Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition Approach
The limited radio spectrum has become a bottleneck for various wireless communications. To better utilize the scare radio spectrum, cognitive radios have recently attracted increasing attention, which makes spectrum sharing more viable. Sharing radio spectrum from primary users to secondary users is...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/262137 |
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| _version_ | 1849699304876277760 |
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| author | Yanmin Zhu Wei Sun Jiadi Yu Tong Liu Bo Li |
| author_facet | Yanmin Zhu Wei Sun Jiadi Yu Tong Liu Bo Li |
| author_sort | Yanmin Zhu |
| collection | DOAJ |
| description | The limited radio spectrum has become a bottleneck for various wireless communications. To better utilize the scare radio spectrum, cognitive radios have recently attracted increasing attention, which makes spectrum sharing more viable. Sharing radio spectrum from primary users to secondary users is of great importance. A licensed primary user (PU) can lease its spectrum to secondary users (SUs) for wireless communications. This paper studies the problem of social welfare maximization of distributed spectrum sharing among a PU and SUs. We first formulate the problem of social welfare maximization which takes into account both the cost of the PU and the utility gained by each SU. The social welfare maximization is a convex optimization problem and thus can be solved by a centralized algorithm. However, the utility function of each SU may contain the private information. To avoid privacy leakage of SUs, we propose an iterative distributed algorithm based on a pricing-based decomposition framework. It is theoretically proved that our algorithm converges to the optimal solution. Simulation results are presented to show that our algorithm achieves the optimal social welfare and converges quickly in a practical setting. |
| format | Article |
| id | doaj-art-2c241e8d0a9e4fbdb7faae57ef820df6 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-2c241e8d0a9e4fbdb7faae57ef820df62025-08-20T03:18:38ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-11-011010.1155/2014/262137262137Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition ApproachYanmin Zhu0Wei Sun1Jiadi Yu2Tong Liu3Bo Li4 Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai 200240, China Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Hong Kong University of Science and Technology, Sai Kung, Hong KongThe limited radio spectrum has become a bottleneck for various wireless communications. To better utilize the scare radio spectrum, cognitive radios have recently attracted increasing attention, which makes spectrum sharing more viable. Sharing radio spectrum from primary users to secondary users is of great importance. A licensed primary user (PU) can lease its spectrum to secondary users (SUs) for wireless communications. This paper studies the problem of social welfare maximization of distributed spectrum sharing among a PU and SUs. We first formulate the problem of social welfare maximization which takes into account both the cost of the PU and the utility gained by each SU. The social welfare maximization is a convex optimization problem and thus can be solved by a centralized algorithm. However, the utility function of each SU may contain the private information. To avoid privacy leakage of SUs, we propose an iterative distributed algorithm based on a pricing-based decomposition framework. It is theoretically proved that our algorithm converges to the optimal solution. Simulation results are presented to show that our algorithm achieves the optimal social welfare and converges quickly in a practical setting.https://doi.org/10.1155/2014/262137 |
| spellingShingle | Yanmin Zhu Wei Sun Jiadi Yu Tong Liu Bo Li Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition Approach International Journal of Distributed Sensor Networks |
| title | Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition Approach |
| title_full | Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition Approach |
| title_fullStr | Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition Approach |
| title_full_unstemmed | Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition Approach |
| title_short | Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition Approach |
| title_sort | distributed spectrum sharing in cognitive radio networks a pricing based decomposition approach |
| url | https://doi.org/10.1155/2014/262137 |
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