Pub Date : 2026-02-24DOI: 10.23919/JCN.2025.000083
Doaa Abueida;Mahmoud A. Albreem;Saeed Abdallah;A. Abdelaziz Salem;Khawla Alnajjar;Mohamed Saad
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.
{"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}
Pub Date : 2026-02-24DOI: 10.23919/JCN.2025.000116
{"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}
Pub Date : 2026-02-24DOI: 10.23919/JCN.2025.000086
Chaitali J. Pawase;Attiq Ur Rehman;KyungHi Chang
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.
{"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}
Pub Date : 2026-02-24DOI: 10.23919/JCN.2025.000117
{"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}
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.
{"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}
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.
{"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}
We develop adaptive frequency block allocation schemes to mitigate the interference between intelligent low Earth orbit (LEO) satellites. As satellite networks attract increasing attention, the demand for limited frequency resources is expected to surge, creating a need for more efficient frequency utilization techniques. In particular, intelligent and dynamic frequency allocation methods will be more popular, which underscores the necessity for novel frequency resource allocation algorithms that take these considerations into account. In this work, we introduce two resource allocation strategies that exploit multi-agent reinforcement learning: the unmodified terrestrial-to-satellite (UTS) strategy that extends previous terrestrial method to the satellite environment, and the adapted satellite-specific (ASS) strategy that is tailored to satellite communication systems. Through simulations in both controlled and interference-prone environments, we evaluate and compare their performance, showing that, compared to the UTS strategy, the proposed ASS strategy improves throughput by up to 38% and reduces collision rate by up to 89% across different interference scenarios. Our findings highlight the effectiveness of customized resource allocation strategies in dynamic LEO satellite environments, paving the way for more efficient and scalable satellite communication systems in 6G networks.
{"title":"Multi-agent adaptive frequency block selection of LEO satellites for interference avoidance","authors":"Jihyeon Yun;Taegun An;Bon-Jun Ku;Daesub Oh;Changhee Joo","doi":"10.23919/JCN.2025.000072","DOIUrl":"https://doi.org/10.23919/JCN.2025.000072","url":null,"abstract":"We develop adaptive frequency block allocation schemes to mitigate the interference between intelligent low Earth orbit (LEO) satellites. As satellite networks attract increasing attention, the demand for limited frequency resources is expected to surge, creating a need for more efficient frequency utilization techniques. In particular, intelligent and dynamic frequency allocation methods will be more popular, which underscores the necessity for novel frequency resource allocation algorithms that take these considerations into account. In this work, we introduce two resource allocation strategies that exploit multi-agent reinforcement learning: the unmodified terrestrial-to-satellite (UTS) strategy that extends previous terrestrial method to the satellite environment, and the adapted satellite-specific (ASS) strategy that is tailored to satellite communication systems. Through simulations in both controlled and interference-prone environments, we evaluate and compare their performance, showing that, compared to the UTS strategy, the proposed ASS strategy improves throughput by up to 38% and reduces collision rate by up to 89% across different interference scenarios. Our findings highlight the effectiveness of customized resource allocation strategies in dynamic LEO satellite environments, paving the way for more efficient and scalable satellite communication systems in 6G networks.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"52-60"},"PeriodicalIF":3.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275007","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}
This study proposes a novel approach to ensure the security of textual data transmission in a semantic communication system. In the proposed system, a sender transmits textual information to a receiver, while a potential eavesdropper attempts to intercept the information. At the sender side, the text is initially preprocessed, where each sentence is annotated with its corresponding topic, and subsequently extracted into a knowledge graph. To achieve the secure transmission of the knowledge graph, we propose a channel encryption scheme that integrates constellation diagonal transformation with multiparameter weighted fractional Fourier transform (MP-WFRFT). At the receiver side, the textual data is first decrypted, and then recovered via a transformer model. Experimental results demonstrate that the proposed method reduces the probability of information compromise. The legitimate receiver achieves a bilingual evaluation understudy (BLEU) score of 0.9, whereas the BLEU score of the eavesdropper remains below 0.3. Compared to the baselines, the proposed method can improve the security by up to 20%.
{"title":"A secure semantic communication system based on knowledge graph","authors":"Qin Guo;Haonan Tong;Sihua Wang;Peiyuan Si;Jun Zhao;Changchuan Yin","doi":"10.23919/JCN.2025.000074","DOIUrl":"https://doi.org/10.23919/JCN.2025.000074","url":null,"abstract":"This study proposes a novel approach to ensure the security of textual data transmission in a semantic communication system. In the proposed system, a sender transmits textual information to a receiver, while a potential eavesdropper attempts to intercept the information. At the sender side, the text is initially preprocessed, where each sentence is annotated with its corresponding topic, and subsequently extracted into a knowledge graph. To achieve the secure transmission of the knowledge graph, we propose a channel encryption scheme that integrates constellation diagonal transformation with multiparameter weighted fractional Fourier transform (MP-WFRFT). At the receiver side, the textual data is first decrypted, and then recovered via a transformer model. Experimental results demonstrate that the proposed method reduces the probability of information compromise. The legitimate receiver achieves a bilingual evaluation understudy (BLEU) score of 0.9, whereas the BLEU score of the eavesdropper remains below 0.3. Compared to the baselines, the proposed method can improve the security by up to 20%.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"98-110"},"PeriodicalIF":3.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11333397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275008","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}
Pub Date : 2026-01-06DOI: 10.23919/JCN.2025.000082
Mugon Joe;Miru Kim;Minhae Kwon
The rapid development of network systems has highlighted the critical importance of robust network intrusion detection systems (NIDS) for ensuring security. A key challenge in developing effective NIDS is class imbalance, where certain traffic types dominate while others have significantly fewer samples. This issue can be addressed by generating appropriate representations for each class within an imbalanced distribution. This study develops a training framework to tackle class imbalance in NIDS. To mitigate class imbalance, we employ contrastive learning to enhance feature representations. In this process, pairs of samples are selected such that one sample is drawn based on its original probability in the dataset, while the other sample is chosen using the inverse of this probability. We also propose a novel approach for refining borderline samples, improving the classification accuracy of samples near decision boundaries. Extensive simulations are conducted on six datasets, including real-world datasets, comparing the proposed method with state-of-the-art algorithms. The results demonstrate that the proposed solution achieves superior accuracy, outperforming all existing methods with an average improvement of 9.92%.
{"title":"Contrastive learning based network attack classifier for imbalanced data","authors":"Mugon Joe;Miru Kim;Minhae Kwon","doi":"10.23919/JCN.2025.000082","DOIUrl":"https://doi.org/10.23919/JCN.2025.000082","url":null,"abstract":"The rapid development of network systems has highlighted the critical importance of robust network intrusion detection systems (NIDS) for ensuring security. A key challenge in developing effective NIDS is class imbalance, where certain traffic types dominate while others have significantly fewer samples. This issue can be addressed by generating appropriate representations for each class within an imbalanced distribution. This study develops a training framework to tackle class imbalance in NIDS. To mitigate class imbalance, we employ contrastive learning to enhance feature representations. In this process, pairs of samples are selected such that one sample is drawn based on its original probability in the dataset, while the other sample is chosen using the inverse of this probability. We also propose a novel approach for refining borderline samples, improving the classification accuracy of samples near decision boundaries. Extensive simulations are conducted on six datasets, including real-world datasets, comparing the proposed method with state-of-the-art algorithms. The results demonstrate that the proposed solution achieves superior accuracy, outperforming all existing methods with an average improvement of 9.92%.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"86-97"},"PeriodicalIF":3.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147274991","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}
Pub Date : 2026-01-06DOI: 10.23919/JCN.2025.000006
Mantisha Gupta;Rakesh Kumar Jha;Santosh Sharma
The evolutions in communication technologies demand high-performance processing units and reliable back- hauling lines for the management of vast data in wireless networks. A reliable low-latency network is, therefore, essential for efficient data transfer, system maintenance, and information dissemination. This paper analyzes a backbone network system, for consideration in the real-time deployment and analysis of touch technology interfacing middleware networks. The proposed layer-wise network deployed using graph theory underscores an ultra-reliable, low-latency network design for optimal network performance. The algorithm selects symmetric or asymmetric deployed networks based on the topology and application requirements, ensuring minimum latency. The network optimizes throughput, latency, and data transfer for efficient connectivity between sources and destinations. It connects to controllers and edge devices at the user end, ensuring reliable data transfer and efficient communication. The computational time of the deployed network path between the source and destination end is evaluated and compared with popular algorithms, determining the computational complexity of the deployed network. Finally, the computational complexities between existing network approaches and the proposed deployed network are compared. This paper thus outlines optimal network design for touch technology systems in 6G.
{"title":"Network optimization algorithm for 6G enabled touch-technology system using graph theory","authors":"Mantisha Gupta;Rakesh Kumar Jha;Santosh Sharma","doi":"10.23919/JCN.2025.000006","DOIUrl":"https://doi.org/10.23919/JCN.2025.000006","url":null,"abstract":"The evolutions in communication technologies demand high-performance processing units and reliable back- hauling lines for the management of vast data in wireless networks. A reliable low-latency network is, therefore, essential for efficient data transfer, system maintenance, and information dissemination. This paper analyzes a backbone network system, for consideration in the real-time deployment and analysis of touch technology interfacing middleware networks. The proposed layer-wise network deployed using graph theory underscores an ultra-reliable, low-latency network design for optimal network performance. The algorithm selects symmetric or asymmetric deployed networks based on the topology and application requirements, ensuring minimum latency. The network optimizes throughput, latency, and data transfer for efficient connectivity between sources and destinations. It connects to controllers and edge devices at the user end, ensuring reliable data transfer and efficient communication. The computational time of the deployed network path between the source and destination end is evaluated and compared with popular algorithms, determining the computational complexity of the deployed network. Finally, the computational complexities between existing network approaches and the proposed deployed network are compared. This paper thus outlines optimal network design for touch technology systems in 6G.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"28 1","pages":"111-126"},"PeriodicalIF":3.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275002","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}
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