Journal of Communications and Networks最新文献

{"title":"AURORA: Adaptive spreading factor control for low-power multihop LoRa network","authors":"Eunjeong Park;Sungbo Eo;Hongchan Kim;Jeongyeup Paek;Saewoong Bahk","doi":"10.23919/JCN.2025.000084","DOIUrl":"https://doi.org/10.23919/JCN.2025.000084","url":null,"abstract":"LoRa has gained significant attention for Internet of Things (IoT) applications that require low-power and long-range communication such as animal tracking. However, LoRa’s spreading factor (SF) selection mechanism and 1-hop topology lack effective per-link adaptation to dynamics of the communication channel nor provide comprehensive coverage of moving animals. To address this problem, we present AURORA, an adaptive and distributed SF control scheme for low-power multihop LoRa networks. AURORA exploits the key tradeoff of SF—receiver sensitivity vs. data rate—with a focus on energy efficiency. AURORA accurately predicts the packet delivery ratio (PDR) for each SF using efficient probing and model fitting techniques, enabling rapid per-link SF adaptation to improve both PDR and duty cycle in multihop networks. Through real-world experiments conducted on both indoor and outdoor testbeds, we demonstrate that AURORA effectively reduces energy consumption while ensuring reliable communication and extending network coverage. Compared to state-of-the-art approaches such as ADR+, AURORA improves the PDR by 14% and reduces the duty cycle by 18%.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 2","pages":"223-232"},"PeriodicalIF":3.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11413253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147923900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How antenna selection and RIS can enable integrated sensing and communication with a UAV","authors":"Chandan Kumar;Payal Gupta;Jaap van de Beek","doi":"10.23919/JCN.2025.000104","DOIUrl":"https://doi.org/10.23919/JCN.2025.000104","url":null,"abstract":"We consider a scenario in which an unmanned aerial vehicle (UAV) performs transmit antenna selection (AS) and is assisted by a reconfigurable intelligent surface (RIS) for integrated sensing and communication. We optimize the antenna subset and transmit beamformer at the UAV and phase shift at RIS to maximize the beampattern gain towards the target under a specific rate constraint for the communication. To solve this, we propose an alternating optimization based algorithm in which the optimization problem is split into a sequence of subproblems and presented as semidefinite programs. Our numerical results show that an improved average beampattern gain and outage performance is obtained by increasing number of transmit antennas or number of RIS elements. We show that by increasing the RIS elements, we can obtain the savings of radiated power while keeping the average beampattern gain fixed, a system with subset AS can obtain improved performance compared to a multiantenna system. Our results also show that an improved average beampattern gain and outage performance can be obtained even when UAV goes away from the user and comes closer to the RIS and target. A discussion on real-time feasibility challenges and potential directions for future work is also presented.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 2","pages":"190-198"},"PeriodicalIF":3.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11454691","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147923911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SDN-Net: Data fusion and artificial intelligence for enhancing intrusion detection within SDN/NFV network","authors":"Amina Sahbi;Faouzi Jaidi;Adel Bouhoula","doi":"10.23919/JCN.2025.000098","DOIUrl":"https://doi.org/10.23919/JCN.2025.000098","url":null,"abstract":"The advent of software-defined networking (SDN) and network function virtualization (NFV) represents significant advances in networking and telecommunications. Their emergence reveals a change in networking paradigms that are more programmable and adaptable. Nevertheless, these technologies present new difficulties, especially regarding security. Conventional security measures are less effective in these complex, dynamic environments. In fact, through using artificial intelligence (AI) abilities, we can develop more robust and adaptive intrusion detection systems that can effectively identify and overcome challenging issues. Combining AI with SDN/NFV may assure a more secure network architecture by significantly enhancing security measures. In this paper, we propose the integration of deep learning (DL) and machine learning-based (ML) detection algorithms with the concepts of SDN and NFV, to enhance intrusion detection. DL/ML-based techniques have notable success in detecting novel and emerging types of network intrusions when supplied with sufficient and relevant training data. Anomaly detection methods rely heavily on data, which includes features that are representative of system behavior. Data fusion is essential in this context, as it combines information from multiple sources to provide a more comprehensive and accurate understanding of network activities. To address the challenge of finding relevant training data, this study introduces the SDN-Net dataset, which is the result of combining two pre-existing SDN-oriented datasets. SDN-Net has 79 features, 11 categories of traffic, and more than one and a half million rows of observations. By providing a dataset including a wide range of normal and abnormal network behaviors, we facilitate the training and testing of DL/ML models capable of detecting network threats and improving the security and resilience of SDN/NFV networks. The results demonstrate how, in terms of accuracy and efficiency, our AI-based intrusion detection model surpasses more conventional methods. We reached 99.99% precision and 99.9% accuracy when using different DL and ML models.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 2","pages":"233-249"},"PeriodicalIF":3.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11413836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147923952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2025.000126","DOIUrl":"https://doi.org/10.23919/JCN.2025.000126","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 2","pages":"270-272"},"PeriodicalIF":3.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11519566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147923891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data detection techniques for scalable cell-free massive MIMO systems","authors":"Doaa Abueida;Mahmoud A. Albreem;Saeed Abdallah;A. Abdelaziz Salem;Khawla Alnajjar;Mohamed Saad","doi":"10.23919/JCN.2025.000083","DOIUrl":"https://doi.org/10.23919/JCN.2025.000083","url":null,"abstract":"Cell-free (CF) massive multiple-input multiple-output (mMIMO) is emerging as a key technology for sixth-generation (6G) communication systems, offering nearly uniform service for users across various areas while effectively managing interference compared to traditional mMIMO systems. However, data detection in CF-mMIMO environments requires sophisticated signal processing techniques. While both linear and nonlinear detectors have demonstrated strong performance, the exploration of iterative detection methods in CF-mMIMO has been limited. This paper addresses this research gap by examining the performance of five efficient iterative scalable CF-mMIMO detectors based on approximate/avoid matrix inversion techniques: Newton iteration, Gauss-Seidel, Jacobi, accelerated over-relaxation, and successive over-relaxation. Additionally, we propose an efficient detector based on sphere decoding (CF-SD) for scalable CF-mMIMO systems. Simulation results indicate that the linear iterative methods can achieve performance that approximates that of the minimum mean square error detector, while also maintaining a lower computational burden. In addition, while the CF-SD detector demonstrates considerable performance enhancements, it requires higher computational complexity compared to its linear iterative counterparts.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"1-14"},"PeriodicalIF":3.2,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11410075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147274989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2025.000116","DOIUrl":"https://doi.org/10.23919/JCN.2025.000116","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"128-128"},"PeriodicalIF":3.2,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11410077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147274985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced 6G non-terrestrial network link performance using deep learning-based channel estimation and Doppler compensation techniques","authors":"Chaitali J. Pawase;Attiq Ur Rehman;KyungHi Chang","doi":"10.23919/JCN.2025.000086","DOIUrl":"https://doi.org/10.23919/JCN.2025.000086","url":null,"abstract":"In this paper, we present a novel approach to enhance the throughput of 6G non-terrestrial networks (NTN) by incorporating deep learning-based channel estimation, Doppler pre-compensation, and compensation techniques. We propose a new framework for accurate and efficient channel estimation in 6G-NTN systems, leveraging neural networks to improve channel estimation performance, leading to enhanced throughput and link performance. Furthermore, we introduce Doppler pre compensation and compensation techniques to address the challenges posed by high mobility scenarios in 6G-NTN. Extensive simulations demonstrate the effectiveness of our approach, showing significant improvements in mean squared error (MSE), throughput, and robustness to Doppler effects under high mobility scenario in NTN systems. The training data for the convolutional neural network (CNN) model, developed specifically for DM-RS channel estimation, demonstrates a MSE of 1.4175 at a transonic speed of 1,000 km/h and an altitude of 10 km in the NTN environment. The implementation of both Doppler pre-compensation and compensation techniques effectively neutralizes the Doppler shift. This results in a comparable bit error rate (BER) performance, achieving link reliability with a spectral efficiency of 3.325 bps/Hz at an NTN mobility of 1,000 km/h and an altitude of 10 km. The proposed framework has the potential to significantly impact the performance of 6G-NTN systems, paving the way for reliable and efficient wireless communication in challenging environments.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"73-85"},"PeriodicalIF":3.2,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11410073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2025.000117","DOIUrl":"https://doi.org/10.23919/JCN.2025.000117","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"133-135"},"PeriodicalIF":3.2,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11410072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint resource management and task offloading for ISAC-assisted vehicular edge computing networks","authors":"Junlin Huang;Jiajie Zhou;Chao Yang;Suidan Yuan;Taijun Peng;Xin Chen","doi":"10.23919/JCN.2025.000087","DOIUrl":"https://doi.org/10.23919/JCN.2025.000087","url":null,"abstract":"Edge computing and integrated sensing and communication (ISAC) technologies offer promising prospects for intelligent transportation systems (ITSs) in which the sensing data of vehicles can be processed directly or be offloaded to a base station (BS) or to the surrounding vehicles. However, the inherent scarcity of communication resources becomes a crucial problem in ITSs, especially when ISAC is introduced. In this paper, we propose an ISAC-assisted vehicular edge computing networks (VECNs) architecture composed of two interconnected stages: resource management and task offloading. Vehicles perform sensing and dynamically offload sensing tasks to the BS or nearby vehicles based on the link conditions. A two-stage joint optimization problem is formulated to optimize the resource block (RB) allocation for V2I and V2V links, including communication and sensing power among multiple vehicles, so as to maximize the overall data transmission rate. Concurrently, the offloading decisions are optimized, aiming to minimize the weighted sum of the system task completion delay and energy consumption. Considering the complex, dynamic transmission environment, we reformulate these problems as Markov Decision Processes and propose a deep reinforcement learning-based dual-stage resource management and offloading decision strategy (DDROS). Simulation results demonstrate that the proposed DDROS achieves strong convergence and exhibits significant performance advantages over baseline strategies under various conditions.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"40-51"},"PeriodicalIF":3.2,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11410070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147274995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust DOA estimation using acoustic vector sensor arrays with time-varying axial deviation under non-uniform noise","authors":"Weidong Wang;Affaq Qamar;Linya Ma;Hui Li;Zhiqiang Liu;Wentao Shi;Wasiq Ali;Sheeraz Akram","doi":"10.23919/JCN.2025.000071","DOIUrl":"https://doi.org/10.23919/JCN.2025.000071","url":null,"abstract":"To mitigate the significant degradation in direction of arrival (DOA) estimation performance caused by time-varying axial deviation (TVAD) in acoustic vector sensor (AVS) array under non-uniform noise, a two-step least squares fitting (TSLSF) technique is presented in this paper. Firstly, a model for the AVS array incorporating TVAD is established by introducing axial deviation parameters into datasets from various sub-time periods (STPs). Then, to treat the noise vectors of each channel in the AVS array as virtual sparse signals, a novel AVS array manifold matrix is formulated. After that, to estimate the TVAD matrix, sparse signals, and noise vector, two cost functions are constructed based on the principle of weighted least squares. Moreover, their analytical expressions were derived. Furthermore, to handle the effects of TVAD on DOA estimation performance over the entire observation period, the focusing technology is adopted to transform datasets with TVAD from different STPs into the desired dataset. Simulation experiments confirmed the effectiveness and resilience of the proposed method using an AVS array in conjunction with TVAD in the presence of non-uniform noise.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"15-29"},"PeriodicalIF":3.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}