Pub Date : 2025-09-18DOI: 10.23919/JCN.2025.000051
José Gómez-delaHiz;Enrique Moguel;Javier Berrocal;Juan M. Murillo;Jaime Galán-Jiménez
The global expansion of Internet connectivity is a well-documented trend, especially in urban and developed regions. However, many rural and low-income populations still face limited or no Internet access, where the absence of Internet connectivity impedes crucial services such as remote healthcare, emergency assistance, distance learning, and personal commu nication. In this context, the main challenge for the research community is to expand digital coverage in rural areas, thus providing a better quality of life and service in the area. Existing solutions often focus on optimizing isolated metrics (such as latency, energy consumption, or throughput) limiting their flexibility and real-world applicability, which reduces their applicability and flexibility, respectively. This paper addresses this digital divide by proposing an innovative strategy using unmanned aerial vehicles (UAVs) to create a network and deliver digital services to remote rural areas without Internet access. The strategy involves breaking down Internet of things (IoT) applications into microservices and deploying them via UAVs. This approach achieves both reduced latency and lower energy consumption, improving the quality of service for latency-sensitive applications. The focus is particularly crucial for applications with stringent requirements, such as those related to remote health care or emergency services. Simulations conducted in a realistic scenario validate the efficacy of the proposed solution. The results showcase a notable reduction in energy consumption and latency associated with UAVs while handling such requests.
{"title":"Low latency and energy efficient algorithm for the deployment of IoT applications in rural areas making use of UAV networks","authors":"José Gómez-delaHiz;Enrique Moguel;Javier Berrocal;Juan M. Murillo;Jaime Galán-Jiménez","doi":"10.23919/JCN.2025.000051","DOIUrl":"https://doi.org/10.23919/JCN.2025.000051","url":null,"abstract":"The global expansion of Internet connectivity is a well-documented trend, especially in urban and developed regions. However, many rural and low-income populations still face limited or no Internet access, where the absence of Internet connectivity impedes crucial services such as remote healthcare, emergency assistance, distance learning, and personal commu nication. In this context, the main challenge for the research community is to expand digital coverage in rural areas, thus providing a better quality of life and service in the area. Existing solutions often focus on optimizing isolated metrics (such as latency, energy consumption, or throughput) limiting their flexibility and real-world applicability, which reduces their applicability and flexibility, respectively. This paper addresses this digital divide by proposing an innovative strategy using unmanned aerial vehicles (UAVs) to create a network and deliver digital services to remote rural areas without Internet access. The strategy involves breaking down Internet of things (IoT) applications into microservices and deploying them via UAVs. This approach achieves both reduced latency and lower energy consumption, improving the quality of service for latency-sensitive applications. The focus is particularly crucial for applications with stringent requirements, such as those related to remote health care or emergency services. Simulations conducted in a realistic scenario validate the efficacy of the proposed solution. The results showcase a notable reduction in energy consumption and latency associated with UAVs while handling such requests.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"496-508"},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904323","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}
Benefit to the advantages of flexibility and low-cost deployment, employing unmanned aerial vehicle (UAV) as the aerial base station (ABS) constitutes a promising technology to support the multi-user access. However, facing the challenges of both mobile ABS and ever-increasing users, it is crucial to design the UAV trajectory in the dynamic environment, in order to providing communications services for multiple mobile users with fair consideration. Therefore, in this paper, we propose a fairly non-orthogonal multiple access (FNOMA) scheme for UAV communications network, which enables the ground mobile users to be accessed to the ABS by sharing the same spectrum resources with high fairness. For the sake of optimizing the attained system throughput, a novel greedy genetic algorithm assisted Q-learning (GGA-Q) method is conceived, where the UAV trajectory is elaborately designed by jointly considering dynamic user grouping and power allocation. Our simulation results indicate that the proposed UAV trajectory planning algorithm based on FNOMA scheme can significantly improve the fairness level while enhancing system throughput.
{"title":"Q-learning based trajectory design for UAV communications network with fairly non-orthogonal multiple access","authors":"Simeng Feng;Kai Liu;Yunyi Zhang;Chao Dong;Lei Zhang;Qihui Wu","doi":"10.23919/JCN.2025.000054","DOIUrl":"https://doi.org/10.23919/JCN.2025.000054","url":null,"abstract":"Benefit to the advantages of flexibility and low-cost deployment, employing unmanned aerial vehicle (UAV) as the aerial base station (ABS) constitutes a promising technology to support the multi-user access. However, facing the challenges of both mobile ABS and ever-increasing users, it is crucial to design the UAV trajectory in the dynamic environment, in order to providing communications services for multiple mobile users with fair consideration. Therefore, in this paper, we propose a fairly non-orthogonal multiple access (FNOMA) scheme for UAV communications network, which enables the ground mobile users to be accessed to the ABS by sharing the same spectrum resources with high fairness. For the sake of optimizing the attained system throughput, a novel greedy genetic algorithm assisted Q-learning (GGA-Q) method is conceived, where the UAV trajectory is elaborately designed by jointly considering dynamic user grouping and power allocation. Our simulation results indicate that the proposed UAV trajectory planning algorithm based on FNOMA scheme can significantly improve the fairness level while enhancing system throughput.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"483-495"},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904298","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 : 2025-09-18DOI: 10.23919/JCN.2025.000050
Carlo Augusto Grazia;Martin Klapez;Maurizio Casoni
The performance of Internet services heavily relies on efficient traffic management mechanisms. This paper investigates the impact of imposing a local bottleneck at the access networks' side compared to the conventional approach of traffic shaping by Internet service providers (ISPs) through first-in-firstout (FIFO) queues. The proposed local bottleneck strategy aims to enhance Internet performance by reducing latency, mitigating congestion, isolating distinct traffic flows, and enabling quality of service (QoS) differentiation. Through extensive experimentation on various home and office network setups, including fiber to the cabinet (FTTC), fiber to the home (FTTH), and fixed wireless access (FWA), we demonstrate the efficacy of the local bottleneck approach in delivering consistent and high-quality Internet performance, especially in congested environments. The results reveal that the traditional ISP bottleneck struggles to maintain a high-performance standard under congested conditions, highlighting the need for innovative traffic management techniques.
{"title":"Less is more: Reducing bandwidth to enable queueing control and QoS","authors":"Carlo Augusto Grazia;Martin Klapez;Maurizio Casoni","doi":"10.23919/JCN.2025.000050","DOIUrl":"https://doi.org/10.23919/JCN.2025.000050","url":null,"abstract":"The performance of Internet services heavily relies on efficient traffic management mechanisms. This paper investigates the impact of imposing a local bottleneck at the access networks' side compared to the conventional approach of traffic shaping by Internet service providers (ISPs) through first-in-firstout (FIFO) queues. The proposed local bottleneck strategy aims to enhance Internet performance by reducing latency, mitigating congestion, isolating distinct traffic flows, and enabling quality of service (QoS) differentiation. Through extensive experimentation on various home and office network setups, including fiber to the cabinet (FTTC), fiber to the home (FTTH), and fixed wireless access (FWA), we demonstrate the efficacy of the local bottleneck approach in delivering consistent and high-quality Internet performance, especially in congested environments. The results reveal that the traditional ISP bottleneck struggles to maintain a high-performance standard under congested conditions, highlighting the need for innovative traffic management techniques.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"509-520"},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904337","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 : 2025-09-18DOI: 10.23919/JCN.2025.000053
Noor Hafsa;Hadeel Alzoubi;Sajida Imran
The IoT has emerged as a significant target for cyber-attacks, particularly with a focus on the routing protocol for low-power and lossy networks (RPL) within Wireless Sensor Networks (WSNs). These attacks can disrupt network topologies and compromise data transmission. Early detection of routing attacks is crucial, particularly in resource-constrained RPL networks. This study employed a simulated dataset encompassing Hello Flood, Version Number, and Worst Parent attacks to develop a robust detection model for resource-based routing attacks in IoT networks. In this research, a novel cross-layer feature analysis was conducted, identifying 12 key features crucial for distinguishing between normal and malicious nodes within the network out of the 29 features examined. Various machine learning algorithms, including random forest, CatBoost, and extreme gradient boosting, were evaluated for precise classification. The optimized CatBoost model, a gradient-boosting decision tree (DT) algorithm, demonstrated outstanding performance with a 99% of detection rate, 0.8% of false positive rate, 98% of sensitivity, and 98% of positive predictive values on an independent test dataset. Furthermore, an advanced intrusion prevention algorithm leveraging cross-layer feature-induced intrusion detection was introduced to effectively combat prevalent routing attacks. This study significantly contributes to enhancing cybersecurity in IoT networks, particularly in smart cities, by offering robust intrusion detection and prevention mechanisms.
{"title":"Machine learning-based intrusion detection and prevention using cross-layer features in Internet of Things (IoT) networks","authors":"Noor Hafsa;Hadeel Alzoubi;Sajida Imran","doi":"10.23919/JCN.2025.000053","DOIUrl":"https://doi.org/10.23919/JCN.2025.000053","url":null,"abstract":"The IoT has emerged as a significant target for cyber-attacks, particularly with a focus on the routing protocol for low-power and lossy networks (RPL) within Wireless Sensor Networks (WSNs). These attacks can disrupt network topologies and compromise data transmission. Early detection of routing attacks is crucial, particularly in resource-constrained RPL networks. This study employed a simulated dataset encompassing Hello Flood, Version Number, and Worst Parent attacks to develop a robust detection model for resource-based routing attacks in IoT networks. In this research, a novel cross-layer feature analysis was conducted, identifying 12 key features crucial for distinguishing between normal and malicious nodes within the network out of the 29 features examined. Various machine learning algorithms, including random forest, CatBoost, and extreme gradient boosting, were evaluated for precise classification. The optimized CatBoost model, a gradient-boosting decision tree (DT) algorithm, demonstrated outstanding performance with a 99% of detection rate, 0.8% of false positive rate, 98% of sensitivity, and 98% of positive predictive values on an independent test dataset. Furthermore, an advanced intrusion prevention algorithm leveraging cross-layer feature-induced intrusion detection was introduced to effectively combat prevalent routing attacks. This study significantly contributes to enhancing cybersecurity in IoT networks, particularly in smart cities, by offering robust intrusion detection and prevention mechanisms.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"345-358"},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533052","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 : 2025-09-18DOI: 10.23919/JCN.2025.000055
Yi Zhang;Qi Jiang;Zhuo Ma;Kofi Kwarteng Abrokwa
Effective integration of available resources within edge nodes is essential to improve the performance of vehicular edge computing (VEC) to support various randomly offloaded tasks with limited computing capacity and constrained energy. This paper presents an intelligent adaptive resource integration strategy for VEC with energy harvesting. Service caching, task migration and resource allocation are jointly employed to accommodate the temporally and spatially varying computing demands. The optimization to minimize the long-term average task execution time under energy constraint is formulated as Markov decision processes and solved with a parameterized deep Q-network based learning algorithm. This algorithm employs a centralized training and distributed execution framework, where a parameter network and an action network respectively handle continuous and discrete decisions, effectively tackling the hybrid action space challenges in problem solving. Simulation results demonstrate that the proposed algorithm not only achieves faster convergence but also significantly improves system performance compared to benchmarks.
{"title":"Deep reinforcement learning based adaptive resource integration for vehicular edge computing with energy harvesting","authors":"Yi Zhang;Qi Jiang;Zhuo Ma;Kofi Kwarteng Abrokwa","doi":"10.23919/JCN.2025.000055","DOIUrl":"https://doi.org/10.23919/JCN.2025.000055","url":null,"abstract":"Effective integration of available resources within edge nodes is essential to improve the performance of vehicular edge computing (VEC) to support various randomly offloaded tasks with limited computing capacity and constrained energy. This paper presents an intelligent adaptive resource integration strategy for VEC with energy harvesting. Service caching, task migration and resource allocation are jointly employed to accommodate the temporally and spatially varying computing demands. The optimization to minimize the long-term average task execution time under energy constraint is formulated as Markov decision processes and solved with a parameterized deep Q-network based learning algorithm. This algorithm employs a centralized training and distributed execution framework, where a parameter network and an action network respectively handle continuous and discrete decisions, effectively tackling the hybrid action space challenges in problem solving. Simulation results demonstrate that the proposed algorithm not only achieves faster convergence but also significantly improves system performance compared to benchmarks.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"474-482"},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904282","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 : 2025-09-18DOI: 10.23919/JCN.2025.000060
Bin Bai;Gang Xie;Yuanan Liu
The increasing demand for massive device access in the Internet of things (IoT) necessitates the use of non-orthogonal multiple access (NOMA) technology and grant-free (GF) transmission to enhance spectrum efficiency. Semi-grant-free (SGF) transmission has garnered significant research attention in recent years because it enables grant-free (GF) users to share resource blocks with grant-based (GB) users, thereby enhancing spectrum efficiency in large-scale device access scenarios. This study focuses on distributed GF transmission and integrates it with the sparse code multiple access (SCMA) scheme to address the low-latency needs of large-scale short-packet transmission devices while simultaneously meeting the transmission rate requirements of GB users. The proposed SGF-SCMA scheme, based on a user hierarchical strategy, significantly enhances spectral efficiency and reduces delay overhead for GF users compared to existing schemes. The successful transmission probability of SGF-SCMA scheme is derived, and the proposed control transmission factor is optimized to improve the throughput in massive user access scenarios. The solution of the derived successful transmission probability was showed through simulation, and the crosstalk problem of the SCMA system was analyzed. Considering the delay of GF users, the throughput can be improved by 20% to 43% compared with the existing SGF scheme.
{"title":"Distributed semi-grant-free SCMA transmission scheme in IoT networks","authors":"Bin Bai;Gang Xie;Yuanan Liu","doi":"10.23919/JCN.2025.000060","DOIUrl":"https://doi.org/10.23919/JCN.2025.000060","url":null,"abstract":"The increasing demand for massive device access in the Internet of things (IoT) necessitates the use of non-orthogonal multiple access (NOMA) technology and grant-free (GF) transmission to enhance spectrum efficiency. Semi-grant-free (SGF) transmission has garnered significant research attention in recent years because it enables grant-free (GF) users to share resource blocks with grant-based (GB) users, thereby enhancing spectrum efficiency in large-scale device access scenarios. This study focuses on distributed GF transmission and integrates it with the sparse code multiple access (SCMA) scheme to address the low-latency needs of large-scale short-packet transmission devices while simultaneously meeting the transmission rate requirements of GB users. The proposed SGF-SCMA scheme, based on a user hierarchical strategy, significantly enhances spectral efficiency and reduces delay overhead for GF users compared to existing schemes. The successful transmission probability of SGF-SCMA scheme is derived, and the proposed control transmission factor is optimized to improve the throughput in massive user access scenarios. The solution of the derived successful transmission probability was showed through simulation, and the crosstalk problem of the SCMA system was analyzed. Considering the delay of GF users, the throughput can be improved by 20% to 43% compared with the existing SGF scheme.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"464-473"},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904292","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 : 2025-09-18DOI: 10.23919/JCN.2025.000032
Chao Ren;Linfeng Ye;Difei Cao;Xianmei Wang;Chuan Zhao;Yin Long;Haojin Li;Chen Sun
In integrated sensing and communication (ISAC) system, achieving signal-level integration is essential but challenging, as direct amalgamation of communication and sensing signals confronts inherent heterogeneity, engendering substantial complexity in both software and hardware. This paper presents a strategy for enhancing ISAC systems by implementing a semantic-level slicing approach to address key issues as resource utilization, real-time capability, and computational complexity in ISAC systems. A bidirectional mapping mechanism is introduced, combined with edge intelligence, that enhances system predictability, autonomy, and reduces task completion costs by slicing and allocating semantic resources in cloud-edge environments. Additionally, the proposed communication-sensing complementary strategy leverages semantic fusion to enable efficient and adaptable execution of communication and sensing tasks at the resource level, resulting in enhanced task flexibility. The simulation results show that the proposed bidirectional mapping and communication-sensing complementary method significantly improves the signal-to-interference-noise ratio of the traditional system by about 55% in medium and low dynamic environments.
{"title":"Semantic-empowered integrated sensing and communication with resource slicing and bidirectional mapping","authors":"Chao Ren;Linfeng Ye;Difei Cao;Xianmei Wang;Chuan Zhao;Yin Long;Haojin Li;Chen Sun","doi":"10.23919/JCN.2025.000032","DOIUrl":"https://doi.org/10.23919/JCN.2025.000032","url":null,"abstract":"In integrated sensing and communication (ISAC) system, achieving signal-level integration is essential but challenging, as direct amalgamation of communication and sensing signals confronts inherent heterogeneity, engendering substantial complexity in both software and hardware. This paper presents a strategy for enhancing ISAC systems by implementing a semantic-level slicing approach to address key issues as resource utilization, real-time capability, and computational complexity in ISAC systems. A bidirectional mapping mechanism is introduced, combined with edge intelligence, that enhances system predictability, autonomy, and reduces task completion costs by slicing and allocating semantic resources in cloud-edge environments. Additionally, the proposed communication-sensing complementary strategy leverages semantic fusion to enable efficient and adaptable execution of communication and sensing tasks at the resource level, resulting in enhanced task flexibility. The simulation results show that the proposed bidirectional mapping and communication-sensing complementary method significantly improves the signal-to-interference-noise ratio of the traditional system by about 55% in medium and low dynamic environments.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"534-546"},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904263","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 : 2025-09-18DOI: 10.23919/JCN.2025.000052
Mahmoud A. Albreem;Saeed Abdallah;Mohamed Saad;Mahmoud Aldababsa;Khawla Alnajjar
Massive multiple-input multiple-output (mMIMO) plays a crucial role in improving the quality-of-service and achieving high power efficiency and spectrum efficiency in beyond fifth generation communication systems. However, data detection in uplink mMIMO is not a trivial task as the computational complexity increases with the number of antennas. The equalization matrix is diagonally dominant, and hence, most of the existing linear detectors use the diagonal matrix. Unfortunately, detection based on a diagonal matrix may require a high number of iterations to converge, which increases the computational complexity. This is highly challenging because of the large number of antennas on both the transmitting and receiving sides. In this paper, we propose a refinement of six linear mMIMO detectors based on a band matrix formulation to accelerate the convergence rate, and hence reduce the complexity. The proposed linear detectors include the Newton iterations method, the Neumann series method, the accelerated over-relaxation method, the successive over-relaxation method, the Gauss-Seidel (GS) method, and the Jacobi method. The computation of the band matrix inverse is also presented in this paper and employed in the proposed detectors. In addition, efficient initialization based on the structure of the band matrix is proposed, which both improves the convergence rate and yields a substantial performance gain. Simulations show that the proposed detectors achieve minimum mean-squared error performance with significant complexity reduction even when the number of users approaches the number of base station antennas. It is also shown that the refined detector based on the GS and band matrix achieves the highest performance gain with the lowest computational complexity.
{"title":"Low complexity detectors for uplink massive MIMO based on a refinement of linear algorithms and efficient initialization","authors":"Mahmoud A. Albreem;Saeed Abdallah;Mohamed Saad;Mahmoud Aldababsa;Khawla Alnajjar","doi":"10.23919/JCN.2025.000052","DOIUrl":"https://doi.org/10.23919/JCN.2025.000052","url":null,"abstract":"Massive multiple-input multiple-output (mMIMO) plays a crucial role in improving the quality-of-service and achieving high power efficiency and spectrum efficiency in beyond fifth generation communication systems. However, data detection in uplink mMIMO is not a trivial task as the computational complexity increases with the number of antennas. The equalization matrix is diagonally dominant, and hence, most of the existing linear detectors use the diagonal matrix. Unfortunately, detection based on a diagonal matrix may require a high number of iterations to converge, which increases the computational complexity. This is highly challenging because of the large number of antennas on both the transmitting and receiving sides. In this paper, we propose a refinement of six linear mMIMO detectors based on a band matrix formulation to accelerate the convergence rate, and hence reduce the complexity. The proposed linear detectors include the Newton iterations method, the Neumann series method, the accelerated over-relaxation method, the successive over-relaxation method, the Gauss-Seidel (GS) method, and the Jacobi method. The computation of the band matrix inverse is also presented in this paper and employed in the proposed detectors. In addition, efficient initialization based on the structure of the band matrix is proposed, which both improves the convergence rate and yields a substantial performance gain. Simulations show that the proposed detectors achieve minimum mean-squared error performance with significant complexity reduction even when the number of users approaches the number of base station antennas. It is also shown that the refined detector based on the GS and band matrix achieves the highest performance gain with the lowest computational complexity.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"298-309"},"PeriodicalIF":3.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533054","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 : 2025-08-26DOI: 10.23919/JCN.2025.000036
Tingting Yang;Yingqi Zhao;Jin Jin;Kaiyang Guo
This paper considers device-to-device (D2D) communication underlaying cellular networks, where frequency resources are shared between D2D users and cellular users. When D2D users reuse the frequency resources occupied by the cellular users, interference could be produced among the two kinds of users. By means of the fractional frequency reuse approach, a traversal frequency reuse scheme is proposed, in which central D2D users in each cell reuse the frequency bands for neighbouring edge cellular users in sequence according to a counterclockwise direction. The proposed reuse scheme effectively minimizes the number of intra-cell interference links, leading to an improvement of the average sum rate. Subsequently, based on the proposed traversal reuse strategy, a deep Q-learning algorithm is implemented for power control. Simulation results demonstrate that the proposed power control algorithm outperforms other traditional methods in terms of sum rate.
{"title":"Power control for D2D communication underlaying cellular networks based on deep Q-learning and fractional frequency reuse","authors":"Tingting Yang;Yingqi Zhao;Jin Jin;Kaiyang Guo","doi":"10.23919/JCN.2025.000036","DOIUrl":"https://doi.org/10.23919/JCN.2025.000036","url":null,"abstract":"This paper considers device-to-device (D2D) communication underlaying cellular networks, where frequency resources are shared between D2D users and cellular users. When D2D users reuse the frequency resources occupied by the cellular users, interference could be produced among the two kinds of users. By means of the fractional frequency reuse approach, a traversal frequency reuse scheme is proposed, in which central D2D users in each cell reuse the frequency bands for neighbouring edge cellular users in sequence according to a counterclockwise direction. The proposed reuse scheme effectively minimizes the number of intra-cell interference links, leading to an improvement of the average sum rate. Subsequently, based on the proposed traversal reuse strategy, a deep Q-learning algorithm is implemented for power control. Simulation results demonstrate that the proposed power control algorithm outperforms other traditional methods in terms of sum rate.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"359-368"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533043","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 : 2025-08-26DOI: 10.23919/JCN.2025.000047
Cheol Hea Koo
Space networks face significant challenges establishing physical links due to uncertain node positions and heterogeneous communication systems, requiring pre-computed flight dynamics and higher readiness levels than terrestrial networks. This study introduces a visionary server, an authoritative ground-based management system generating pre-negotiated paths instead of relying on real-time computations by individual nodes. The server distributes routing information through a structured extension block for bundle protocol version 7, enabling contact establishment without prior knowledge of adjacent node characteristics. Qualitative simulations using realistic scenarios validate this concept's effectiveness. The key innovation lies in the dual-mode architecture combining centralized coordination with distributed failure detection, which: (i) reduces computational requirements by processing on ground, (ii) facilitates network trouble shoots through available routing information, and (iii) considers data rate, frequency, and modulation type while orchestrating routing decisions across heterogeneous space assets without requiring physical layer standardization.
{"title":"An architecture for bundle routing in space: Collaborative contact negotiation and visionary server for DTN route development","authors":"Cheol Hea Koo","doi":"10.23919/JCN.2025.000047","DOIUrl":"https://doi.org/10.23919/JCN.2025.000047","url":null,"abstract":"Space networks face significant challenges establishing physical links due to uncertain node positions and heterogeneous communication systems, requiring pre-computed flight dynamics and higher readiness levels than terrestrial networks. This study introduces a visionary server, an authoritative ground-based management system generating pre-negotiated paths instead of relying on real-time computations by individual nodes. The server distributes routing information through a structured extension block for bundle protocol version 7, enabling contact establishment without prior knowledge of adjacent node characteristics. Qualitative simulations using realistic scenarios validate this concept's effectiveness. The key innovation lies in the dual-mode architecture combining centralized coordination with distributed failure detection, which: (i) reduces computational requirements by processing on ground, (ii) facilitates network trouble shoots through available routing information, and (iii) considers data rate, frequency, and modulation type while orchestrating routing decisions across heterogeneous space assets without requiring physical layer standardization.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"327-344"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142620","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533053","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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