak detection, we use the U-net architecture and cross-channel attention mechanism to reconstruct the triangular waveform generated from the ground truth ECG signals. The incorporation of multi-channel input and channel-wise attention mechanism improves our model’s ability to discriminate and emphasize critical features from different input channels, further improving IBI estimation accuracy. To evaluate the accuracy and robustness of our proposed method, we trained our model on the open-source dataset [1], and then performed leave-one-subject-out validation, which ensures our approach to be evaluated only on unseen independent subjects. We have achieved a Root Mean Square Error (RMSE) of 26.7 ms for the IBI of each heart-beat. Our results demonstrate the transformative potential of adopting signal reconstruction methods supported by state-of-the-art deep learning techniques. This shift in perspective promises more accurate and robust heart rate and IBI estimation, opening new avenues for improving the accuracy and reliability of cardiac monitoring systems.

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Object Detection on SAR Images Via YOLOv10 and Integrated ACmix Attention Mechanism

Wang Z., Miao F., Huang Y., Lu Z., Ohtsuki T., Gui G.

2024 6th International Conference on Robotics Intelligent Control and Artificial Intelligence Ricai 2024    756 - 760 2024年

　概要を見る

With the rapid advancement of deep learning techniques, object detection on Synthetic Aperture Radar (SAR) images has shown increasing potential in recent years. However, due to the unique microwave imaging mechanism of SAR, targets can easily be confused with background structures like harbors and aircraft aprons, leading to poor recognition accuracy. In this paper, we propose YOLOv10n-ACmix, an enhanced object detection model that integrates the ACmix attention mechanism into the YOLOv10 framework to improve feature extraction and detection precision. Through experiments conducted on the SAR-aircraft and MSAR datasets, YOLOv10n-ACmix consistently outperformed YOLOv5, YOLOv8 and YOLOv10 in precision, recall, and mean Average Precision (mAP). The inclusion of the ACmix mechanism enables the model to focus more effectively on critical features, resulting in more accurate detections. Despite a slight increase in computational complexity, the significant improvements in detection performance make YOLOv10n-ACmix a robust choice for high-precision object detection in SAR images. This work highlights the potential of attention mechanisms to enhance object detection in complex, large-scale SAR image datasets.

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Multi-UAV-Assisted Emergency Caching Networks: UAV Location and NOMA Power Optimization

Yin Y., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

In this paper, we propose an unmanned aerial vehicles (UAVs) assisted emergency caching framework based on non-orthogonal multiple access (NOMA), involving a primary UAV and several secondary UAVs. The primary UAV obtains rescue information from a remote base station (BS) and sends it to secondary UAVs hovering at designated locations to serve ground users. Specifically, a multi-objective optimization problem is formulated to maximize the number of files that can be successfully decoded in the primary UAV and minimize the number of user clusters for secondary UAVs. The multi-objective optimization problem is decomposed into three sub-problems: primary UAV deployment, power allocation, and secondary UAV deployment. First, a primary UAV deployment scheme is proposed to maximize the number of received files subject to the constraint of the demand for the line-of-sight (LoS) link probability between the primary UAV and the ground users. Subsequently, to achieve the same goal, a closed-form solution for the NOMA power allocation factor of the files is derived. Finally, due to the limited range of services of the secondary UAVs, we propose a secondary UAV deployment scheme to minimize the number of their flights, which can save energy and reduce user latency. Extensive numerical results demonstrate that the proposed algorithms outperform the random scheme and the worst scheme.

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Multi-Dimensional Representation for Semantic Communication: A New Horizon for Customized Visualization of Shared Knowledge

Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

Semantic communication plays a crucial role in human interactions, allowing for the exchange of complex ideas and concepts. In this paper, we introduce a novel approach to semantic communication leveraging image generative Artificial Intelligence (AI) models, specifically stable diffusion models. Unlike conventional works, our system enables the transmission of images through a physical channel by transforming them into multi-dimensional semantic representations consisting of text descriptions, low-resolution sketches, and pose information. At the receiver's end, these semantic representations are used to reconstruct the original image using a trained stable diffusion model. The benefits of our approach include reduced transmission bandwidth requirements, flexibility in reconstruction styles, adaptability to multiple receivers' preferences, and the ability to omit unwanted image elements. We present preliminary results demonstrating the feasibility and effectiveness of our method. The similarity score between the transmitted images and reconstructed ones reach values ranging between 0.015 and 0.029 in Root Mean Square Error (RMSE) and between 0.993 and 0.998 using a Siamese network.

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mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud

Al M.A., Shi X., Mondher B., Ohtsuki T.

IEEE International Conference on Communications    2161 - 2166 2024年

ISSN  15503607

　概要を見る

Pose estimation and human action recognition (HAR) are pivotal technologies spanning various domains. While the image-based pose estimation and HAR are widely admired for their superior performance, they lack in privacy protection and suboptimal performance in low-light and dark environments. This paper exploits the capabilities of millimeter-wave (mmWave) radar technology for human pose estimation by processing radar data with Graph Neural Network (GNN) architecture, coupled with the attention mechanism. Our goal is to capture the finer details of the radar point cloud to improve the pose estimation performance. To this end, we present a unique feature extraction technique that exploits the full potential of the GNN processing method for pose estimation. Our model mmGAT demonstrates remarkable performance on two publicly available benchmark mmWave datasets and establishes new state of the art results in most scenarios in terms of human pose estimation. Our approach achieves a noteworthy reduction of pose estimation mean per joint position error (MPJPE) by 35.6% and PA-MPJPE by 14.1% from the current state of the art benchmark within this domain.

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Massive MIMO Belief Propagation Detection Using DIP with DNN-Trained Scaling Factor

Tachibana J., Bouazizi M., Ohtsuki T.

IEEE Wireless Communications and Networking Conference Wcnc 2024年

ISSN  15253511

　概要を見る

Belief propagation (BP) detection is a technique for separating and detecting incoming signals with minimal complexity in massive multiple-input multiple-output (MIMO) systems. However, because of the interference and noise that remain in the received signals even after attempting to remove them through conventional techniques, errors manifest in the transmitted messages. Due to the MIMO channel's numerous loops, a message containing mistakes spreads across the factor graph leading to a degradation in the BP's convergence properties and detection performance. In this paper, we propose a BP detection using deep image prior (DIP) with deep neural network (DNN)-trained scaling factor. By applying DIP to the BP detection algorithm, we achieve a reduction in residual interference and noise. Post DIP application, there is a modification in the variance of both interference and noise components. To align it more accurately with its true value and enhance message reliability, we adjust the variance using scaling factors trained through DNN-based damped BP (DNN-dBP). Using computer simulations, we demonstrate that applying DIP helps decrease the power of the residual interference and noise after removing the interference at each iteration in the BP detection. It is also shown that the proposed method improves the detection performance compared to the normal BP detection, BP detection without DIP when training the scaling factors of the variance, and BP detection to which DIP is applied without training the scaling factors of the variance.

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Low Complexity CSI Feedback Method Using Reformer

Saito Y., Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

To leverage antenna diversity effectively in Massive Multiple-Input Multiple-Output (MIMO) systems, Channel State Information (CSI) utilization at the base station (BS) is crucial. In Frequency Division Duplexing (FDD) MIMO setups, downlink CSI is acquired by user equipment (UE) and transmitted back to the BS. However, this feedback overhead diminishes communication throughput, necessitating CSI compression technologies. Methods such as traditional compression or more advanced ones such as Transformer-based neural networks which aim to address this issue suffer from low compression rates or high computational cost. In this context, Reformers emerge as a potential solution as they employ Locality-Sensitive Hashing (LSH) attention, offering a comparable accuracy to Transformers but with reduced computational demands. In this paper, we propose a CSI feedback method using Reformer. Specifically, by replacing the multi-head attention of the CSI feedback method using Transformer with the LSH attention of Reformer, we aimed to achieve similar CSI reconstruction accuracy while reducing computational complexity. Computer simulations confirm that the CSI feedback method using Reformer reduces computational complexity by about 12% while achieving comparable CSI reconstruction accuracy compared to the CSI feedback method using Transformer.

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Hypersphere Projection-Guided Radio Frequency Fingerprinting Authentication in the Open World

Fu X., Wang Y., Lin Y., Zhang Q., Gui G., Ohtsuki T., Sari H.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

In this paper, we introduce an innovative Radio Frequency Fingerprinting (RFF)-based device authentication scheme for the Internet of Things (IoT), a network marked by extensive interconnections and interactions among various entities. Our approach, designed for an open and dynamic communication environment, not only identifies devices encountered during training but also effectively rejects those not previously seen. The scheme employs a hypersphere projection for feature embedding, strategically avoiding the need to optimize intra-device variations in the radial direction. It uses a K-Means-based binary classifier for initial device assessment based on cosine similarity scores, followed by a SoftMax classifier for precise identification of known devices. Our extensive numerical analysis confirms that this method delivers superior performance, setting a new benchmark in RFF authentication for IoT security.

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Fully Autonomous Distributed Transmission Parameter Selection Method for Mobile IoT Applications Using Deep Reinforcement Learning

Sugiyama S., Makizoe K., Arai M., Hasegawa M., Otsuki T., Li A.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

With the rapid increase in Internet of Things (IoT) devices, packet collision has become a serious problem in Long Range (LoRa) communication. Furthermore, the rising demand for mobile IoT applications necessitates parameter allocation methods that take into account the mobility of End Devices (EDs). However, the existing transmission parameter selection methods considering mobility are almost centralized designs, which may increase the probability of interference and system latency. The decentralized design is few and requires prior information on ED, which may cause scalability issues, e.g., adding ED to an existing network. To solve the problems above, a method through a fully autonomous distributed design supposing the mobility of ED using Double Deep Q Network (DDQN) is proposed in this paper. In the proposed method, ED trains the model based on ACKnowledge (ACK) packets and its location information without any prior information. Performance evaluation results show that the proposed method can achieve a higher packet delivery rate than other autonomous distributed methods in mobile IoT scenarios.

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Enhanced User Clustering and Pairing Scheme for NOMA-Aided UAV Networks

Yin Y., Bouazizi M., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

The increasing demand for spectral efficiency and system capacity in communication networks has driven the integration of Non-Orthogonal Multiple Access (NOMA) technology with Unmanned Aerial Vehicle (UAV) networks. In this paper, we propose an enhanced user clustering and pairing scheme for NOMA-aided UAV networks. Our study aims to maximize the minimum user throughput by proposing an Advanced Balanced K-Means (ABKM) algorithm, based on the traditional K-Means (KM) and Balanced K-Means (BKM) algorithms. The ABKM algorithm addresses the issue in the BKM algorithm where some users are assigned to sub-optimal clusters, resulting in increased distances to the cluster centroids, while simultaneously ensuring balanced clustering. Through extensive numerical simulations, we demonstrate that the ABKM algorithm significantly outperforms KM and BKM algorithms in terms of the minimum user throughput. The results highlight the potential of the proposed ABKM algorithm to enhance system performance and ensure fair resource allocation in NOMA-aided UAV networks, making it a promising solution for future 6G wireless communication systems.

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