Annals of Telecommunications最新文献_第2页

Crosstalk-aware circuit reallocation to reduce blocking in spatial division multiplexed elastic optical networks 空域复用弹性光网络中串扰感知电路重分配减少阻塞

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-04-22 DOI: 10.1007/s12243-025-01092-2

Selles G. F. C. Araújo, André C. B. Soares

引用次数: 0

A two-stage Q-learning routing approach for quantum entanglement networks 量子纠缠网络的两阶段q学习路由方法

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-04-22 DOI: 10.1007/s12243-025-01090-4

Diego Abreu, Arthur Pimentel, David Moura, Christian Rothenberg, Antônio Abelém

{"title":"A two-stage Q-learning routing approach for quantum entanglement networks","authors":"Diego Abreu, Arthur Pimentel, David Moura, Christian Rothenberg, Antônio Abelém","doi":"10.1007/s12243-025-01090-4","DOIUrl":"10.1007/s12243-025-01090-4","url":null,"abstract":"<div><p>The emerging field of quantum internet offers multiple applications, enabling quantum communication across diverse networks. However, the current entanglement networks exhibit complex processes, characterized by variable entanglement generation rates, limited quantum memory capacity, and susceptibility to decoherence rates. Addressing these issues, we propose a two-stage routing system that harnesses the power of reinforcement learning (RL). The first stage focuses on identifying the most efficient routes for quantum data transmission. The second stage concentrates on establishing these routes and improving how and when to apply entanglement swapping and purification. Our extensive evaluations across various network sizes and configurations reveal that our method not only sustains superior end-to-end route fidelity but also achieves significantly higher request success rates compared to traditional methods. These findings highlight the efficacy of our approach in managing the complex dynamics of quantum networks, ensuring robust and scalable quantum communication. Our method’s adaptability to changing network conditions and its proactive management of quantum resources make an important contribution to quantum network efficiency.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 9-10","pages":"807 - 822"},"PeriodicalIF":2.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Power allocation and communication resource scheduling for federated learning in wireless IoT networks 无线物联网网络联合学习的功率分配和通信资源调度

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-04-21 DOI: 10.1007/s12243-025-01089-x

Renan R. de Oliveira, Rogério S. e Silva, Leandro A. Freitas, Antonio Oliveira Jr

{"title":"Power allocation and communication resource scheduling for federated learning in wireless IoT networks","authors":"Renan R. de Oliveira, Rogério S. e Silva, Leandro A. Freitas, Antonio Oliveira Jr","doi":"10.1007/s12243-025-01089-x","DOIUrl":"10.1007/s12243-025-01089-x","url":null,"abstract":"<div><p>Federated learning (FL) allows devices to train a machine learning model collaboratively without compromising data privacy. In wireless networks, FL presents challenges due to limited resources and the unstable nature of transmission channels that can cause delays and errors that compromise the consistency of global model updates. Furthermore, efficient allocation of communication resources is crucial in Internet of Things (IoT) environments, where devices often have limited energy capacity. This work introduces a novel FL algorithm called DFed-w<span>(_{text {Opt}}^{text {DP}})</span>, designed for wireless networks within the IoT framework. This algorithm incorporates a device selection mechanism that evaluates the quality of device data distribution and connection quality with the aggregate server. By optimizing the power allocation for each device, DFed-w<span>(_{text {Opt}}^{text {DP}})</span> minimizes overall energy consumption while enhancing the success rate of transmissions. The simulation results demonstrate that DFed-w<span>(_{text {Opt}}^{text {DP}})</span> effectively operates with low transmission power while preserving the accuracy of the global model compared to other algorithms.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 9-10","pages":"915 - 928"},"PeriodicalIF":2.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new fragmentation- and physical layer impairments-aware algorithm to core and spectrum assignment in spatial division multiplexing elastic optical networks 一种新的碎片和物理层损伤感知的空域复用弹性光网络芯和频谱分配算法

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-04-16 DOI: 10.1007/s12243-025-01091-3

Jurandir C. Lacerda Jr., Aline G. Morais, Adolfo V. T. Cartaxo, André C. B. Soares

{"title":"A new fragmentation- and physical layer impairments-aware algorithm to core and spectrum assignment in spatial division multiplexing elastic optical networks","authors":"Jurandir C. Lacerda Jr., Aline G. Morais, Adolfo V. T. Cartaxo, André C. B. Soares","doi":"10.1007/s12243-025-01091-3","DOIUrl":"10.1007/s12243-025-01091-3","url":null,"abstract":"<div><p>Spatial division multiplexing elastic optical networks (SDM-EONs) based on multicore fibers (MCFs) are a technology that can handle the Internet’s growing traffic demand. However, SDM-EONs present challenges in their implementation, such as the physical layer impairments (PLI) and the spectrum fragmentation. This paper proposes the fragmentation-aware and PLI-aware algorithm (FXAA) to solve the core and spectrum assignment problem in MCF-based SDM-EONs. The FXAA implements a low-cost PLI-aware mechanism to select lightpaths with low inter- and intra-core impairment incidence, ensuring the quality of transmission (QoT) of the network lightpaths. In addition, FXAA clusters the lightpaths with the same number of frequency slots to reduce spectrum fragmentation. The numerical results show that compared with the other nine algorithms proposed in the literature, FXAA achieves a gain of circuit blocking probability of at least 33.36%, a gain of bandwidth blocking probability of at least 17.99%, and an increase in spectral utilization of at least 1.08%.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 9-10","pages":"715 - 727"},"PeriodicalIF":2.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing resource allocation in 5G-V2X communication: adaptive strategies for enhanced QoS in intelligent transportation systems 优化5G-V2X通信中的资源分配：智能交通系统中增强QoS的自适应策略

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-04-02 DOI: 10.1007/s12243-025-01086-0

Oummany Youssef, Elassali Raja, Elbahhar Boukour Fouzia

{"title":"Optimizing resource allocation in 5G-V2X communication: adaptive strategies for enhanced QoS in intelligent transportation systems","authors":"Oummany Youssef, Elassali Raja, Elbahhar Boukour Fouzia","doi":"10.1007/s12243-025-01086-0","DOIUrl":"10.1007/s12243-025-01086-0","url":null,"abstract":"<div><p>5G vehicle-to-everything (V2X) connectivity plays a fundamental role in enabling advanced vehicular networks within intelligent transportation systems (ITS). However, challenges arising from limited resources, such as unreliable connections between vehicles and the substantial signaling overhead in centralized resource distribution methods, impede the efficiency of V2X communication systems, especially in safety-critical applications. This study critically explores the limitations of centralized resource management in 5G-V2X, focusing on issues of resource scarcity and allocation inefficiencies. In response to these challenges, our approach focuses on optimizing resource utilization within the constraints of limited resources. The article introduces innovative strategies to enhance V2X service satisfaction, emphasizing the efficient allocation of resources for different service classes. Simulations showcase the impact of our tailored approach on resource utilization and satisfaction rates, shedding light on potential improvements in scenarios with constrained resources.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 and networking","pages":"489 - 500"},"PeriodicalIF":2.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep neural network-driven precision agriculture multi-path multi-hop noisy plant image data transmission and plant disease detection 深度神经网络驱动的精准农业多路径多跳噪声植物图像数据传输与植物病害检测

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-04-01 DOI: 10.1007/s12243-025-01087-z

Derek K. P. Asiedu, Kwabena E. Bennin, Dennis A. N. Gookyi, Mustapha Benjillali, Samir Saoudi

{"title":"Deep neural network-driven precision agriculture multi-path multi-hop noisy plant image data transmission and plant disease detection","authors":"Derek K. P. Asiedu, Kwabena E. Bennin, Dennis A. N. Gookyi, Mustapha Benjillali, Samir Saoudi","doi":"10.1007/s12243-025-01087-z","DOIUrl":"10.1007/s12243-025-01087-z","url":null,"abstract":"<div><p>Precision agriculture (PA) and plant disease detection (PDD) are essential for farm crops’ life quality and crop yield. Unfortunately, current PDD algorithms are trained and deployed with perfect plant images. This is impractical since PA sensor networks (PANs) transfer imperfect data due to wireless communication imperfections, such as channel estimation and noise, as well as hardware imperfections and noise. To capture the influence of channel imperfections and combat its effect, this work considers on- and/or offsite PDD implementation using plant image data transferred over multi-path imperfect PAN. Here, both traditional decode-and-forward (DF) data routing and channel effect considering machine learning data autoencoder multi-path routing are used for image data transmission. The multi-path DF data routing considers equal gain combining (EGC) and maximum ratio combining (MRC) techniques at the destination gateway for data decoding. In addition, a PDD deep learning algorithm is developed to predict whether or not a farm plant is diseased, using the noisy image data captured by the multi-path data routing PAN. From the PAN-PDD integrated system simulation, the proposed ML multi-path PAN-PDD algorithms (i.e., EGC and MRC) are compared to the ML single-path PAN-PDD algorithm and the traditional single-path PAN-PDD system. The simulation results showed that the multi-path approach performed fairly well over the other DF PAN-PDD systems. Incorporating the channel effects in designing an intelligent wireless data transfer solution/technique improves the communication system performance in PDD implementation.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 and networking","pages":"445 - 457"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient bimodal emotion recognition system based on speech/text embeddings and ensemble learning fusion 基于语音/文本嵌入和集成学习融合的高效双峰情感识别系统

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-04-01 DOI: 10.1007/s12243-025-01088-y

Adil Chakhtouna, Sara Sekkate, Abdellah Adib

{"title":"Efficient bimodal emotion recognition system based on speech/text embeddings and ensemble learning fusion","authors":"Adil Chakhtouna, Sara Sekkate, Abdellah Adib","doi":"10.1007/s12243-025-01088-y","DOIUrl":"10.1007/s12243-025-01088-y","url":null,"abstract":"<div><p>Emotion recognition (ER) is a pivotal discipline in the field of contemporary human–machine interaction. Its primary objective is to explore and advance theories, systems, and methodologies that can effectively recognize, comprehend, and interpret human emotions. This research investigates both unimodal and bimodal strategies for ER using advanced feature embeddings for audio and text data. We leverage pretrained models such as ImageBind for speech and RoBERTa, alongside traditional TF-IDF embeddings for text, to achieve accurate recognition of emotional states. A variety of machine learning (ML) and deep learning (DL) algorithms were implemented to evaluate their performance in speaker dependent (SD) and speaker independent (SI) scenarios. Additionally, three feature fusion methods, early fusion, majority voting fusion, and stacking ensemble fusion, were employed for the bimodal emotion recognition (BER) task. Extensive numerical simulations were conducted to systematically address the complexities and challenges associated with both unimodal and bimodal ER. Our most remarkable findings demonstrate an accuracy of <span>(86.75%)</span> in the SD scenario and <span>(64.04%)</span> in the SI scenario on the IEMOCAP database for the proposed BER system.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 and networking","pages":"379 - 399"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid deep learning model for multi-class DDoS detection in SDN networks SDN网络中多类DDoS检测的混合深度学习模型

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-03-29 DOI: 10.1007/s12243-025-01085-1

Ameur Salem Zaidoun, Zied Lachiri

{"title":"A hybrid deep learning model for multi-class DDoS detection in SDN networks","authors":"Ameur Salem Zaidoun, Zied Lachiri","doi":"10.1007/s12243-025-01085-1","DOIUrl":"10.1007/s12243-025-01085-1","url":null,"abstract":"<div><p>This paper, as an extended version of a communication presented at the ISIVC’2024 conference, deals with security issues in the software-defined networks (SDN); it introduces a Distributed Denial of Service (DDoS) detection system leveraging deep learning (DL) features. The main objective is to enhance SDN security by accurately classifying DDoS attacks, improving efficiency, particularly for zero-day attack detection, and enabling targeted mitigation strategies. Our contribution focuses on refining a hybrid DL model with a novel architecture that applies algorithms simultaneously to distinguish the normal SDN traffic and five carefully selected other classes covering various attack kinds, using an optimized CIC-DDoS2019 dataset for more efficient classification. Compared to the conference paper, the model has been reinforced by the use of attention mechanisms and transformer architectures in addition to layers’ adjustments and hyper-parameters re-settings. Additionally, the previously used training and testing data have been combined and split into three sets: 70% for training, 15% for validation (continuous partial evaluation), and 15% for final testing. The resulting solution (hybrid DNN-LSTM) demonstrated continuous exponential improvement of validation accuracy during the training step, recording a higher value near 99% and achieving a final testing accuracy of 98.84%. The improved model is suitable for real-world SDN systems, with its deployment, potential challenges, and practical benefits discussed.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":"80 and networking","pages":"459 - 472"},"PeriodicalIF":2.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145170887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and fabrication of a novel frequency-reconfigurable patch antenna for WiFi and 5 G applications 设计和制造一种用于WiFi和5g应用的新型频率可重构贴片天线

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-03-25 DOI: 10.1007/s12243-025-01080-6

Nouhayla El Anzoul, Younes Karfa Bekali, Khalid Minaoui, Mohammed Lahsaini, Ilyass Saouidi

引用次数: 0

Enhanced deep learning approach for high-accuracy mobility coordinate prediction 基于深度学习的高精度移动坐标预测方法

IF 2.2 4区计算机科学

Annals of Telecommunications Pub Date : 2025-03-22 DOI: 10.1007/s12243-025-01081-5

Siham Sadiki, Hanae Belmajdoub, Nisrine Ibadah, Khalid Minaoui

引用次数: 0