Throughput Optimization in Cognitive Radio Networks Ensembling Physical Layer Measurement
Wireless networks are developed under the fashion of wider spectrum utilization （e.g., cognitive radio） and multi-hop communication （e.g., wireless mesh networks）. In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes...
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| Published in | Journal of computer science and technology Vol. 30; no. 6; pp. 1290 - 1305 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.11.2015
Springer Nature B.V State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China%Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, U.S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-9000 1860-4749 |
| DOI | 10.1007/s11390-015-1599-x |
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| Abstract | Wireless networks are developed under the fashion of wider spectrum utilization （e.g., cognitive radio） and multi-hop communication （e.g., wireless mesh networks）. In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes the key concern. In this paper, we study the throughput optimization via spectrum allocation in cognitive radio networks （CRNs）. The previous studies incorporate either the conflict graph or SINR model to characterize the interference relationship. However, the former model neglects the accumulative interference effect and leads to unwanted interference and sub-optimal results, while the work based on the latter model neglects its heavy reliance on the accuracy of estimated RSS （receiving signal strength） among all potential links. Both are inadequate to characterize the complex relationship between interference and throughput. To this end, by considering the feature of CRs, like spectrum diversity and non-continuous OFDM, we propose a measurement-assisted SINR-based cross-layer throughput optimization solution. Our work concerns features in different layers： in the physical layer, we present an efficient RSS estimation algorithm to improve the accuracy of the SINR model; in the upper layer, a flow level SINR-based throughput optimization problem for WMNs is modelled as a mixed integer non-linear programming problem which is proved to be NP-hard. To solve this problem, a centralized （1 -ε）-optimal algorithm and an efficient distributed algorithm are provided. To evaluate the algorithm performance, the real-world traces are used to illustrate the effectiveness of our scheme. |
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| AbstractList | Wireless networks are developed under the fashion of wider spectrum utilization （e.g., cognitive radio） and multi-hop communication （e.g., wireless mesh networks）. In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes the key concern. In this paper, we study the throughput optimization via spectrum allocation in cognitive radio networks （CRNs）. The previous studies incorporate either the conflict graph or SINR model to characterize the interference relationship. However, the former model neglects the accumulative interference effect and leads to unwanted interference and sub-optimal results, while the work based on the latter model neglects its heavy reliance on the accuracy of estimated RSS （receiving signal strength） among all potential links. Both are inadequate to characterize the complex relationship between interference and throughput. To this end, by considering the feature of CRs, like spectrum diversity and non-continuous OFDM, we propose a measurement-assisted SINR-based cross-layer throughput optimization solution. Our work concerns features in different layers： in the physical layer, we present an efficient RSS estimation algorithm to improve the accuracy of the SINR model; in the upper layer, a flow level SINR-based throughput optimization problem for WMNs is modelled as a mixed integer non-linear programming problem which is proved to be NP-hard. To solve this problem, a centralized （1 -ε）-optimal algorithm and an efficient distributed algorithm are provided. To evaluate the algorithm performance, the real-world traces are used to illustrate the effectiveness of our scheme. Wireless networks are developed under the fashion of wider spectrum utilization (e.g., cognitive radio) and multi-hop communication (e.g., wireless mesh networks). In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes the key concern. In this paper, we study the throughput optimization via spectrum allocation in cognitive radio networks (CRNs). The previous studies incorporate either the conflict graph or SINR model to characterize the interference relationship. However, the former model neglects the accumulative interference effect and leads to unwanted interference and sub-optimal results, while the work based on the latter model neglects its heavy reliance on the accuracy of estimated RSS (receiving signal strength) among all potential links. Both are inadequate to characterize the complex relationship between interference and throughput. To this end, by considering the feature of CRs, like spectrum diversity and non-continuous OFDM, we propose a measurement-assisted SINR-based cross-layer throughput optimization solution. Our work concerns features in different layers: in the physical layer, we present an efficient RSS estimation algorithm to improve the accuracy of the SINR model; in the upper layer, a flow level SINR-based throughput optimization problem for WMNs is modelled as a mixed integer non-linear programming problem which is proved to be NP-hard. To solve this problem, a centralized (1 − ε)-optimal algorithm and an efficient distributed algorithm are provided. To evaluate the algorithm performance, the real-world traces are used to illustrate the effectiveness of our scheme. Wireless networks are developed under the fashion of wider spectrum utilization (e.g., cognitive radio) and multi-hop communication (e.g., wireless mesh networks). In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes the key concern. In this paper, we study the throughput optimization via spectrum allocation in cognitive radio networks (CRNs). The previous studies incorporate either the conflict graph or SINR model to characterize the interference relationship. However, the former model neglects the accumulative interference effect and leads to unwanted interference and sub-optimal results, while the work based on the latter model neglects its heavy reliance on the accuracy of estimated RSS (receiving signal strength) among all potential links. Both are inadequate to characterize the complex relationship between interference and throughput. To this end, by considering the feature of CRs, like spectrum diversity and non-continuous OFDM, we propose a measurement-assisted SINR-based cross-layer throughput optimization solution. Our work concerns features in different layers: in the physical layer, we present an efficient RSS estimation algorithm to improve the accuracy of the SINR model; in the upper layer, a flow level SINR-based throughput optimization problem for WMNs is modelled as a mixed integer non-linear programming problem which is proved to be NP-hard. To solve this problem, a centralized (1 - epsilon )-optimal algorithm and an efficient distributed algorithm are provided. To evaluate the algorithm performance, the real-world traces are used to illustrate the effectiveness of our scheme. |
| Author | 赵彦超 吴杰 李文中 陆桑璐 |
| AuthorAffiliation | State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, U.S.A. |
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| CitedBy_id | crossref_primary_10_1109_JIOT_2018_2792300 crossref_primary_10_1177_1550147717727713 |
| Cites_doi | 10.1109/JPROC.2009.2015704 10.1109/TIT.2006.871582 10.1016/j.comnet.2004.12.001 10.1109/TMC.2010.204 10.1109/PROC.1980.11899 10.1109/18.825799 10.1109/JSAC.2006.881641 10.1109/MWC.2009.4907554 10.1016/j.comnet.2006.05.001 10.1007/s00365-007-9003-x 10.1137/070697835 10.1007/s11276-011-0367-2 10.1109/TWC.2013.013013.120007 10.1145/1161089.1161119 10.1109/CROWNCOM.2007.4549824 10.1109/INFOCOM.2006.294 10.1007/978-1-4757-4388-3 10.1109/INFCOM.2009.5062048 10.1109/INFCOM.2004.1357030 10.1145/1288107.1288125 10.1109/VTCF.2006.257 10.1016/j.crma.2008.03.014 10.1109/WCNC.2012.6213962 10.1109/DYSPAN.2010.5457857 10.1109/ICPADS.2011.155 10.1145/1080829.1080836 10.1145/1288107.1288122 10.1109/TIT.2006.885507 |
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| Copyright | Springer Science+Business Media New York 2015 Springer Science+Business Media New York 2015. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Notes | Wireless networks are developed under the fashion of wider spectrum utilization （e.g., cognitive radio） and multi-hop communication （e.g., wireless mesh networks）. In these paradigms, how to effectively allocate the spectrum to different transmission links with minimized mutual interference becomes the key concern. In this paper, we study the throughput optimization via spectrum allocation in cognitive radio networks （CRNs）. The previous studies incorporate either the conflict graph or SINR model to characterize the interference relationship. However, the former model neglects the accumulative interference effect and leads to unwanted interference and sub-optimal results, while the work based on the latter model neglects its heavy reliance on the accuracy of estimated RSS （receiving signal strength） among all potential links. Both are inadequate to characterize the complex relationship between interference and throughput. To this end, by considering the feature of CRs, like spectrum diversity and non-continuous OFDM, we propose a measurement-assisted SINR-based cross-layer throughput optimization solution. Our work concerns features in different layers： in the physical layer, we present an efficient RSS estimation algorithm to improve the accuracy of the SINR model; in the upper layer, a flow level SINR-based throughput optimization problem for WMNs is modelled as a mixed integer non-linear programming problem which is proved to be NP-hard. To solve this problem, a centralized （1 -ε）-optimal algorithm and an efficient distributed algorithm are provided. To evaluate the algorithm performance, the real-world traces are used to illustrate the effectiveness of our scheme. cognitive radio network, wireless mesh network, throughput optimization, centralized algorithm, distributedalgorithm, spectrum allocation 11-2296/TP ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| Snippet | Wireless networks are developed under the fashion of wider spectrum utilization （e.g., cognitive radio） and multi-hop communication （e.g., wireless mesh... Wireless networks are developed under the fashion of wider spectrum utilization (e.g., cognitive radio) and multi-hop communication (e.g., wireless mesh... |
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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Cognitive radio Computer Science Data Structures and Information Theory Finite element method Information Systems Applications (incl.Internet) Interference Internet access Linear programming Links Mathematical models Mixed integer Networks Nonlinear programming Optimization Radio networks Regular Paper Signal strength Software Software Engineering Spectrum allocation Theory of Computation Wireless networks 优化组合 吞吐量 无线Mesh网络 无线电网络 测量 物理层 非线性规划问题 频谱利用率 |
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| Title | Throughput Optimization in Cognitive Radio Networks Ensembling Physical Layer Measurement |
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