Pub Date : 2023-04-01DOI: 10.23919/JCN.2023.100018
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2023.100018","DOIUrl":"https://doi.org/10.23919/JCN.2023.100018","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10127634/10127637.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49948848","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 : 2023-04-01DOI: 10.23919/JCN.2023.100017
{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2023.100017","DOIUrl":"https://doi.org/10.23919/JCN.2023.100017","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10127634/10127635.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49948843","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 : 2023-03-25DOI: 10.23919/JCN.2023.000006
Howon Lee;Byungju Lee;Heecheol Yang;Junghyun Kim;Seungnyun Kim;Wonjae Shin;Byonghyo Shim;H. Vincent Poor
Technology forecasts anticipate a new era in which massive numbers of humans, machines, and things are connected to wireless networks to sense, process, act, and communicate with the surrounding environment in a real-time manner. To make the visions come true, the sixth generation (6G) wireless networks should be hyper-connected, implying that there are no constraints on the data rate, coverage, and computing. In this article, we first identify the main challenges for 6G hyperconnectivity, including terabits-per-second (Tbps) data rates for immersive user experiences, zero coverage-hole networks, and pervasive computing for connected intelligence. To overcome these challenges, we highlight key enabling technologies for 6G such as distributed and intelligence-aware cell-free massive multi-input multi-output (MIMO) networks, boundless and fully integrated terrestrial and non-terrestrial networks, and communication-aware distributed computing for computationintensive applications. We further illustrate and discuss the hyper-connected 6G network architecture along with open issues and future research directions.
{"title":"Towards 6G hyper-connectivity: Vision, challenges, and key enabling technologies","authors":"Howon Lee;Byungju Lee;Heecheol Yang;Junghyun Kim;Seungnyun Kim;Wonjae Shin;Byonghyo Shim;H. Vincent Poor","doi":"10.23919/JCN.2023.000006","DOIUrl":"https://doi.org/10.23919/JCN.2023.000006","url":null,"abstract":"Technology forecasts anticipate a new era in which massive numbers of humans, machines, and things are connected to wireless networks to sense, process, act, and communicate with the surrounding environment in a real-time manner. To make the visions come true, the sixth generation (6G) wireless networks should be hyper-connected, implying that there are no constraints on the data rate, coverage, and computing. In this article, we first identify the main challenges for 6G hyperconnectivity, including terabits-per-second (Tbps) data rates for immersive user experiences, zero coverage-hole networks, and pervasive computing for connected intelligence. To overcome these challenges, we highlight key enabling technologies for 6G such as distributed and intelligence-aware cell-free massive multi-input multi-output (MIMO) networks, boundless and fully integrated terrestrial and non-terrestrial networks, and communication-aware distributed computing for computationintensive applications. We further illustrate and discuss the hyper-connected 6G network architecture along with open issues and future research directions.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10190217/10136521.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49950916","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 : 2023-02-01DOI: 10.23919/JCN.2023.100012
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2023.100012","DOIUrl":"https://doi.org/10.23919/JCN.2023.100012","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10077469/10077473.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49944451","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 : 2023-02-01DOI: 10.23919/JCN.2023.000002
Mina Soltani Siapoush;Shahram Jamali;Amin Badirzadeh
The growth of information technology along with the revolution of the industry and business has led to the generation of an enormous amount of data. This big data needs a platform beyond the traditional data possessing context that relies on some computational servers communicating through a network in its lower layer. One of the most important challenges in data processing is how to transfer the big batches of data between the servers to achieve fast responsiveness. Consequently, the underlying network plays a critical role in the performance of a big data analysis platform. Ideally, this network must use the shortest path that has the lowest amount of load, to transfer the large-scale data. To address this issue, we propose a software-defined networking (SDN) enabled scheduling method that uses the tabu search algorithm to schedule big data tasks. The proposed algorithm not only considers data locality but also uses the network traffic status for efficient scheduling. Our extensive simulative study in the Mininet emulator shows that the proposed scheme gives high performance and minimizes job completion time.
{"title":"Software-defined networking enabled big data tasks scheduling: A tabu search approach","authors":"Mina Soltani Siapoush;Shahram Jamali;Amin Badirzadeh","doi":"10.23919/JCN.2023.000002","DOIUrl":"https://doi.org/10.23919/JCN.2023.000002","url":null,"abstract":"The growth of information technology along with the revolution of the industry and business has led to the generation of an enormous amount of data. This big data needs a platform beyond the traditional data possessing context that relies on some computational servers communicating through a network in its lower layer. One of the most important challenges in data processing is how to transfer the big batches of data between the servers to achieve fast responsiveness. Consequently, the underlying network plays a critical role in the performance of a big data analysis platform. Ideally, this network must use the shortest path that has the lowest amount of load, to transfer the large-scale data. To address this issue, we propose a software-defined networking (SDN) enabled scheduling method that uses the tabu search algorithm to schedule big data tasks. The proposed algorithm not only considers data locality but also uses the network traffic status for efficient scheduling. Our extensive simulative study in the Mininet emulator shows that the proposed scheme gives high performance and minimizes job completion time.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10077469/10077470.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49944444","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 : 2023-02-01DOI: 10.23919/JCN.2023.100011
{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2023.100011","DOIUrl":"https://doi.org/10.23919/JCN.2023.100011","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10077469/10077471.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49944453","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 : 2023-01-09DOI: 10.23919/JCN.2022.000057
Mehmet Ariman;Mertkan Akkoç;Talip Tolga Sari;Muhammed Raşit Erol;Gökhan Seçinti;Berk Canberk
Rapid deployment of wireless devices with 5G and beyond enabled a connected world. However, an immediate demand increase right after a disaster paralyzes network infrastructure temporarily. The continuous flow of information is crucial during disaster times to coordinate rescue operations and identify the survivors. Communication infrastructures built for users of disaster areas should satisfy rapid deployment, increased coverage, and availability. Unmanned air vehicles (UAV) provide a potential solution for rapid deployment as they are not affected by traffic jams and physical road damage during a disaster. In addition, ad-hoc WiFi communication allows the generation of broadcast domains within a clear channel which eases one-to-many communications. Moreover, using reinforcement learning (RL) helps reduce the computational cost and increases the accuracy of the NP-hard problem of aerial network deployment. To this end, a novel flying WiFi ad-hoc network management model is proposed in this paper. The model utilizes deep-Q-learning to maintain quality-of-service (QoS), increase user equipment (UE) coverage, and optimize power efficiency. Furthermore, a testbed is deployed on Istanbul Technical University (ITU) campus to train the developed model. Training results of the model using testbed accumulates over 90% packet delivery ratio as QoS, over 97% coverage for the users in flow tables, and 0.28 KJ/Bit average power consumption.
{"title":"Energy-efficient RL-based aerial network deployment testbed for disaster areas","authors":"Mehmet Ariman;Mertkan Akkoç;Talip Tolga Sari;Muhammed Raşit Erol;Gökhan Seçinti;Berk Canberk","doi":"10.23919/JCN.2022.000057","DOIUrl":"https://doi.org/10.23919/JCN.2022.000057","url":null,"abstract":"Rapid deployment of wireless devices with 5G and beyond enabled a connected world. However, an immediate demand increase right after a disaster paralyzes network infrastructure temporarily. The continuous flow of information is crucial during disaster times to coordinate rescue operations and identify the survivors. Communication infrastructures built for users of disaster areas should satisfy rapid deployment, increased coverage, and availability. Unmanned air vehicles (UAV) provide a potential solution for rapid deployment as they are not affected by traffic jams and physical road damage during a disaster. In addition, ad-hoc WiFi communication allows the generation of broadcast domains within a clear channel which eases one-to-many communications. Moreover, using reinforcement learning (RL) helps reduce the computational cost and increases the accuracy of the NP-hard problem of aerial network deployment. To this end, a novel flying WiFi ad-hoc network management model is proposed in this paper. The model utilizes deep-Q-learning to maintain quality-of-service (QoS), increase user equipment (UE) coverage, and optimize power efficiency. Furthermore, a testbed is deployed on Istanbul Technical University (ITU) campus to train the developed model. Training results of the model using testbed accumulates over 90% packet delivery ratio as QoS, over 97% coverage for the users in flow tables, and 0.28 KJ/Bit average power consumption.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10077469/10012517.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49977741","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 : 2023-01-09DOI: 10.23919/JCN.2022.000058
Hoa Tran-Dang;Dong-Seong Kim
Fog computing networks have been widely integrated in IoT-based systems to improve the quality of services (QoS) such as low response service delay through efficient offloading algorithms. However, designing an efficient offloading solution is still facing many challenges including the complicated heterogeneity of fog computing devices and complex computation tasks. In addition, the need for a scalable and distributed algorithm with low computational complexity can be unachievable by global optimization approaches with centralized information management in the dense fog networks. In these regards, this paper proposes a distributed computation offloading framework (DISCO) for offloading the splittable tasks using matching theory. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems compared to some related works.
{"title":"DISCO: Distributed computation offloading framework for fog computing networks","authors":"Hoa Tran-Dang;Dong-Seong Kim","doi":"10.23919/JCN.2022.000058","DOIUrl":"https://doi.org/10.23919/JCN.2022.000058","url":null,"abstract":"Fog computing networks have been widely integrated in IoT-based systems to improve the quality of services (QoS) such as low response service delay through efficient offloading algorithms. However, designing an efficient offloading solution is still facing many challenges including the complicated heterogeneity of fog computing devices and complex computation tasks. In addition, the need for a scalable and distributed algorithm with low computational complexity can be unachievable by global optimization approaches with centralized information management in the dense fog networks. In these regards, this paper proposes a distributed computation offloading framework (DISCO) for offloading the splittable tasks using matching theory. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems compared to some related works.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10077469/10012518.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49944443","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 : 2023-01-09DOI: 10.23919/JCN.2022.000037
Wonjun Kim;Yongjun Ahn;Jinhong Kim;Byonghyo Shim
Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success of DL has stimulated increasing interest in applying this paradigm to wireless channel estimation in recent years. Since DL principles are inductive in nature and distinct from the conventional rule-based algorithms, when one tries to use DL technique to the channel estimation, one might easily get stuck and confused by so many knobs to control and small details to be aware of. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition, and neural network design for 6G. Specifically, we present several case studies together with the numerical experiments to demonstrate the effectiveness of the DL-based wireless channel estimation framework.
{"title":"Towards deep learning-aided wireless channel estimation and channel state information feedback for 6G","authors":"Wonjun Kim;Yongjun Ahn;Jinhong Kim;Byonghyo Shim","doi":"10.23919/JCN.2022.000037","DOIUrl":"https://doi.org/10.23919/JCN.2022.000037","url":null,"abstract":"Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success of DL has stimulated increasing interest in applying this paradigm to wireless channel estimation in recent years. Since DL principles are inductive in nature and distinct from the conventional rule-based algorithms, when one tries to use DL technique to the channel estimation, one might easily get stuck and confused by so many knobs to control and small details to be aware of. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition, and neural network design for 6G. Specifically, we present several case studies together with the numerical experiments to demonstrate the effectiveness of the DL-based wireless channel estimation framework.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10077469/10012511.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49977738","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 : 2023-01-09DOI: 10.23919/JCN.2022.000053
Haoyu You;Zhiquan Bai;Hongwu Liu;Theodoros A. Tsiftsis;Kyung Sup Kwak
Intelligent reflecting surface (IRS) has been regarded as promising technique to improve system performance for wireless communications. In this paper, we propose a rate-splitting (RS) scheme for an IRS-assisted cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) system, where the primary user's (PU's) quality of service (QoS) requirements must be guaranteed to be same as in orthogonal multiple access. Assisted by IRS, the threshold for the PU's tolerable interference power is improved, which in turn makes it possible to increase the achievable rate for the secondary user (SU). The optimal transmit power allocation, target rate allocation, and successive interference cancellation (SIC) decoding order are jointly designed for the proposed RS scheme. Taking into account the statistics of the direct link and IRS reflecting channels, closed-form expression for the PU's and SU's outage probabilities are respectively derived. Various simulation results are presented to clarify the enhanced outage performance achieved by the proposed RS scheme over the existing CR-NOMA and IRS-assisted CR-NOMA schemes.
{"title":"Rate-splitting for intelligent reflecting surface-assisted CR-NOMA systems","authors":"Haoyu You;Zhiquan Bai;Hongwu Liu;Theodoros A. Tsiftsis;Kyung Sup Kwak","doi":"10.23919/JCN.2022.000053","DOIUrl":"https://doi.org/10.23919/JCN.2022.000053","url":null,"abstract":"Intelligent reflecting surface (IRS) has been regarded as promising technique to improve system performance for wireless communications. In this paper, we propose a rate-splitting (RS) scheme for an IRS-assisted cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) system, where the primary user's (PU's) quality of service (QoS) requirements must be guaranteed to be same as in orthogonal multiple access. Assisted by IRS, the threshold for the PU's tolerable interference power is improved, which in turn makes it possible to increase the achievable rate for the secondary user (SU). The optimal transmit power allocation, target rate allocation, and successive interference cancellation (SIC) decoding order are jointly designed for the proposed RS scheme. Taking into account the statistics of the direct link and IRS reflecting channels, closed-form expression for the PU's and SU's outage probabilities are respectively derived. Various simulation results are presented to clarify the enhanced outage performance achieved by the proposed RS scheme over the existing CR-NOMA and IRS-assisted CR-NOMA schemes.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5449605/10077469/10012514.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49944446","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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