ICT Express最新文献

英文中文

Data-driven integrated sensing and communication: Recent advances, challenges, and future prospects 数据驱动的集成传感和通信：最新进展、挑战和未来展望

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-06-26 DOI: 10.1016/j.icte.2025.06.010

Hammam Salem , Haleema Sadia , MD Muzakkir Quamar , Adeb Magad , Mohammed Elrashidy , Nasir Saeed , Mudassir Masood

The integration of integrated sensing and communication (ISAC) with artificial intelligence (AI)-driven techniques has emerged as a transformative research frontier, attracting significant interest from both academia and industry. As sixth-generation (6G) networks advance to support ultra-reliable, low-latency, and high-capacity applications, machine learning (ML) has become a critical enabler for optimizing ISAC functionalities. Recent advancements in deep learning (DL) and deep reinforcement learning (DRL) have demonstrated immense potential in enhancing ISAC-based systems across diverse domains, including intelligent vehicular networks, autonomous mobility, unmanned aerial vehicles based communications, radar sensing, localization, millimeter wave/terahertz communication, and adaptive beamforming. However, despite these advancements, several challenges persist, such as real-time decision-making under resource constraints, robustness in adversarial environments, and scalability for large-scale deployments. This paper provides a comprehensive review of ML-driven ISAC methodologies, analyzing their impact on system design, computational efficiency, and real-world implementations, while also discussing existing challenges and future research directions to explore how AI can further enhance ISAC’s adaptability, resilience, and performance in next-generation wireless networks. By bridging theoretical advancements with practical implementations, this paper serves as a foundational reference for researchers, engineers, and industry stakeholders, aiming to leverage AI’s full potential in shaping the future of intelligent ISAC systems within the 6G ecosystem.

集成传感与通信（ISAC）与人工智能（AI）驱动技术的集成已经成为一个变革性的研究前沿，吸引了学术界和工业界的极大兴趣。随着第六代（6G）网络的发展，以支持超可靠、低延迟和高容量应用，机器学习（ML）已成为优化ISAC功能的关键推动者。深度学习（DL）和深度强化学习（DRL）的最新进展显示了在不同领域增强基于isac的系统的巨大潜力，包括智能车辆网络、自主移动、基于无人机的通信、雷达传感、定位、毫米波/太赫兹通信和自适应波束形成。然而，尽管取得了这些进步，仍然存在一些挑战，例如资源约束下的实时决策、对抗环境中的鲁棒性以及大规模部署的可扩展性。本文全面回顾了机器学习驱动的ISAC方法，分析了它们对系统设计、计算效率和现实世界实现的影响，同时还讨论了现有的挑战和未来的研究方向，以探索人工智能如何进一步增强ISAC在下一代无线网络中的适应性、弹性和性能。通过将理论进步与实际实施相结合，本文为研究人员、工程师和行业利益相关者提供了基础参考，旨在充分利用人工智能的潜力，在6G生态系统中塑造智能ISAC系统的未来。

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引用次数: 0

MSS-TCP: A congestion control algorithm for boosting TCP performance in mmwave cellular networks MSS-TCP：一种在毫米波蜂窝网络中提高TCP性能的拥塞控制算法

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-05-26 DOI: 10.1016/j.icte.2025.05.005

Omar Imhemed Alramli , Zurina Mohd Hanapi , Mohamed Othman , Normalia Samian , Idawaty Ahmad

The increasing demand for high-speed, low-latency applications, especially with 5G mmWave technology, has led to challenges in TCP performance due to signal blockages, small buffers, and high Packet Error Rates (PERs). Existing congestion control algorithms (CCAs) struggle to fully utilize available bandwidth under these conditions. This paper proposes MSS-TCP, a novel congestion control algorithm designed for mmWave networks. MSS-TCP dynamically adjusts the congestion window (cwnd) based on the maximum segment size (MSS) and round-trip time (RTT), improving bandwidth utilization and congestion adaptability. The simulation results using the ns-3 network simulator show that MSS-TCP outperforms state-of-the-art CCAs, including NewReno, HighSpeed, CUBIC, and Bottleneck Bandwidth and Round-trip propagation time (BBR), and Fuzzy Logic-based (FB-TCP), particularly when the buffer matches the bandwidth-delay product (BDP), achieving a 24.26% to 45.43% improvement in throughput compared to BBR while maintaining low latency. These findings demonstrate that MSS-TCP enhances TCP performance in 5G mmWave networks, making it a promising solution for next-generation wireless communication.

对高速、低延迟应用的需求不断增长，特别是5G毫米波技术，由于信号阻塞、小缓冲区和高分组错误率（per），导致TCP性能面临挑战。在这种情况下，现有的拥塞控制算法（cca）难以充分利用可用带宽。本文提出了一种针对毫米波网络设计的新型拥塞控制算法MSS-TCP。MSS- tcp根据最大段大小（MSS）和往返时间（RTT）动态调整拥塞窗口（cwnd），提高带宽利用率和拥塞适应性。使用ns-3网络模拟器的仿真结果表明，MSS-TCP优于最先进的cca，包括NewReno， HighSpeed， CUBIC，瓶颈带宽和往返传播时间（BBR）以及基于模糊逻辑的（FB-TCP），特别是当缓冲区匹配带宽延迟产品（BDP）时，与BBR相比，吞吐量提高了24.26%至45.43%，同时保持了低延迟。这些发现表明，MSS-TCP增强了5G毫米波网络中的TCP性能，使其成为下一代无线通信的有前途的解决方案。

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引用次数: 0

Federated learning and TinyML on IoT edge devices: Challenges, advances, and future directions IoT边缘设备上的联合学习和TinyML：挑战、进展和未来方向

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-06-19 DOI: 10.1016/j.icte.2025.06.008

Montaser N.A. Ramadan , Mohammed A.H. Ali , Shin Yee Khoo , Mohammad Alkhedher

This paper examines the integration of Federated Learning (FL), TinyML, and IoT in resource-constrained edge devices, highlighting key challenges and opportunities. It reviews FL and TinyML frameworks with a focus on communication, privacy, accuracy, efficiency, and memory constraints. We propose a novel FL-IoT framework that combines over-the-air (OTA) AI model updates, LoRa-based distributed communication, and lossless data compression techniques such as Run-Length Encoding (RLE), Huffman coding, and LZW to reduce transmission cost, optimize local processing, and maintain data privacy. The framework features Raspberry Pi-based aggregation nodes and microcontroller-based IoT clients, enabling scalable, low-power learning across heterogeneous devices. Evaluation includes memory usage, communication cost, energy consumption, and accuracy trade-offs across multiple FL scenarios. Results show improved scalability and significant power savings compared to baseline FL setups. The proposed framework is particularly impactful in applications such as smart agriculture, healthcare, and smart cities. Future directions for real-time, privacy-preserving edge intelligence are discussed.

本文研究了联邦学习（FL）、TinyML和物联网在资源受限边缘设备中的集成，突出了关键挑战和机遇。它回顾了FL和TinyML框架，重点关注通信、隐私、准确性、效率和内存约束。我们提出了一种新的FL-IoT框架，该框架结合了空中（OTA） AI模型更新，基于lora的分布式通信以及无损数据压缩技术，如运行长度编码（RLE），霍夫曼编码和LZW，以降低传输成本，优化本地处理并维护数据隐私。该框架的特点是基于Raspberry pi的聚合节点和基于微控制器的物联网客户端，支持跨异构设备的可扩展、低功耗学习。评估包括内存使用、通信成本、能耗和跨多个FL场景的准确性权衡。结果显示，与基线FL设置相比，可伸缩性得到了改进，并且显著节省了功耗。提出的框架在智能农业、医疗保健和智能城市等应用中特别有影响力。讨论了实时、隐私保护边缘智能的未来发展方向。

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引用次数: 0

EntUn: Mitigating the forget-retain dilemma in unlearning via entropy EntUn：通过熵来缓解遗忘-保留困境

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-06-23 DOI: 10.1016/j.icte.2025.06.007

Dahuin Jung

Advancements in natural language processing and computer vision have raised concerns about models inadvertently exposing private data and confidently misclassifying inputs. Machine unlearning has emerged as a solution, enabling the removal of specific data influences to meet privacy standards. This work focuses on unlearning in Instance-Removal (IR) and Class-Removal (CR) scenarios: IR targets the removal of individual data points, while CR eliminates all data related to a specific class. We propose EntUn, which maximizes entropy for the forget-set to reduce confidence in data to be forgotten and minimizes it for the retain-set to preserve discriminative power. An entropy-based intra-class mixup further stabilizes this process, using higher-entropy samples to guide controlled information removal. Experiments on CIFAR10, CIFAR100, and TinyImageNet show that EntUn outperforms state-of-the-art baselines, improving forgetting and enhancing privacy protection as confirmed by membership inference attack tests. This demonstrates entropy maximization as a robust strategy for effective unlearning.

自然语言处理和计算机视觉的进步引起了人们对模型无意中暴露私人数据和自信地错误分类输入的担忧。机器学习已经成为一种解决方案，可以消除特定的数据影响，以满足隐私标准。这项工作的重点是实例删除（IR）和类删除（CR）场景中的学习：IR的目标是删除单个数据点，而CR则消除与特定类相关的所有数据。我们提出了EntUn，它使遗忘集的熵最大化以降低对被遗忘数据的置信度，使保留集的熵最小化以保持判别能力。基于熵的类内混合进一步稳定了这一过程，使用更高熵的样本来指导受控的信息删除。在CIFAR10、CIFAR100和TinyImageNet上的实验表明，EntUn优于最先进的基线，改善了遗忘，增强了隐私保护，这一点得到了成员推理攻击测试的证实。这表明熵最大化是一种有效的遗忘策略。

引用次数: 0

Optical wireless communications for next-generation radio access networks 用于下一代无线接入网的光无线通信

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-05-22 DOI: 10.1016/j.icte.2025.04.016

Abdul Wadud , Anas Basalamah

High-speed and high-bandwidth capabilities provided by free space optical wireless communication (FSO-WC) improve communication technologies with better channel security. With its high carrier frequency, wide bandwidth, and use of unlicensed spectrum, FSO has been identified by researchers looking into innovations in next-generation wireless communications as a promising way to deliver ultrafast data links to meet the growing demands for massive connectivity and high data rates in a variety of 6G applications, such as cellular wireless backhauls and heterogeneous networks. However, issues like atmospheric turbulence, absorption, and scattering have a major impact on the system’s performance by raising the bit error rate (BER) and symbol error rate (SER). In order to tackle these problems, this paper looks at Deep Neural Network (DNN) models, particularly Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN). We experiment DNN-based equalizer in context of Open Radio Access Network (O-RAN), which aims to minimize SER and BER. According to the investigation, CNNs use more processing resources than MLPs, although offering superior error reduction. Our investigation shows that FSO can be adopted in high data rate front haul between the distributed units (DUs) and radio units (RUs).

自由空间光无线通信（FSO-WC）提供的高速和高带宽能力改进了通信技术，提高了信道安全性。凭借其高载波频率、宽带宽和免授权频谱的使用，FSO已被研究下一代无线通信创新的研究人员确定为提供超高速数据链路的有前途的方式，以满足各种6G应用（如蜂窝无线回程和异构网络）中对大规模连接和高数据速率日益增长的需求。然而，大气湍流、吸收和散射等问题会提高系统的误码率（BER）和符号误码率（SER），对系统的性能产生重大影响。为了解决这些问题，本文着眼于深度神经网络（DNN）模型，特别是多层感知器（MLP）和卷积神经网络（CNN）。我们在开放无线接入网（O-RAN）的背景下实验了基于dnn的均衡器，其目的是最小化SER和BER。根据调查，cnn比mlp使用更多的处理资源，尽管提供了更好的减少错误。我们的研究表明，在分布式单元（du）和无线单元（ru）之间的高数据速率前端传输中可以采用FSO。

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引用次数: 0

An Efficient Resource Allocation Mechanism with Fuzzy C-Means and Adaptive RNNs for D2D Communications in Cellular Networks 基于模糊c均值和自适应rnn的蜂窝网络D2D通信有效资源分配机制

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-05-27 DOI: 10.1016/j.icte.2025.05.003

Sambi Reddy Gottam, Udit Narayana Kar

Direct communication links between nearby users can be established via device-to-device (D2D) communications, eliminating the need for a base station (BS) or remaining core networks. The D2D users’ transmission power is lower than the BS’s traffic burden. Nonorthogonal multiple access (NOMA) expertise allows a transmitter to direct multiple impulses at the same wavelength by power superposition, possibly enhancing spectrum efficiency. In this work, an adaptive recurrent neural network (ARNN) is developed to effectively handle the nonlinearity of transmission powers and channel diversity. Furthermore, a method called fuzzy C-means clustering (FCMC) is designed to group users on different subcarriers with different strengths. For spectrum utilization to improve, clustering is necessary. The advanced coati optimization algorithm (ACOA) is subsequently utilized to assign assets. The Levy Flight (LF) function is taken into consideration when choosing the weight value in the Coati Optimization Algorithm (COA). The simulation findings demonstrate that our method is better at increasing system throughput while meeting users’ file requests. This method enables the efficient use of resources and power control in interactions between devices. The proposed method is implemented in MATLAB, and its performance is evaluated via performance measures. It is compared with conventional approaches. The results indicate that the suggested method achieves superior outage probability values across different user counts, with values of 0.99465 for 40 users, 0.99946 for 60 users, 0.99946 for 80 users, and 0.999446 for 100 users. Comparatively, the Recurrent Neural Network-Honey Badger Algorithm (RNN-HBA) achieved slightly lower outage probabilities, whereas the Deep Belief Network (DBN) and Particle Swarm Optimization (PSO) demonstrated more significant variations, especially with a greater number of users.

附近用户之间可以通过设备对设备（D2D）通信建立直接通信链路，从而消除了对基站（BS）或剩余核心网络的需求。D2D用户的传输功率低于BS的业务负担。非正交多址（NOMA）技术允许发射机通过功率叠加引导相同波长的多个脉冲，可能提高频谱效率。为了有效地处理传输功率和信道分集的非线性，本文提出了一种自适应递归神经网络（ARNN）。在此基础上，设计了一种模糊c均值聚类（FCMC）方法，对不同子载波上不同强度的用户进行分组。为了提高频谱利用率，聚类是必要的。随后利用先进的coati优化算法（ACOA）进行资产分配。Coati优化算法（COA）在选择权重值时考虑了Levy Flight （LF）函数。仿真结果表明，该方法在满足用户文件请求的同时，能更好地提高系统吞吐量。这种方法可以在设备之间的交互中实现资源的有效利用和功率控制。在MATLAB中实现了该方法，并通过性能指标对其性能进行了评价。并与传统方法进行了比较。结果表明，所建议的方法在不同用户数量下获得了更好的停机概率值，40个用户的停机概率值为0.99465,60个用户的停机概率值为0.99946,80个用户的停机概率值为0.99946,100个用户的停机概率值为0.999446。相比之下，递归神经网络-蜜獾算法（RNN-HBA）的中断概率略低，而深度信念网络（DBN）和粒子群优化（PSO）表现出更显著的变化，特别是在用户数量较大的情况下。

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引用次数: 0

Explainable AI based feature selection in cancer RNA-seq 癌症RNA-seq中可解释的基于AI的特征选择

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-05-09 DOI: 10.1016/j.icte.2025.05.004

Hyein Seo , Jae-Ho Park , Jangho Lee , Byung Chang Chung

Identifying informative features in bioinformatics is challenging due to their small proportion within large datasets. We propose a scalable and interpretable feature selection framework for cancer RNA-seq by transforming non-image bio-data into 2D formats and applying convolutional neural networks (CNNs) with transfer learning for efficient classification. Explainable artificial intelligence (XAI) techniques identify and prioritize important features, while principal component analysis (PCA) determines the optimal number of selected features, ensuring transparency and reliability. Comparative analysis of CNN and XAI highlights the effectiveness of our approach, providing a robust framework for high-dimensional genomic data analysis with applications in cancer diagnosis and prognosis.

识别生物信息学中的信息特征是具有挑战性的，因为它们在大数据集中的比例很小。我们提出了一个可扩展和可解释的癌症RNA-seq特征选择框架，通过将非图像生物数据转换为2D格式，并应用卷积神经网络（cnn）和迁移学习进行有效分类。可解释的人工智能（XAI）技术识别并优先考虑重要特征，而主成分分析（PCA）确定所选特征的最佳数量，确保透明度和可靠性。CNN和XAI的对比分析突出了我们方法的有效性，为高维基因组数据分析提供了一个强大的框架，可用于癌症诊断和预后。

引用次数: 0

Multi-objective task offloading optimization using deep reinforcement learning with resource distribution clustering 基于资源分布聚类的深度强化学习多目标任务卸载优化

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-05-18 DOI: 10.1016/j.icte.2025.05.006

Qin Yang, Sang-Jo Yoo

Task offloading in multi-access edge computing (MEC) systems is critical for managing computational tasks in dynamic urban environments. Existing strategies face challenges such as high communication overheads and regional performance deviations including centralized and distributed methods. Clustering approaches have been explored to address these issues, yet they often rely on physical proximity to form clusters, overlooking the variability in task rate distributions across edges. To overcome these limitations, this paper proposes a graph-driven inter-cluster resource distribution (GIRD) clustering scheme that clusters edge nodes based on task request distribution and computing resource status, ensuring similar resource utilization across clusters. Building on this, a proximal policy optimization (PPO)-enabled intra-cluster task offloading algorithm (PITO) is introduced to determine one execution server for task offloading—either an edge server within a cluster or a cloud server—using various network state information. This dynamic decision-making process optimizes a multi-objective function that includes task processing delay, consumed energy, success rate, and cloud cost. Simulation results demonstrate the proposed GIRD-PITO framework achieves superior task success rates, reduced delays, and improved regional performance fairness, making it a promising solution for large-scale MEC systems. 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/).

多访问边缘计算（MEC）系统中的任务卸载对于管理动态城市环境中的计算任务至关重要。现有战略面临着诸如高通信开销和区域性能偏差（包括集中式和分布式方法）等挑战。已经探索了聚类方法来解决这些问题，但是它们通常依赖于物理接近来形成聚类，忽略了任务率分布在边缘上的可变性。为了克服这些限制，本文提出了一种基于任务请求分布和计算资源状态对边缘节点进行聚类的图驱动集群间资源分布（GIRD）聚类方案，以确保集群间的资源利用率相似。在此基础上，引入了支持近端策略优化（PPO）的集群内任务卸载算法（PITO），以使用各种网络状态信息确定一个任务卸载的执行服务器—集群中的边缘服务器或云服务器。这种动态决策过程优化了一个多目标函数，包括任务处理延迟、消耗的能量、成功率和云成本。仿真结果表明，提出的ggrid - pito框架具有更高的任务成功率、更低的延迟和更高的区域性能公平性，是一种很有前景的大规模MEC系统解决方案。2018年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

DeLECA: Deblurring for Long and short Exposure images with a dual-branch multimodal cross attention mechanism DeLECA：用双分支多模态交叉注意机制消除长曝光和短曝光图像的模糊

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-06-21 DOI: 10.1016/j.icte.2025.06.003

Keunho Byeon, Jeewoo Lim, Jin Tae Kwak

Blurred images often result from camera shake or object motion, complicating the visual inspection and recognition of objects. To address this issue, we propose DeLECA, a dual-branch Transformer architecture that leverages the complementary nature of the paired blurred images, obtained with long-exposure times, and noisy images, captured with short exposure times, to improve the quality and sharpness of the blurred images. We evaluate DeLECA using two public datasets, GoPro and HIDE. Experimental results show that DeLECA outperforms existing methods, achieving PSNR of 36.08 dB and SSIM of 0.965 on the GoPro dataset, and 40.05 dB and 0.972 on the HIDE dataset.

模糊图像通常是由相机抖动或物体运动引起的，使视觉检查和物体识别变得复杂。为了解决这个问题，我们提出了DeLECA，这是一种双支路变压器架构，它利用长曝光时间获得的对模糊图像和短曝光时间捕获的噪声图像的互补性，以提高模糊图像的质量和清晰度。我们使用两个公共数据集GoPro和HIDE来评估DeLECA。实验结果表明，DeLECA优于现有方法，在GoPro数据集上的PSNR为36.08 dB， SSIM为0.965，在HIDE数据集上的PSNR为40.05 dB， SSIM为0.972。

引用次数: 0

Edge-enhanced decentralized vehicle authentication protocol for IoV 面向车联网的边缘增强分散式车辆认证协议

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

ICT Express

Pub Date : 2025-08-01 Epub Date: 2025-07-05 DOI: 10.1016/j.icte.2025.06.013

Nai-Wei Lo , Wen-Hsien Yu , Jheng-Jia Huang , Yu-Chi Chen

The Internet of Vehicles (IoV) requires secure and efficient authentication. This study proposes a decentralized protocol leveraging edge nodes and consortium blockchain to enhance security while reducing cloud dependency. A mathematical model evaluates performance and scalability, while simulations validate resilience against network failures, attacks, and topology changes. The protocol integrates with IoT infrastructure and considers implementation costs. Results demonstrate improved efficiency, security, and feasibility for large-scale vehicular networks.

车联网（IoV）需要安全高效的身份验证。本研究提出了一种分散的协议，利用边缘节点和联盟区块链来增强安全性，同时减少对云的依赖。数学模型评估性能和可伸缩性，而仿真验证针对网络故障、攻击和拓扑变化的弹性。该协议与物联网基础设施集成，并考虑实施成本。结果表明，该方法提高了大规模车辆网络的效率、安全性和可行性。

引用次数: 0

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