Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning
In a cognitive wireless mesh network, licensed users (primary users, PUs) may rent surplus spectrum to unlicensed users (secondary users, SUs) for getting some revenue. For such spectrum sharing paradigm, maximizing the revenue is the key objective of the PUs while that of the SUs is to meet their r...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2012/562615 |
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| author | Ayoub Alsarhan Anjali Agarwal |
| author_facet | Ayoub Alsarhan Anjali Agarwal |
| author_sort | Ayoub Alsarhan |
| collection | DOAJ |
| description | In a cognitive wireless mesh network, licensed users (primary users, PUs) may rent surplus spectrum to unlicensed users (secondary users, SUs) for getting some revenue. For such spectrum sharing paradigm, maximizing the revenue is the key objective of the PUs while that of the SUs is to meet their requirements. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. RL is used to extract the optimal control policy that maximizes the PUs’ profit continuously over time. The extracted policy is used by PUs to manage renting the spectrum to SUs and it helps PUs to adapt to the changing network conditions. Performance evaluation of the proposed spectrum trading approach shows that it is able to find the optimal size and price of spectrum for each primary user under different conditions. Moreover, the approach constitutes a framework for studying, synthesizing and optimizing other schemes. Another contribution is proposing a new distributed algorithm to manage spectrum sharing among PUs. In our scheme, PUs exchange channels dynamically based on the availability of neighbor’s idle channels. In our cooperative scheme, the objective of spectrum sharing is to maximize the total revenue and utilize spectrum efficiently. Compared to the poverty-line heuristic that does not consider the availability of unused spectrum, our scheme has the advantage of utilizing spectrum efficiently. |
| format | Article |
| id | doaj-art-afafcdf570d144f68adb4c2ba4ba0e20 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-afafcdf570d144f68adb4c2ba4ba0e202025-02-03T05:45:31ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552012-01-01201210.1155/2012/562615562615Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine LearningAyoub Alsarhan0Anjali Agarwal1Department of Computer Information System, Hashemite University, Zarqa 13115, JordanDepartment of Electrical and Computer Engineering, Concordia University, Montreal, QC, H3G 2W1, CanadaIn a cognitive wireless mesh network, licensed users (primary users, PUs) may rent surplus spectrum to unlicensed users (secondary users, SUs) for getting some revenue. For such spectrum sharing paradigm, maximizing the revenue is the key objective of the PUs while that of the SUs is to meet their requirements. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. RL is used to extract the optimal control policy that maximizes the PUs’ profit continuously over time. The extracted policy is used by PUs to manage renting the spectrum to SUs and it helps PUs to adapt to the changing network conditions. Performance evaluation of the proposed spectrum trading approach shows that it is able to find the optimal size and price of spectrum for each primary user under different conditions. Moreover, the approach constitutes a framework for studying, synthesizing and optimizing other schemes. Another contribution is proposing a new distributed algorithm to manage spectrum sharing among PUs. In our scheme, PUs exchange channels dynamically based on the availability of neighbor’s idle channels. In our cooperative scheme, the objective of spectrum sharing is to maximize the total revenue and utilize spectrum efficiently. Compared to the poverty-line heuristic that does not consider the availability of unused spectrum, our scheme has the advantage of utilizing spectrum efficiently.http://dx.doi.org/10.1155/2012/562615 |
| spellingShingle | Ayoub Alsarhan Anjali Agarwal Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning Journal of Electrical and Computer Engineering |
| title | Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning |
| title_full | Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning |
| title_fullStr | Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning |
| title_full_unstemmed | Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning |
| title_short | Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning |
| title_sort | optimizing spectrum trading in cognitive mesh network using machine learning |
| url | http://dx.doi.org/10.1155/2012/562615 |
| work_keys_str_mv | AT ayoubalsarhan optimizingspectrumtradingincognitivemeshnetworkusingmachinelearning AT anjaliagarwal optimizingspectrumtradingincognitivemeshnetworkusingmachinelearning |