Journal of Communications and Networks最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}