Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks
Abstract The internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-11-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12678 |
| _version_ | 1797635947478646784 |
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| author | Jing Zhang Chu‐Long Liang Hong‐Xu Gao He Yan Hong‐Bo Zhu |
| author_facet | Jing Zhang Chu‐Long Liang Hong‐Xu Gao He Yan Hong‐Bo Zhu |
| author_sort | Jing Zhang |
| collection | DOAJ |
| description | Abstract The internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum access (OSA) has been addressed widely in the past decade. However, the tradeoff between two key indexes, the throughput of cognitive radio network (CRN) and the delay of secondary user (SU), is rarely mentioned so far, which ignites the authors’ work in this paper. Taking into account a channel handoff (CH) based multi‐channel OSA scenario, the authors first analyze the opportunistic transmission performance of SU, and model the throughput of CRN as well as the handoff delay of SU. Then, the authors build up a delay‐constraint throughput optimization problem, and thus formulate the throughput‐delay tradeoff for OSA. Finally, the optimal traffic rates of SU for a good throughput‐delay tradeoff are derived according to maximizing the throughput of CRN. Theoretical and simulated results show that to enhance the throughput and to reduce the delay do not conflict always. By well adjusting the traffic rates of SU according to the traffic rates of primary user, the throughput of CRN can be improved while the handoff delay can be kept under a given level. |
| first_indexed | 2024-03-11T12:28:16Z |
| format | Article |
| id | doaj.art-06e5ef95b8a4469a9d9168b62b215948 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-03-11T12:28:16Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-06e5ef95b8a4469a9d9168b62b2159482023-11-06T04:32:48ZengWileyIET Communications1751-86281751-86362023-11-0117182053206710.1049/cmu2.12678Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networksJing Zhang0Chu‐Long Liang1Hong‐Xu Gao2He Yan3Hong‐Bo Zhu4Jiangsu Province Key Lab of Wireless Communications School of Communications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing ChinaZTE Communication Co., Ltd Shenzhen ChinaChina Post Construction Technology Co., Ltd Nanjing ChinaCollege of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing ChinaJiangsu Province Key Lab of Wireless Communications School of Communications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing ChinaAbstract The internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum access (OSA) has been addressed widely in the past decade. However, the tradeoff between two key indexes, the throughput of cognitive radio network (CRN) and the delay of secondary user (SU), is rarely mentioned so far, which ignites the authors’ work in this paper. Taking into account a channel handoff (CH) based multi‐channel OSA scenario, the authors first analyze the opportunistic transmission performance of SU, and model the throughput of CRN as well as the handoff delay of SU. Then, the authors build up a delay‐constraint throughput optimization problem, and thus formulate the throughput‐delay tradeoff for OSA. Finally, the optimal traffic rates of SU for a good throughput‐delay tradeoff are derived according to maximizing the throughput of CRN. Theoretical and simulated results show that to enhance the throughput and to reduce the delay do not conflict always. By well adjusting the traffic rates of SU according to the traffic rates of primary user, the throughput of CRN can be improved while the handoff delay can be kept under a given level.https://doi.org/10.1049/cmu2.12678channel handoffcognitive radiohandoff delayMarkov processesopportunistic spectrum accessthroughput |
| spellingShingle | Jing Zhang Chu‐Long Liang Hong‐Xu Gao He Yan Hong‐Bo Zhu Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks IET Communications channel handoff cognitive radio handoff delay Markov processes opportunistic spectrum access throughput |
| title | Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks |
| title_full | Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks |
| title_fullStr | Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks |
| title_full_unstemmed | Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks |
| title_short | Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks |
| title_sort | throughput delay tradeoff for opportunistic spectrum access in cognitive radio networks |
| topic | channel handoff cognitive radio handoff delay Markov processes opportunistic spectrum access throughput |
| url | https://doi.org/10.1049/cmu2.12678 |
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