Pub Date : 2025-12-01DOI: 10.23919/JCN.2025.000069
Jiaqi Li;Seung-Hoon Hwang
This paper proposes two interference mitigation methods to evaluate the feasibility of coexistence between existing radar altimeter services and emerging 5G as well as potentially 6G cellular services in the C-band. Monte Carlo simulations are performed to evaluate the cumulative interference power from ground base stations on radar altimeters. Our results demonstrate that the antenna height protection scheme can help achieve coexistence only when the ground base station is equipped with the 4-by-4 and 8-by-8 antenna arrays in rural, suburban, and urban environments. Specifically, when using the angular protection method, ground base stations with 4-by-4, 8-by-8, or 16-by-8 antenna arrays can coexist with radar altimeters, except the ground base stations equipped with the 16-by-8 antenna array in the rural environment.
{"title":"Antenna height and angular protection methods to mitigate base station interference on radar altimeters","authors":"Jiaqi Li;Seung-Hoon Hwang","doi":"10.23919/JCN.2025.000069","DOIUrl":"https://doi.org/10.23919/JCN.2025.000069","url":null,"abstract":"This paper proposes two interference mitigation methods to evaluate the feasibility of coexistence between existing radar altimeter services and emerging 5G as well as potentially 6G cellular services in the C-band. Monte Carlo simulations are performed to evaluate the cumulative interference power from ground base stations on radar altimeters. Our results demonstrate that the antenna height protection scheme can help achieve coexistence only when the ground base station is equipped with the 4-by-4 and 8-by-8 antenna arrays in rural, suburban, and urban environments. Specifically, when using the angular protection method, ground base stations with 4-by-4, 8-by-8, or 16-by-8 antenna arrays can coexist with radar altimeters, except the ground base stations equipped with the 16-by-8 antenna array in the rural environment.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"440-453"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904272","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 : 2025-12-01DOI: 10.23919/JCN.2025.000095
{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2025.000095","DOIUrl":"https://doi.org/10.23919/JCN.2025.000095","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"555-559"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11333396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904327","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 : 2025-12-01DOI: 10.23919/JCN.2025.000061
Xinxin Yang;Qi Zhu
In recent years, symbiotic radio systems have garnered significant attention from academia and industry for addressing the challenges of spectrum scarcity and energy consumption in large-scale Internet of Things (IoT) deployments. At the same time, due to the wide distribution of IoT devices, some remote areas cannot be covered by mobile networks. Introducing unmanned aerial vehicles (UAV) into wireless networks can improve network coverage performance and increase spectrum utilization. Therefore, this paper proposes a joint optimization algorithm for channel allocation, reflection coefficients and UAV's position for a UAV-assisted symbiotic radio system consisting of multiple primary users (PUs) and multiple backscatter devices (BDs). Under the constraints of energy and the quality of service (QoS) of the primary transmission system, the optimization problem of sum rate maximization in the backscatter communication system is constructed. The Kuhn-Munkres (KM) algorithm is used to solve the channel optimal matching problem. Based on the block coordinate descent (BCD) algorithm, the non-convex problem is decomposed into three subproblems: transmission power, BDs' reflection coefficients and UAV's position. The transmission power subproblem is solved in two cases where the number of BDs is less than/greater than the number of PUs, and the expression of the optimal solution of the reflection coefficient is derived. The Nelder-Mead algorithm is used to solve the UAV's position subproblem. Finally, the global optimal solution is obtained through global iteration. Simulation results demonstrate that the proposed algorithm achieves strong convergence and significantly enhances the sum rate of backscatter communication in UAV-assisted symbiotic radio systems.
{"title":"Sum rate maximization for UAV-assisted symbiotic radio system","authors":"Xinxin Yang;Qi Zhu","doi":"10.23919/JCN.2025.000061","DOIUrl":"https://doi.org/10.23919/JCN.2025.000061","url":null,"abstract":"In recent years, symbiotic radio systems have garnered significant attention from academia and industry for addressing the challenges of spectrum scarcity and energy consumption in large-scale Internet of Things (IoT) deployments. At the same time, due to the wide distribution of IoT devices, some remote areas cannot be covered by mobile networks. Introducing unmanned aerial vehicles (UAV) into wireless networks can improve network coverage performance and increase spectrum utilization. Therefore, this paper proposes a joint optimization algorithm for channel allocation, reflection coefficients and UAV's position for a UAV-assisted symbiotic radio system consisting of multiple primary users (PUs) and multiple backscatter devices (BDs). Under the constraints of energy and the quality of service (QoS) of the primary transmission system, the optimization problem of sum rate maximization in the backscatter communication system is constructed. The Kuhn-Munkres (KM) algorithm is used to solve the channel optimal matching problem. Based on the block coordinate descent (BCD) algorithm, the non-convex problem is decomposed into three subproblems: transmission power, BDs' reflection coefficients and UAV's position. The transmission power subproblem is solved in two cases where the number of BDs is less than/greater than the number of PUs, and the expression of the optimal solution of the reflection coefficient is derived. The Nelder-Mead algorithm is used to solve the UAV's position subproblem. Finally, the global optimal solution is obtained through global iteration. Simulation results demonstrate that the proposed algorithm achieves strong convergence and significantly enhances the sum rate of backscatter communication in UAV-assisted symbiotic radio systems.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"454-463"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11333399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904329","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 : 2025-12-01DOI: 10.23919/JCN.2025.000100
{"title":"Reviewer list for 2025","authors":"","doi":"10.23919/JCN.2025.000100","DOIUrl":"https://doi.org/10.23919/JCN.2025.000100","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"548-550"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11333395","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904306","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 : 2025-12-01DOI: 10.23919/JCN.2025.000059
Chih-Heng Ke;Nien-Tzu Hsieh;Chih-Lin Hu;En-Cheng Lin
The CSMA/CA protocol adopts the binary exponential backoff algorithm that varies the value of contention window to resolve the situation of concurrent access and contention when multiple stations coexist in a wireless network. However, a dilemma arises in that a small contention window increases the probability of collision, but a large contention window increases the delay of accessing wireless channels. The design of adaptive contention window becomes essential for improving the transmission performance of a wireless network. Our study considers that using deep reinforcement learning (DRL) can better decide an appropriate value of contention window. Since DRL agents are engaged in monitoring environmental states, they can predict the coming variations and adjust the value of contention window. In this paper, we propose a linear-increase-and-linear-decrease backoff scheme with deep Q-networks (LILD-DQN) to deal with the CW optimization problem. We examine the efficacy of the LILD-DQN scheme under a high-density scenario of 100 stationary stations. Experimental simulation presents the relative performance between the CSMA/CA, LILD, CCOD-DQN, and LILD-DQN schemes. Performance results show that the proposed LILD-DQN scheme outperforms CSMA/CA by increasing 42% of throughput and decreasing 60% of collision rate. Compared with LILD and CCOD-DQN, the LILD-DQN scheme achieves the increasing throughput of 12% and 10%, and the decreasing collision rate of 37% and 29%, respectively. Hence, the LILD-DQN scheme with deep reinforcement learning is superior to prior channel contention schemes, CSMA/CA, LILD, and CCOD-DQN, in terms of throughput and collision rate.
{"title":"Linear scaling on contention window with deep Q-networks in wireless networks","authors":"Chih-Heng Ke;Nien-Tzu Hsieh;Chih-Lin Hu;En-Cheng Lin","doi":"10.23919/JCN.2025.000059","DOIUrl":"https://doi.org/10.23919/JCN.2025.000059","url":null,"abstract":"The CSMA/CA protocol adopts the binary exponential backoff algorithm that varies the value of contention window to resolve the situation of concurrent access and contention when multiple stations coexist in a wireless network. However, a dilemma arises in that a small contention window increases the probability of collision, but a large contention window increases the delay of accessing wireless channels. The design of adaptive contention window becomes essential for improving the transmission performance of a wireless network. Our study considers that using deep reinforcement learning (DRL) can better decide an appropriate value of contention window. Since DRL agents are engaged in monitoring environmental states, they can predict the coming variations and adjust the value of contention window. In this paper, we propose a linear-increase-and-linear-decrease backoff scheme with deep Q-networks (LILD-DQN) to deal with the CW optimization problem. We examine the efficacy of the LILD-DQN scheme under a high-density scenario of 100 stationary stations. Experimental simulation presents the relative performance between the CSMA/CA, LILD, CCOD-DQN, and LILD-DQN schemes. Performance results show that the proposed LILD-DQN scheme outperforms CSMA/CA by increasing 42% of throughput and decreasing 60% of collision rate. Compared with LILD and CCOD-DQN, the LILD-DQN scheme achieves the increasing throughput of 12% and 10%, and the decreasing collision rate of 37% and 29%, respectively. Hence, the LILD-DQN scheme with deep reinforcement learning is superior to prior channel contention schemes, CSMA/CA, LILD, and CCOD-DQN, in terms of throughput and collision rate.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"429-439"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904344","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 : 2025-12-01DOI: 10.23919/JCN.2025.000057
Syed M. A. H. Bukhari;Muhammad Afaq;Wang-Cheol Song
Video streaming has become one of the primary contributors to global Internet traffic, posing significant challenges to network infrastructures. Software-defined networking (SDN) offers a promising solution for managing such dynamic and bandwidth intensive services by enabling centralized control and realtime adaptability. However, decoupled decision making fails to account for the interplay between server workload and link congestion, often leading to suboptimal resource allocation. To address this issue, this paper presents a graph neural network (GNN)-based client-server-link (G-CSL) prediction model designed to optimize video streaming performance in SDN environments. G-CSL utilizes a machine learning model in conjunction with a GNN-based link estimation model. The machine learning predicts the video streaming server CPU utilization, which serves as input to the GNN model as node embeddings for link estimation between the client and server. For load forecasting, two machine learning and two deep learning models are evaluated, with random forest (RF) outperforming its counterpart. For the link estimation task, both traditional and GNN-based models are considered. GraphSAGE outperforms its counterparts by accurately estimating the existence of a link between the client and the video streaming server. A lightweight neighbor score heuristic then assigns each request to the least loaded server over the highest confidence path, maximizing a composite utility of computational headroom and bandwidth. An ablation study of the GraphSAGE model is presented highlighting the importance of architectural components, including batch normalization, bilinear decoders, temporal features, and threshold-based edge masking, in enhancing model robustness. The proposed model is evaluated under realistic video streaming scenarios involving 10,000 requests and compared with baselines. Experimental results show that G-CSL has achieved a 61% reduction in request drop rate, maintains an average delay of 22 ms per request, and improves system utility by 23%, demonstrating its effectiveness in balancing computational and bandwidth resources.
{"title":"G-CSL: A GNN-based client-server-link prediction for video streaming in SDN","authors":"Syed M. A. H. Bukhari;Muhammad Afaq;Wang-Cheol Song","doi":"10.23919/JCN.2025.000057","DOIUrl":"https://doi.org/10.23919/JCN.2025.000057","url":null,"abstract":"Video streaming has become one of the primary contributors to global Internet traffic, posing significant challenges to network infrastructures. Software-defined networking (SDN) offers a promising solution for managing such dynamic and bandwidth intensive services by enabling centralized control and realtime adaptability. However, decoupled decision making fails to account for the interplay between server workload and link congestion, often leading to suboptimal resource allocation. To address this issue, this paper presents a graph neural network (GNN)-based client-server-link (G-CSL) prediction model designed to optimize video streaming performance in SDN environments. G-CSL utilizes a machine learning model in conjunction with a GNN-based link estimation model. The machine learning predicts the video streaming server CPU utilization, which serves as input to the GNN model as node embeddings for link estimation between the client and server. For load forecasting, two machine learning and two deep learning models are evaluated, with random forest (RF) outperforming its counterpart. For the link estimation task, both traditional and GNN-based models are considered. GraphSAGE outperforms its counterparts by accurately estimating the existence of a link between the client and the video streaming server. A lightweight neighbor score heuristic then assigns each request to the least loaded server over the highest confidence path, maximizing a composite utility of computational headroom and bandwidth. An ablation study of the GraphSAGE model is presented highlighting the importance of architectural components, including batch normalization, bilinear decoders, temporal features, and threshold-based edge masking, in enhancing model robustness. The proposed model is evaluated under realistic video streaming scenarios involving 10,000 requests and compared with baselines. Experimental results show that G-CSL has achieved a 61% reduction in request drop rate, maintains an average delay of 22 ms per request, and improves system utility by 23%, demonstrating its effectiveness in balancing computational and bandwidth resources.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"521-533"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11333398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904274","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 : 2025-12-01DOI: 10.23919/JCN.2025.000096
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2025.000096","DOIUrl":"https://doi.org/10.23919/JCN.2025.000096","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"560-562"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11333393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904296","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 : 2025-12-01DOI: 10.23919/JCN.2025.000101
This index covers all papers that appeared in JCN during 2025. The Author Index contains the primary entry for eachitem, listed under the first author's name, and cross-references from all coauthors. The Title Index contains paper titles for each Division in the alphabetical order from No. 1 to No. 6. Please refer to the primary entry in the Author Index for the exact title, coauthors, and comments / corrections.
{"title":"2025 Index journal of communications and networks, volume 27","authors":"","doi":"10.23919/JCN.2025.000101","DOIUrl":"https://doi.org/10.23919/JCN.2025.000101","url":null,"abstract":"This index covers all papers that appeared in JCN during 2025. The Author Index contains the primary entry for eachitem, listed under the first author's name, and cross-references from all coauthors. The Title Index contains paper titles for each Division in the alphabetical order from No. 1 to No. 6. Please refer to the primary entry in the Author Index for the exact title, coauthors, and comments / corrections.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 6","pages":"1-4"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904324","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 : 2025-10-01DOI: 10.23919/JCN.2025.000078
{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2025.000078","DOIUrl":"https://doi.org/10.23919/JCN.2025.000078","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"421-425"},"PeriodicalIF":3.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11251111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533055","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 : 2025-10-01DOI: 10.23919/JCN.2025.000079
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2025.000079","DOIUrl":"https://doi.org/10.23919/JCN.2025.000079","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"426-428"},"PeriodicalIF":3.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11251107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533045","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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