The steeply rising demand for mobile data drives the investigation of the transmission backhaul network architecture and cost for the fifth generation (5G) of mobile technologies. The proposed backhaul architecture will facilitate high throughput, low latency, scalability, low cost of ownership, and high capacity backhaul for 5G mobile technologies. This paper presents a transmission backhaul network architecture for 5G technology; the proposed internet protocol (IP) transmission backhauling architecture includes the data center, core network, distribution network, and access or IP random access network. A mathematical model for the data center IP core network, IP distributed network, and the IP access network for capital expenditure (Capex), operational expenditure (Opex), and the total cost of ownership (TCO) are presented, as well as a mathematical model for the entire backhauling architecture. The result shows that the increase in IP sites is positively proportional to the Capex and negatively proportional to the Opex. The selectivity analysis shows that the increase in bandwidth is directly proportional to the Capex, Opex, and TCO in the IP core network. The increase in data centers is directly proportional to the Capex, Opex, and TCO of the entire backhauling architecture.
移动数据需求的急剧增长推动了对第五代（5G）移动技术的传输回程网络架构和成本的研究。所提出的回程架构将促进 5G 移动技术的高吞吐量、低延迟、可扩展性、低拥有成本和高容量回程。本文提出了一种适用于 5G 技术的传输回程网络架构；建议的互联网协议（IP）传输回程架构包括数据中心、核心网络、分配网络和接入网或 IP 随机接入网。提出了数据中心 IP 核心网、IP 分布网和 IP 接入网的资本支出（Capex）、运营支出（Opex）和总拥有成本（TCO）的数学模型，以及整个回程架构的数学模型。结果表明，IP 站点的增加与资本支出（Capex）成正比，与运营支出（Opex）成反比。选择性分析表明，带宽的增加与 IP 核心网的资本支出、运营支出和总体拥有成本成正比。数据中心的增加与整个回程架构的资本支出、运营支出和总拥有成本成正比。
{"title":"Low Latency 5G IP Transmission Backhaul Network Architecture: A Techno-Economic Analysis","authors":"Ibrahim Alhassan Gedel, Nnamdi I. Nwulu","doi":"10.1155/2024/6388723","DOIUrl":"https://doi.org/10.1155/2024/6388723","url":null,"abstract":"The steeply rising demand for mobile data drives the investigation of the transmission backhaul network architecture and cost for the fifth generation (5G) of mobile technologies. The proposed backhaul architecture will facilitate high throughput, low latency, scalability, low cost of ownership, and high capacity backhaul for 5G mobile technologies. This paper presents a transmission backhaul network architecture for 5G technology; the proposed internet protocol (IP) transmission backhauling architecture includes the data center, core network, distribution network, and access or IP random access network. A mathematical model for the data center IP core network, IP distributed network, and the IP access network for capital expenditure (Capex), operational expenditure (Opex), and the total cost of ownership (TCO) are presented, as well as a mathematical model for the entire backhauling architecture. The result shows that the increase in IP sites is positively proportional to the Capex and negatively proportional to the Opex. The selectivity analysis shows that the increase in bandwidth is directly proportional to the Capex, Opex, and TCO in the IP core network. The increase in data centers is directly proportional to the Capex, Opex, and TCO of the entire backhauling architecture.","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Federated learning-based medical data privacy sharing can promote the development of medical industry intelligence, but limited by its own security and privacy deficiencies, federated learning still suffers from a single point of failure and privacy leakage of intermediate parameters. To address these problems, this paper proposes a privacy protection framework for medical data based on blockchain and cross-silo federated learning, using cross-silo federated learning to establish a collaborative training platform for multiple medical institutions to enhance the privacy of medical data, introducing blockchain and smart contracts to realize decentralized federated learning to enhance trust between distrustful medical institutions and solve the problem of a single point of failure. In addition, a secure aggregation scheme is designed using threshold homomorphic encryption to prevent the privacy leakage problem during parameter transmission. The experimental and analytical results show that the accuracy of this paper’s scheme is consistent with the original federated learning scheme, effectively deals with the problems of single-point failure and inference attacks of federated learning, improves system robustness, and is suitable for medical scenarios with more stringent requirements on security and accuracy.
{"title":"Federated Medical Learning Framework Based on Blockchain and Homomorphic Encryption","authors":"Xiaohui Yang, Chongbo Xing","doi":"10.1155/2024/8138644","DOIUrl":"https://doi.org/10.1155/2024/8138644","url":null,"abstract":"Federated learning-based medical data privacy sharing can promote the development of medical industry intelligence, but limited by its own security and privacy deficiencies, federated learning still suffers from a single point of failure and privacy leakage of intermediate parameters. To address these problems, this paper proposes a privacy protection framework for medical data based on blockchain and cross-silo federated learning, using cross-silo federated learning to establish a collaborative training platform for multiple medical institutions to enhance the privacy of medical data, introducing blockchain and smart contracts to realize decentralized federated learning to enhance trust between distrustful medical institutions and solve the problem of a single point of failure. In addition, a secure aggregation scheme is designed using threshold homomorphic encryption to prevent the privacy leakage problem during parameter transmission. The experimental and analytical results show that the accuracy of this paper’s scheme is consistent with the original federated learning scheme, effectively deals with the problems of single-point failure and inference attacks of federated learning, improves system robustness, and is suitable for medical scenarios with more stringent requirements on security and accuracy.","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139103760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hyperledger Fabric (shortened to Fabric) is an open-source, enterprise-level, permissioned distributed ledger technology platform with a highly modular, configurable architecture. It supports writing smart contracts in general-purpose programing languages and has become the preferred choice for enterprise-level blockchain applications. However, the transaction throughput of the Fabric system remains a critical factor that restricts the further application of this technology in various fields. Therefore, it is necessary to evaluate and optimize the performance of the Fabric blockchain platform. Existing performance modeling methods need to be improved in terms of compatibility and effectiveness. To address this, we propose a performance-compatible modeling method for Fabric using queuing theory, which considers the limited transaction pool and the situation where node groups are attacked. Using the Fabric 2.0 version as an example, we have established a model of the transaction process in the Fabric network. By analyzing the model’s continuous 3D time Markov process, we solved the system stationary equation and obtained analytical expressions for performance indicators such as system throughput, system steady-state queue length, and system average response time. We conducted extensive analyses and simulations to verify the models’ and formulations’ accuracy and validity. We believe this approach can be extended to various scenarios in other blockchain systems.
{"title":"Performance Modeling of Hyperledger Fabric 2.0: A Queuing Theory-Based Approach","authors":"Ou Wu, Zhongxing Wang, Zhongjin Li","doi":"10.1155/2023/9957995","DOIUrl":"https://doi.org/10.1155/2023/9957995","url":null,"abstract":"Hyperledger Fabric (shortened to Fabric) is an open-source, enterprise-level, permissioned distributed ledger technology platform with a highly modular, configurable architecture. It supports writing smart contracts in general-purpose programing languages and has become the preferred choice for enterprise-level blockchain applications. However, the transaction throughput of the Fabric system remains a critical factor that restricts the further application of this technology in various fields. Therefore, it is necessary to evaluate and optimize the performance of the Fabric blockchain platform. Existing performance modeling methods need to be improved in terms of compatibility and effectiveness. To address this, we propose a performance-compatible modeling method for Fabric using queuing theory, which considers the limited transaction pool and the situation where node groups are attacked. Using the Fabric 2.0 version as an example, we have established a model of the transaction process in the Fabric network. By analyzing the model’s continuous 3D time Markov process, we solved the system stationary equation and obtained analytical expressions for performance indicators such as system throughput, system steady-state queue length, and system average response time. We conducted extensive analyses and simulations to verify the models’ and formulations’ accuracy and validity. We believe this approach can be extended to various scenarios in other blockchain systems.","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automatic modulation recognition plays an important role in many military and civilian applications, including cognitive radio, spectrum sensing, signal surveillance, and interference identification. Due to the powerful ability of deep learning to extract hidden features and perform classification, it can extract highly separative features from massive signal samples. Considering the condition of limited training samples, we propose a semi-supervised learning framework based on Haar time–frequency (HTF) mask data augmentation and the positional–spatial attention (PSA) mechanism. Specifically, the HTF mask is designed to increase data diversity, and the PSA is designed to address the limited receptive field of the convolutional layer and enhance the feature extraction capability of the constructed network. Extensive experimental results obtained on the public RML2016.10a dataset show that the proposed semi-supervised framework utilizes 1% of the given labeled data and reaches a recognition accuracy of 92.09% under 6 dB signals.
{"title":"Semi-supervised Learning for Automatic Modulation Recognition Using Haar Time–Frequency Mask and Positional–Spatial Attention","authors":"Hui Liu, Dan Zhong, Yuanpu Guo, Zehong Xu, Zhenlin Wu, Chunxian Gao","doi":"10.1155/2023/2683780","DOIUrl":"https://doi.org/10.1155/2023/2683780","url":null,"abstract":"Automatic modulation recognition plays an important role in many military and civilian applications, including cognitive radio, spectrum sensing, signal surveillance, and interference identification. Due to the powerful ability of deep learning to extract hidden features and perform classification, it can extract highly separative features from massive signal samples. Considering the condition of limited training samples, we propose a semi-supervised learning framework based on Haar time–frequency (HTF) mask data augmentation and the positional–spatial attention (PSA) mechanism. Specifically, the HTF mask is designed to increase data diversity, and the PSA is designed to address the limited receptive field of the convolutional layer and enhance the feature extraction capability of the constructed network. Extensive experimental results obtained on the public RML2016.10a dataset show that the proposed semi-supervised framework utilizes 1% of the given labeled data and reaches a recognition accuracy of 92.09% under 6 dB signals.","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138826440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we model the causes of power-related network outages in Ghana using discrete-time Markov chains. We used data consisting of 2,756 small-scale carrier telecommunications outages occurring in Ghana, with accompanying root causes over a period of 5 years and 8 months, from August 2015 to April 2021. The results indicate that the majority (n = 1,404) of the network outages were caused by the generators while the least number (18) of outages were caused by a communication equipment. However, longer network outages were caused by fuel issues with an average outage time of 1,027.82 min over the study period. The transition probability matrix obtained from the data revealed that regardless of the present cause of the network outage, the probability that the next network outage will be caused by the generators is higher than the probability that the outage will be attributable to any other cause. The steady-state distribution indicates that in the long run (n ≥ 16), 51% of the network outages will be caused by the “Generators” while 10.8% of the network outages will be caused by the “Batteries.” We also checked and simulated the probabilities of a network outage caused by any of the 12 possible root causes for 12 steps. It seemed apparent from the simulations that generators are the most likely cause of network outages from Step 1 up to Step 7, irrespective of what the initial cause of the network outage is. With these findings, players in the telecommunications industry can clearly plan better to reduce future network outages.
{"title":"Modeling the Causes of Power-Related Network Outages Using Discrete-Time Markov Chains","authors":"Ibrahim A. Gedel, Wahab A. Iddrisu","doi":"10.1155/2023/8717626","DOIUrl":"https://doi.org/10.1155/2023/8717626","url":null,"abstract":"In this paper, we model the causes of power-related network outages in Ghana using discrete-time Markov chains. We used data consisting of 2,756 small-scale carrier telecommunications outages occurring in Ghana, with accompanying root causes over a period of 5 years and 8 months, from August 2015 to April 2021. The results indicate that the majority (<i>n</i> = 1,404) of the network outages were caused by the generators while the least number (18) of outages were caused by a communication equipment. However, longer network outages were caused by fuel issues with an average outage time of 1,027.82 min over the study period. The transition probability matrix obtained from the data revealed that regardless of the present cause of the network outage, the probability that the next network outage will be caused by the generators is higher than the probability that the outage will be attributable to any other cause. The steady-state distribution indicates that in the long run (<i>n</i> ≥ 16), 51% of the network outages will be caused by the “Generators” while 10.8% of the network outages will be caused by the “Batteries.” We also checked and simulated the probabilities of a network outage caused by any of the 12 possible root causes for 12 steps. It seemed apparent from the simulations that generators are the most likely cause of network outages from Step 1 up to Step 7, irrespective of what the initial cause of the network outage is. With these findings, players in the telecommunications industry can clearly plan better to reduce future network outages.","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138821225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a study on a model of multirelay radio network system that utilizes reconfigurable intelligent surfaces (RISs). We investigate the use of nonorthogonal multiple access (NOMA) combined with cooperative RIS systems, using partial RIS selection (PRISs). Specifically, the RISs act as relays to forward data from the base station to the two users. The focus of this paper is to analyze the outage probabilities and throughput for the two users. Based on the results, we examine how PRISs affect the performance of the proposed NOMA scheme. The derived asymptotic expressions show that the proposed model can improve user fairness. Finally, we compare the analysis results with the simulation results and find good agreement.
{"title":"Performance Analysis of Multiple-RIS-Based NOMA Systems","authors":"Huu Q. Tran, Quoc-Tuan Vien","doi":"10.1155/2023/6785737","DOIUrl":"https://doi.org/10.1155/2023/6785737","url":null,"abstract":"In this paper, we present a study on a model of multirelay radio network system that utilizes reconfigurable intelligent surfaces (RISs). We investigate the use of nonorthogonal multiple access (NOMA) combined with cooperative RIS systems, using partial RIS selection (PRISs). Specifically, the RISs act as relays to forward data from the base station to the two users. The focus of this paper is to analyze the outage probabilities and throughput for the two users. Based on the results, we examine how PRISs affect the performance of the proposed NOMA scheme. The derived asymptotic expressions show that the proposed model can improve user fairness. Finally, we compare the analysis results with the simulation results and find good agreement.","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138715226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks","authors":"Wireless Communications and Mobile Computing","doi":"10.1155/2023/9878739","DOIUrl":"https://doi.org/10.1155/2023/9878739","url":null,"abstract":"<jats:p />","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"26 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138976503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search","authors":"Wireless Communications and Mobile Computing","doi":"10.1155/2023/9867609","DOIUrl":"https://doi.org/10.1155/2023/9867609","url":null,"abstract":"<jats:p />","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"143 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139006152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: The Index Data System of Agricultural Modernization Development Based on Internet Big Data","authors":"Wireless Communications and Mobile Computing","doi":"10.1155/2023/9814819","DOIUrl":"https://doi.org/10.1155/2023/9814819","url":null,"abstract":"<jats:p />","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138976431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Image Energy Saving Recognition Technology of Monitoring System Based on Ant Colony Algorithm","authors":"Wireless Communications and Mobile Computing","doi":"10.1155/2023/9835360","DOIUrl":"https://doi.org/10.1155/2023/9835360","url":null,"abstract":"<jats:p />","PeriodicalId":501499,"journal":{"name":"Wireless Communications and Mobile Computing","volume":"101 4‐6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138976956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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