Connected map-induced resource allocation scheme for cognitive radio network quality of service maximization
Abstract Quality of Service (QoS) in cognitive radio networks (CRNs) is achieved through fair resource allocation and scheduling for secondary users regardless of channel capacity through multi-channel communications. Fairness index updates are periodic towards multi-user allocations to meet the QoS...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-98946-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849713516427083776 |
|---|---|
| author | K. Saravanan R. Jaikumar Stalin Allwin Devaraj Om Prakash Kumar |
| author_facet | K. Saravanan R. Jaikumar Stalin Allwin Devaraj Om Prakash Kumar |
| author_sort | K. Saravanan |
| collection | DOAJ |
| description | Abstract Quality of Service (QoS) in cognitive radio networks (CRNs) is achieved through fair resource allocation and scheduling for secondary users regardless of channel capacity through multi-channel communications. Fairness index updates are periodic towards multi-user allocations to meet the QoS demands. In this article, a Connected Resource Map-induced Resource Allocation Scheme (CRM-RAS) is introduced. The proposed scheme identifies radio and user resource availability and constructs an allocation map from the primary users. For a periodic allocation interval, the map’s fairness index is updated through maximum resource utilization and QoS factor. This QoS factor is computed based on low latency and high allocation rates that are directly proportional to the fairness index. The fairness index is verified using distributed federated learning that is active between the primary and secondary user terminals. If the fairness index drops below the actual allocation rate, then the scheduling for resource allocation with concurrency is pursued. Based on the improving fairness index through concurrent scheduling the distributed federated learning encourages consecutive radio resource allocation. Thus the process is repeated until the allocation map is confined to a one-to-one connectivity between the primary and secondary users. The proposed CRM-RAS achieves 8.15% high sum rate and 8.88% less error rate for the maximum SNR. |
| format | Article |
| id | doaj-art-20d2204d1f8f4430a12dc4d5236aed8e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-20d2204d1f8f4430a12dc4d5236aed8e2025-08-20T03:13:57ZengNature PortfolioScientific Reports2045-23222025-04-0115112110.1038/s41598-025-98946-5Connected map-induced resource allocation scheme for cognitive radio network quality of service maximizationK. Saravanan0R. Jaikumar1Stalin Allwin Devaraj2Om Prakash Kumar3Department of Mechatronics Engineering, KPR Institute of Engineering and TechnologyDepartment of Electronics and Communication Engineering, KPR Institute of Engineering and TechnologyDepartment of Electronics and Communication Engineering, Francis Xavier Engineering CollegeDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationAbstract Quality of Service (QoS) in cognitive radio networks (CRNs) is achieved through fair resource allocation and scheduling for secondary users regardless of channel capacity through multi-channel communications. Fairness index updates are periodic towards multi-user allocations to meet the QoS demands. In this article, a Connected Resource Map-induced Resource Allocation Scheme (CRM-RAS) is introduced. The proposed scheme identifies radio and user resource availability and constructs an allocation map from the primary users. For a periodic allocation interval, the map’s fairness index is updated through maximum resource utilization and QoS factor. This QoS factor is computed based on low latency and high allocation rates that are directly proportional to the fairness index. The fairness index is verified using distributed federated learning that is active between the primary and secondary user terminals. If the fairness index drops below the actual allocation rate, then the scheduling for resource allocation with concurrency is pursued. Based on the improving fairness index through concurrent scheduling the distributed federated learning encourages consecutive radio resource allocation. Thus the process is repeated until the allocation map is confined to a one-to-one connectivity between the primary and secondary users. The proposed CRM-RAS achieves 8.15% high sum rate and 8.88% less error rate for the maximum SNR.https://doi.org/10.1038/s41598-025-98946-5CRNFederated learningQoSResource allocationResource schedulingSDG 9: Industry, Innovation, and Infrastructure |
| spellingShingle | K. Saravanan R. Jaikumar Stalin Allwin Devaraj Om Prakash Kumar Connected map-induced resource allocation scheme for cognitive radio network quality of service maximization Scientific Reports CRN Federated learning QoS Resource allocation Resource scheduling SDG 9: Industry, Innovation, and Infrastructure |
| title | Connected map-induced resource allocation scheme for cognitive radio network quality of service maximization |
| title_full | Connected map-induced resource allocation scheme for cognitive radio network quality of service maximization |
| title_fullStr | Connected map-induced resource allocation scheme for cognitive radio network quality of service maximization |
| title_full_unstemmed | Connected map-induced resource allocation scheme for cognitive radio network quality of service maximization |
| title_short | Connected map-induced resource allocation scheme for cognitive radio network quality of service maximization |
| title_sort | connected map induced resource allocation scheme for cognitive radio network quality of service maximization |
| topic | CRN Federated learning QoS Resource allocation Resource scheduling SDG 9: Industry, Innovation, and Infrastructure |
| url | https://doi.org/10.1038/s41598-025-98946-5 |
| work_keys_str_mv | AT ksaravanan connectedmapinducedresourceallocationschemeforcognitiveradionetworkqualityofservicemaximization AT rjaikumar connectedmapinducedresourceallocationschemeforcognitiveradionetworkqualityofservicemaximization AT stalinallwindevaraj connectedmapinducedresourceallocationschemeforcognitiveradionetworkqualityofservicemaximization AT omprakashkumar connectedmapinducedresourceallocationschemeforcognitiveradionetworkqualityofservicemaximization |