Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm
In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user colla...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/3748089 |
| _version_ | 1826824210565038080 |
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| author | Tao Zhang |
| author_facet | Tao Zhang |
| author_sort | Tao Zhang |
| collection | DOAJ |
| description | In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at c = 0.3 ∗ 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter α=α∗, the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%. |
| first_indexed | 2024-04-10T20:57:01Z |
| format | Article |
| id | doaj.art-b87da78d6f0b4842bd5e54d285fe256e |
| institution | Directory Open Access Journal |
| issn | 2090-0155 |
| language | English |
| last_indexed | 2025-02-16T07:36:04Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj.art-b87da78d6f0b4842bd5e54d285fe256e2025-02-03T06:04:50ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/3748089Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization AlgorithmTao Zhang0Department of Electronics and Information TechnologyIn recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at c = 0.3 ∗ 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter α=α∗, the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%.http://dx.doi.org/10.1155/2023/3748089 |
| spellingShingle | Tao Zhang Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm Journal of Electrical and Computer Engineering |
| title | Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm |
| title_full | Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm |
| title_fullStr | Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm |
| title_full_unstemmed | Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm |
| title_short | Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm |
| title_sort | collaborative cognitive wireless network optimization model and network parameter optimization algorithm |
| url | http://dx.doi.org/10.1155/2023/3748089 |
| work_keys_str_mv | AT taozhang collaborativecognitivewirelessnetworkoptimizationmodelandnetworkparameteroptimizationalgorithm |