Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation
In this paper, we study the utility fairness resource allocation in a multi-user orthogonal based cognitive radio network with cochannel interference (CCI) mitigation. In our proposed system model, we introduce the correct reception probability (CRP) model as a network utility metric. Furthermore, u...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8375945/ |
| _version_ | 1818644488806989824 |
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| author | Woping Xu Runhe Qiu Julian Cheng |
| author_facet | Woping Xu Runhe Qiu Julian Cheng |
| author_sort | Woping Xu |
| collection | DOAJ |
| description | In this paper, we study the utility fairness resource allocation in a multi-user orthogonal based cognitive radio network with cochannel interference (CCI) mitigation. In our proposed system model, we introduce the correct reception probability (CRP) model as a network utility metric. Furthermore, useful bounds on CRP are derived to analyze the performance of proposed allocation schemes. The optimal resource allocation is formulated as a worst-case user CRP maximum problem with both average CCI and average power budget constraints. However, this problem is non-convex and generally challenging to solve. Therefore, we solve this problem by successively performing subchannel allocation and power allocation. Firstly, a k-means clustering inspired subchannel allocation strategy is proposed to divide secondary users (SUs) into multiple groups by minimizing the average mutual-signal-to-interference-ratio degree between any two SUs. The concept of reference user is employed to guarantee the quality of service of the primary user. In each subchannel, we formulate a max-min utility optimal power allocation problem. The non-linear Perron Frobenius theory is applied to solve this power allocation problem. Simulation results show that the proposed resource allocation scheme is fair and has relatively fast convergence. |
| first_indexed | 2024-12-17T00:15:39Z |
| format | Article |
| id | doaj.art-751bdf5ca5cd4bdfa7aec52448f74a40 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:15:39Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-751bdf5ca5cd4bdfa7aec52448f74a402022-12-21T22:10:42ZengIEEEIEEE Access2169-35362018-01-016374183742910.1109/ACCESS.2018.28454608375945Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference MitigationWoping Xu0https://orcid.org/0000-0001-8503-6165Runhe Qiu1Julian Cheng2College of Information Sciences and Technology, Donghua University, Shanghai, ChinaCollege of Information Sciences and Technology, Donghua University, Shanghai, ChinaSchool of Engineering, The University of British Columbia, Kelowna, CanadaIn this paper, we study the utility fairness resource allocation in a multi-user orthogonal based cognitive radio network with cochannel interference (CCI) mitigation. In our proposed system model, we introduce the correct reception probability (CRP) model as a network utility metric. Furthermore, useful bounds on CRP are derived to analyze the performance of proposed allocation schemes. The optimal resource allocation is formulated as a worst-case user CRP maximum problem with both average CCI and average power budget constraints. However, this problem is non-convex and generally challenging to solve. Therefore, we solve this problem by successively performing subchannel allocation and power allocation. Firstly, a k-means clustering inspired subchannel allocation strategy is proposed to divide secondary users (SUs) into multiple groups by minimizing the average mutual-signal-to-interference-ratio degree between any two SUs. The concept of reference user is employed to guarantee the quality of service of the primary user. In each subchannel, we formulate a max-min utility optimal power allocation problem. The non-linear Perron Frobenius theory is applied to solve this power allocation problem. Simulation results show that the proposed resource allocation scheme is fair and has relatively fast convergence.https://ieeexplore.ieee.org/document/8375945/Multi-user CRNsnonlinear Perron Frobenius theoryresource allocationsubchannel interference mitigation |
| spellingShingle | Woping Xu Runhe Qiu Julian Cheng Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation IEEE Access Multi-user CRNs nonlinear Perron Frobenius theory resource allocation subchannel interference mitigation |
| title | Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation |
| title_full | Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation |
| title_fullStr | Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation |
| title_full_unstemmed | Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation |
| title_short | Fair Optimal Resource Allocation in Cognitive Radio Networks With Co-Channel Interference Mitigation |
| title_sort | fair optimal resource allocation in cognitive radio networks with co channel interference mitigation |
| topic | Multi-user CRNs nonlinear Perron Frobenius theory resource allocation subchannel interference mitigation |
| url | https://ieeexplore.ieee.org/document/8375945/ |
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