Joint opportunistic MIMO-mode selection and channel–user assignment for improved throughput in beyond 5G networks
Cognitive radio (CR) technology enables dynamic spectrum access for 5G and Beyond 5G (B5G) networks. Specifically, CR provides massive spectrum opportunities for enabling large-scale deployment of 5G/B5G networks. Multiple-input, multiple-output (MIMO) technology is another promising technique that...
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| Published in | Ad hoc networks Vol. 144; p. 103151 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.05.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1570-8705 1570-8713 |
| DOI | 10.1016/j.adhoc.2023.103151 |
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| Abstract | Cognitive radio (CR) technology enables dynamic spectrum access for 5G and Beyond 5G (B5G) networks. Specifically, CR provides massive spectrum opportunities for enabling large-scale deployment of 5G/B5G networks. Multiple-input, multiple-output (MIMO) technology is another promising technique that can significantly improve the performance of 5G/B5G networks. Integrating the MIMO technology with the CR technology can further improve the wireless network’s performance. However, due to the dynamic nature of the CR operating environment, such integration imposes several design challenges regarding the appropriate MIMO-mode selection, spectrum assignment, and user selection. Unlike the majority of previously developed MIMO-based spectrum access protocols that execute the channel assignment sequentially, we investigate the problem of maximizing the network throughput by simultaneously providing distributed CR-aware MIMO-mode channel-assignment decisions for multiple CR transmissions (batching approach) subject to a set of constraints. The joint batch-based MIMO-mode and channel-assignment optimization is, in general, an NP-hard combinatorial integer non-linear problem. To solve such a joint problem in polynomial-time, we propose a two-stage optimization algorithm. In the first stage, the proposed algorithm finds the highest number of packets that each contending CR user can send over each idle channel. Then, in the second stage, the best channel–user assignment that can result in the highest number of transmitted packets is computed. To realize the proposed batch-based algorithm in a distributed manner, we adopt an admission control process that allows CR users to announce the required control information. Compared with the existing MIMO-based CRN protocols, the results show that by jointly performing batch-based channel assignment and per-user MIMO-mode optimization, the proposed protocol significantly enhances the overall network throughput by up to 60%. |
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| AbstractList | Cognitive radio (CR) technology enables dynamic spectrum access for 5G and Beyond 5G (B5G) networks. Specifically, CR provides massive spectrum opportunities for enabling large-scale deployment of 5G/B5G networks. Multiple-input, multiple-output (MIMO) technology is another promising technique that can significantly improve the performance of 5G/B5G networks. Integrating the MIMO technology with the CR technology can further improve the wireless network’s performance. However, due to the dynamic nature of the CR operating environment, such integration imposes several design challenges regarding the appropriate MIMO-mode selection, spectrum assignment, and user selection. Unlike the majority of previously developed MIMO-based spectrum access protocols that execute the channel assignment sequentially, we investigate the problem of maximizing the network throughput by simultaneously providing distributed CR-aware MIMO-mode channel-assignment decisions for multiple CR transmissions (batching approach) subject to a set of constraints. The joint batch-based MIMO-mode and channel-assignment optimization is, in general, an NP-hard combinatorial integer non-linear problem. To solve such a joint problem in polynomial-time, we propose a two-stage optimization algorithm. In the first stage, the proposed algorithm finds the highest number of packets that each contending CR user can send over each idle channel. Then, in the second stage, the best channel–user assignment that can result in the highest number of transmitted packets is computed. To realize the proposed batch-based algorithm in a distributed manner, we adopt an admission control process that allows CR users to announce the required control information. Compared with the existing MIMO-based CRN protocols, the results show that by jointly performing batch-based channel assignment and per-user MIMO-mode optimization, the proposed protocol significantly enhances the overall network throughput by up to 60%. |
| ArticleNumber | 103151 |
| Author | Alkana’neh, Aseel Halloush, Rami Jararweh, Yaser Musa, Ahmed Bany Salameh, Haythem |
| Author_xml | – sequence: 1 givenname: Haythem surname: Bany Salameh fullname: Bany Salameh, Haythem organization: Artificial Intelligence Research Center, Al Ain University, Al Ain, United Arab Emirates – sequence: 2 givenname: Aseel surname: Alkana’neh fullname: Alkana’neh, Aseel organization: Telecommunications Engineering Department, Yarmouk University, Irbid, Jordan – sequence: 3 givenname: Rami surname: Halloush fullname: Halloush, Rami organization: Telecommunications Engineering Department, Yarmouk University, Irbid, Jordan – sequence: 4 givenname: Ahmed surname: Musa fullname: Musa, Ahmed organization: Telecommunications Engineering Department, Yarmouk University, Irbid, Jordan – sequence: 5 givenname: Yaser orcidid: 0000-0003-3429-7212 surname: Jararweh fullname: Jararweh, Yaser email: yaser@email.arizona.edu organization: Computer Science Department, Jordan University of Science and Technology, Irbid, Jordan |
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| Cites_doi | 10.1109/WCNC.2012.6213952 10.1109/ICTC.2016.7763393 10.1109/ICECCME55909.2022.9988381 10.1109/JSYST.2022.3154162 10.1016/j.adhoc.2018.07.021 10.1109/JSAC.2003.810348 10.1080/08839514.2018.1486132 10.1109/TCCN.2019.2933816 10.1109/JIOT.2021.3127747 10.1016/j.adhoc.2023.103111 10.1109/TWC.2021.3122959 10.1007/s11277-020-07704-5 10.1109/TVT.2022.3148063 10.1109/ICCE50685.2021.9427695 10.3390/s22031240 10.1093/ietcom/e88-b.7.3010 10.1109/TCCN.2021.3130986 10.1016/j.compeleceng.2014.07.013 10.1007/BF02190106 10.1145/800057.808695 |
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