ICT Express最新文献

英文中文

UAV-Based Vehicle Detection and Tracking in Urban Environments Using Multi-Task CNN and Deep Reinforcement Learning 基于多任务CNN和深度强化学习的城市环境中基于无人机的车辆检测与跟踪

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.09.016

Chae-Won Park , Ji-Hye Lim , Seung-Jun Lee , Keum-Seong Nam, Qin Yang, Sang-Jo Yoo

This paper presents a real-time vehicle detection and tracking system using an unmanned aerial vehicle (UAV) to address challenges in dynamic urban environments. The system combines a convolutional neural network (CNN) for vehicle detection with a deep Q-network (DQN)-based navigation policy for continuous tracking. Input images are enhanced using contrast limited adaptive histogram equalization (CLAHE) and unsharp masking. The CNN jointly predicts vehicle center coordinates and probabilistic heatmaps, while a self-attention module captures long-range spatial dependencies to improve detection under clutter and occlusion. The DQN is trained on multi-step spatiotemporal states to learn optimal UAV movement strategies under diverse weather and structural conditions. Experiments conducted in a three-dimensional (3D) urban simulation environment using Unity’s machine learning agents (ML-Agents) show that the self-attention design reduced pixel-level localization error by about 7%, and the DQN-based tracking policy achieved stable convergence after approximately 2000–3000 episodes. These results demonstrate high tracking accuracy and system stability, highlighting the potential of the proposed approach for real-world UAV-based traffic monitoring applications.

2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

本文提出了一种使用无人机（UAV）的实时车辆检测和跟踪系统，以应对动态城市环境中的挑战。该系统结合了用于车辆检测的卷积神经网络（CNN）和用于持续跟踪的基于深度q网络（DQN）的导航策略。输入图像增强使用对比度有限的自适应直方图均衡化（CLAHE）和非锐利掩蔽。CNN联合预测车辆中心坐标和概率热图，而自关注模块捕获远程空间依赖关系，以提高在杂波和遮挡下的检测。DQN在多步时空状态下进行训练，以学习不同天气和结构条件下的最优无人机运动策略。在三维（3D）城市模拟环境中使用Unity的机器学习代理（ML-Agents）进行的实验表明，自关注设计将像素级定位误差降低了约7%，并且基于dqn的跟踪策略在大约2000-3000集后实现了稳定收敛。这些结果证明了高跟踪精度和系统稳定性，突出了该方法在现实世界中基于无人机的交通监控应用的潜力。2018韩国通信与信息科学研究所。这是一篇基于CC by-nc-nd许可（http://creativecommons.org/licenses/by-nc-nd/4.0/）的开放获取文章。

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引用次数: 0

RIS-assisted UAV communications: A review of system models, frameworks and outage performance ris辅助无人机通信：系统模型、框架和中断性能的回顾

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.10.006

Saddaf Rubab , Ghulam E Mustafa Abro , Hifza Mustafa , Saad Khan Baloch , Sufyan Ali Memon , Nasir Saeed

Integration of Reconfigurable Intelligent Surfaces (RIS) with Unmanned Aerial Vehicles (UAVs) generates a revolutionary paradigm for next-generation wireless communications, particularly in IoT and 6G applications. UAVs provide adaptable and versatile deployment options; yet, they encounter obstacles including signal degradation, restricted Line-of-Sight (LoS), and computing limitations. RIS technology mitigates these constraints by rearranging the wireless propagation environment to improve signal quality, energy efficiency, and connection dependability. This survey offers a detailed examination of RIS-assisted UAV communication systems, addressing system models, channel characteristics, and essential performance metrics including SNR, BER, and outage probability. We further investigate control schemes utilising deep reinforcement and federated learning for real-time trajectory optimisation and reconfigurable intelligent surface phase adjustment. This study presents the novel notion of multi-edge cooperative frameworks alongside classical designs, wherein UAVs delegate demanding tasks – such as trajectory planning and channel estimation – to proximate edge servers, including mobile base stations or other UAVs. These architectures provide diminished latency, enhanced scalability, and immediate flexibility. The paper also discusses outstanding issues in physical-layer security, edge coordination, and deployment complexity. This study establishes a standard for creating resilient, intelligent, and scalable RIS-UAV communication systems that meet the requirements of future smart cities and critical mission settings.

可重构智能表面（RIS）与无人机（uav）的集成为下一代无线通信，特别是在物联网和6G应用中，创造了革命性的范例。无人机提供适应性强和多用途的部署选项；然而，它们遇到的障碍包括信号退化、受限的视距（LoS）和计算限制。RIS技术通过重新安排无线传播环境来改善信号质量、能源效率和连接可靠性，从而减轻了这些限制。该调查提供了ris辅助无人机通信系统、寻址系统模型、信道特性和基本性能指标（包括信噪比、误码率和中断概率）的详细检查。我们进一步研究了利用深度强化和联邦学习进行实时轨迹优化和可重构智能表面相位调整的控制方案。本研究提出了与经典设计相结合的多边缘协作框架的新概念，其中无人机将要求苛刻的任务（如轨迹规划和信道估计）委托给近距边缘服务器，包括移动基站或其他无人机。这些体系结构减少了延迟、增强了可伸缩性和即时灵活性。本文还讨论了物理层安全、边缘协调和部署复杂性方面的突出问题。该研究为创建弹性、智能和可扩展的RIS-UAV通信系统建立了标准，以满足未来智慧城市和关键任务设置的要求。

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引用次数: 0

Graph neural networks for minimizing worst-case outage probability in dense spectrum-sharing networks 密集频谱共享网络中最小化最坏停机概率的图神经网络

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.11.014

Liang Han, Xiaosen Shi, Tingting Lu

The rise of wireless devices makes interference a key challenge for reliable communication in dense spectrum-sharing networks. This paper proposes a graph neural network (GNN)-based power control algorithm to minimize the worst-case outage probability by using statistical channel state information (CSI), i.e., position information. By representing the network as a fully connected directed graph with node and edge features derived from transceiver positions, the GNN employs message-passing layers to aggregate interference patterns and infer near-optimal transmit powers. Simulation results demonstrate the scalability and generalization capability of the proposed method, confirming its suitability for real-time deployment in large-scale wireless systems.

无线设备的兴起使得干扰成为密集频谱共享网络中可靠通信的关键挑战。本文提出了一种基于图神经网络（GNN）的功率控制算法，利用统计信道状态信息（CSI）即位置信息来最小化最坏停电概率。通过将网络表示为一个完全连接的有向图，其中包含来自收发器位置的节点和边缘特征，GNN采用消息传递层来聚合干扰模式并推断出接近最佳的发射功率。仿真结果验证了该方法的可扩展性和泛化能力，验证了该方法适合大规模无线系统的实时部署。

引用次数: 0

Efficient RGBW remosaicing using local interpolation and global refinement 使用局部插值和全局细化的高效RGBW重切片

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.09.010

Sanga Park , An Gia Vien , Chul Lee

We propose an efficient RGBW remosaicing algorithm that converts RGBW images into Bayer images using learned kernel-based local interpolation and global residual learning. First, the proposed algorithm extracts local and global features from an input RGBW image. Then, we develop a learned kernel-based interpolation module to generate an intermediate Bayer image using the local features. Next, the proposed algorithm generates a residual image containing complementary information. Finally, we obtain the reconstructed Bayer image by refining the intermediate Bayer image with the residual image. Experimental results demonstrate that the proposed algorithm significantly outperforms state-of-the-art algorithms.

我们提出了一种高效的RGBW重构算法，该算法使用基于学习核的局部插值和全局残差学习将RGBW图像转换为拜耳图像。首先，该算法从输入的RGBW图像中提取局部和全局特征。然后，我们开发了一个基于学习核的插值模块，利用局部特征生成中间的拜耳图像。其次，该算法生成包含互补信息的残差图像。最后，利用残差图像对中间的拜耳图像进行细化，得到重构的拜耳图像。实验结果表明，该算法明显优于现有算法。

引用次数: 0

Imbalanced classification with label noise: A systematic review and comparative analysis 带有标签噪声的不平衡分类：系统回顾与比较分析

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-12-01 DOI: 10.1016/j.icte.2025.09.011

Faria Brishti , Fan Zhang , Sameeruddin Mohammed , Ling Bai , Fan Wu , Baiyun Chen

Class imbalance in datasets presents a significant challenge in machine learning, often causing traditional classification algorithms to exhibit bias toward majority classes while underrepresenting minority classes, which may be of crucial importance in various applications. This classification challenge is further exacerbated by the presence of label noise, which impedes the identification of optimal decision boundaries between classes and potentially leads to model overfitting. While extensive research has addressed class imbalance and label noise as separate phenomena, there remains a notable gap in the literature regarding their concurrent occurrence in datasets, specifically in the domain of imbalanced classification with label noise (ICLN). This review aims to bridge this gap by conducting an extensive analysis of existing methodologies addressing ICLN challenges. Our review encompasses approaches across diverse categories, including resampling techniques, ensemble methods, cost-sensitive learning, deep learning, active learning, meta-learning, and hybrid methodologies. Through rigorous empirical evaluation, we compare representative methods from each category using synthetic and real-world datasets, revealing a trade-off between minority class preservation, noise robustness, and computational efficiency. Our findings reveal that algorithm effectiveness is fundamentally dataset-dependent, with deep learning methods excelling on complex datasets while resampling approaches achieve competitive performance with lower computational cost. Statistical significance analysis validates our empirical observations, and we identify concrete future research directions for advancing ICLN methodologies.

数据集中的类不平衡对机器学习提出了重大挑战，通常会导致传统的分类算法对多数类表现出偏见，而对少数类的代表性不足，这在各种应用中可能至关重要。标签噪声的存在进一步加剧了这一分类挑战，它阻碍了类之间最佳决策边界的识别，并可能导致模型过拟合。虽然广泛的研究已经将类别不平衡和标签噪声作为单独的现象来解决，但关于它们在数据集中同时出现的文献仍然存在显著的差距，特别是在带有标签噪声的不平衡分类（ICLN）领域。本次审查旨在通过广泛分析应对ICLN挑战的现有方法来弥合这一差距。我们的综述涵盖了不同类别的方法，包括重新采样技术、集成方法、成本敏感学习、深度学习、主动学习、元学习和混合方法。通过严格的实证评估，我们使用合成数据集和现实世界数据集比较了每个类别的代表性方法，揭示了少数类保存、噪声鲁棒性和计算效率之间的权衡。我们的研究结果表明，算法的有效性从根本上依赖于数据集，深度学习方法在复杂数据集上表现出色，而重采样方法以更低的计算成本获得了具有竞争力的性能。统计显著性分析验证了我们的实证观察结果，并确定了推进ICLN方法的具体未来研究方向。

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引用次数: 0

Understanding deep reinforcement learning: Enhancing explainable decision-making in optical networks 理解深度强化学习：增强光网络中可解释的决策

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-10-01 DOI: 10.1016/j.icte.2025.08.002

Jorge A. Bermúdez, Patricia Morales, Hermann Pempelfort, Mauricio Araya, Nicolás Jara

Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving complex tasks in optical networks. However, its black-box nature poses challenges for interpretability. For network operators, understanding the reasoning behind decisions is crucial for effective control and resource management. This paper addresses this gap by proposing a framework that generates explanations based on DRL agents’ decision-making processes. Using imitation learning, we train four classifiers to approximate a robust DRL agent designed for elastic optical networks. Our approach enhances explainability, enabling us to better understand and manage DRL-based decisions in optical network environments.

深度强化学习（DRL）已成为解决光网络中复杂任务的一种有前途的方法。然而，它的黑箱性质对可解释性提出了挑战。对于网络运营商来说，了解决策背后的原因对于有效控制和资源管理至关重要。本文通过提出一个基于DRL代理的决策过程生成解释的框架来解决这一差距。利用模仿学习，我们训练了四个分类器来近似设计用于弹性光网络的鲁棒DRL代理。我们的方法增强了可解释性，使我们能够更好地理解和管理光网络环境中基于drl的决策。

引用次数: 0

Large-scale wireless coverage optimization: A quantum approach 大规模无线覆盖优化：量子方法

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-10-01 DOI: 10.1016/j.icte.2025.06.019

Chengkang Pan , Xin Yi , Shuai Hou , Wenqing Zhong , Deming Li , Fei Wang , Chunfeng Cui , Qinglin Pan

Wireless network coverage optimization is critical for improving service quality. However, optimizing large-scale networks remains challenging for both classical algorithms and quantum methods in the NISQ era. This paper proposes a quantum approach that models the problem as a covering graph, partitions it using a QUBO formulation, and solves subproblems via a filtered variational quantum eigensolver. The method is experimentally validated on real quantum hardware, including a coherent Ising machine and a superconducting quantum processor, and compared with classical methods like SA and PSO. This work introduces a divide-and-conquer strategy for large-scale network coverage optimization and expands the solution landscape.

无线网络覆盖优化是提高服务质量的关键。然而，在NISQ时代，优化大规模网络对于经典算法和量子方法来说仍然是一个挑战。本文提出了一种量子方法，该方法将问题建模为覆盖图，使用QUBO公式对其进行划分，并通过过滤变分量子特征求解器求解子问题。该方法在实际量子硬件上进行了实验验证，包括相干伊辛机和超导量子处理器，并与经典方法如SA和PSO进行了比较。这项工作为大规模网络覆盖优化引入了一种分而治之的策略，并扩展了解决方案的范围。

引用次数: 0

End-to-end training and adaptive transmission for OFDM-based semantic communication 基于ofdm语义通信的端到端训练与自适应传输

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-10-01 DOI: 10.1016/j.icte.2025.05.001

Jihun Park, Hyeonwoo Kim, Junyong Shin, Yongjeong Oh, Yo-Seb Jeon

This paper presents a semantic communication framework for orthogonal frequency division multiplexing (OFDM) systems. In this framework, we first introduce an end-to-end training strategy which leverages binary symmetric channels (BSCs) to model OFDM communication errors, thereby eliminating the need to specify channel distributions, modulation order, and transmission power during end-to-end training. We then propose a joint modulation order and power optimization scheme for OFDM systems designed for our end-to-end training strategy. Our optimization aims to maximize the transmission rate while satisfying target bit-error rate and power constraints. Through simulations, we demonstrate the superiority of our framework compared to existing schemes.

提出了一种用于正交频分复用（OFDM）系统的语义通信框架。在这个框架中，我们首先引入了一种端到端训练策略，该策略利用二进制对称信道（BSCs）来模拟OFDM通信错误，从而消除了在端到端训练期间指定信道分布、调制顺序和传输功率的需要。然后，我们提出了一种针对我们的端到端训练策略而设计的OFDM系统的联合调制顺序和功率优化方案。我们的优化目标是在满足目标误码率和功率限制的情况下最大限度地提高传输速率。通过仿真，我们证明了该框架与现有方案相比的优越性。

引用次数: 0

Graph neural network-based multi-metric performance modeling in urban multi-RAT wireless networks 基于图神经网络的城市多rat无线网络多度量性能建模

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-10-01 DOI: 10.1016/j.icte.2025.07.004

Jun-Hwan Huh , Toshiki Inagaki , Jin Nakazato , Maki Arai , Kazuto Yano , Mikio Hasegawa

As urban networks integrate heterogeneous radio access technologies (RATs), such as Wi-Fi and 5G/B5G, modeling performance becomes challenging due to interference, spatial variability, and propagation conditions. This paper proposes a graph neural network (GNN)-based framework for predicting throughput, delay, and jitter in multi-RAT environments, considering RAT type. The model encodes network topology and channel characteristics using node and edge features, capturing spatial configuration, congestion, and line-of-sight (LoS) versus non-line-of-sight (NLoS) conditions. The results show that GNNs exhibit robustness across station densities and spatial conditions. The message-passing GNN method performs well for throughput and delay, while non-graph methods better estimate jitter.

随着城市网络集成了异构无线接入技术（rat），如Wi-Fi和5G/B5G，由于干扰、空间可变性和传播条件，建模性能变得具有挑战性。本文提出了一种基于图神经网络（GNN）的框架，用于在考虑RAT类型的情况下预测多RAT环境下的吞吐量、延迟和抖动。该模型使用节点和边缘特征对网络拓扑和信道特征进行编码，捕获空间配置、拥塞以及视线（LoS）与非视线（NLoS）条件。结果表明，GNNs具有跨站点密度和空间条件的鲁棒性。消息传递GNN方法在吞吐量和延迟方面表现良好，而非图方法在估计抖动方面表现较好。

引用次数: 0

A survey on digital twin-assisted intelligent vehicle localization 数字双辅助智能汽车定位研究进展

IF 4.2 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express

Pub Date : 2025-10-01 DOI: 10.1016/j.icte.2025.08.008

Md Abdul Latif Sarker , Md Omar Faruque Sarker , Dong Seog Han

Localization modules play an important role in ensuring the secure operation of intelligent vehicles (IV) and accelerating the development of driving technologies. To safely drive an intelligent vehicle, an exact data registration is necessary. Map drift, scan-to-map alignment, high computational load, sensor noise, and calibration are the most prominent issues that cause poor localization performance for intelligent vehicles. Therefore, by presenting a digital twin-assisted data registration (DT-ADR) technique, this research attempts to mitigate that gap. The purpose of this study is to show how to implement the DT-ADR technique to help improve the existing light detection and ranging (LiDAR) data registration technique for more accurate vehicle localization and driving capabilities. We first investigate traditional data registration techniques that address the challenges for scan matching localization. We then present the proposed DT-ADR technique and discuss a pose selection technique. Next, we demonstrate an implementation of the proposed DT-ADR technique using AWSIM cosimulation, Autoware universe, and ROS2 virtual environments. To verify the effectiveness of the description of the localization approach, a case study is also conducted to analyze the pose estimation of IV. Lastly, an initial result is evaluated for the proposed DT-ADR technique, which reduces the orientation root mean squared error by an average

41 %

compared to the existing LiDAR data registration technique based on normal distribution transforms. This work could be utilized as a testbed for future research that attempts to include advanced localization features for IV driving.

定位模块在保障智能汽车安全运行、加速驾驶技术发展方面发挥着重要作用。为了安全驾驶智能汽车，精确的数据登记是必要的。地图漂移、扫描到地图对齐、高计算负荷、传感器噪声和校准是导致智能汽车定位性能不佳的最突出问题。因此，通过提出数字双辅助数据注册（DT-ADR）技术，本研究试图缓解这一差距。本研究的目的是展示如何实现DT-ADR技术，以帮助改进现有的光探测和测距（LiDAR）数据注册技术，以实现更准确的车辆定位和驾驶能力。我们首先研究了解决扫描匹配定位挑战的传统数据配准技术。然后，我们提出了DT-ADR技术，并讨论了一种姿态选择技术。接下来，我们将使用AWSIM联合仿真、Autoware宇宙和ROS2虚拟环境演示所提出的DT-ADR技术的实现。为了验证定位方法描述的有效性，还进行了一个案例研究，分析了IV的姿态估计。最后，对所提出的DT-ADR技术进行了初步评估，与基于正态分布变换的现有激光雷达数据配准技术相比，该技术将方向均方根误差平均降低了41%。这项工作可以用作未来研究的测试平台，以尝试包括IV驾驶的高级定位功能。

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