Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks
In this paper, joint optimization of throughput and error rate via cooperative spectrum sensing in cognitive radio networks is investigated. An optimization problem is formulated, which aims to maximize the average achievable throughput of cooperating cognitive users while keeping the error rate at...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/97374 http://hdl.handle.net/10220/13149 |
| _version_ | 1811676477320593408 |
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| author | Zhang, Wenjie Yeo, Chai Kiat |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Zhang, Wenjie Yeo, Chai Kiat |
| author_sort | Zhang, Wenjie |
| collection | NTU |
| description | In this paper, joint optimization of throughput and error rate via cooperative spectrum sensing in cognitive radio networks is investigated. An optimization problem is formulated, which aims to maximize the average achievable throughput of cooperating cognitive users while keeping the error rate at a lower level. This is a multi-variable nonconvex optimization problem. Instead of solving it directly, we propose an iterative algorithm which jointly optimizes the threshold and sensing time together to decrease the effect of the error and to increase the achievable throughput. We first prove that the local error rate of the cognitive user is a convex function of energy threshold and determine a closed-form for the optimal threshold which minimizes the error rate. Then we show that the AND rule is the optimal fusion rule to maximize the achievable throughput. Furthermore we determine the least number of cooperating cognitive users that can guarantee a minimum target error rate. This initial nonconvex problem is converted into a single variable convex optimization problem which can be successfully solved by common methods e.g. Newton’s method. Simulation results illustrate the fast convergence and effectiveness of the joint iterative algorithm. |
| first_indexed | 2024-10-01T02:22:06Z |
| format | Journal Article |
| id | ntu-10356/97374 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:22:06Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/973742020-05-28T07:18:03Z Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks Zhang, Wenjie Yeo, Chai Kiat School of Computer Engineering DRNTU::Engineering::Computer science and engineering In this paper, joint optimization of throughput and error rate via cooperative spectrum sensing in cognitive radio networks is investigated. An optimization problem is formulated, which aims to maximize the average achievable throughput of cooperating cognitive users while keeping the error rate at a lower level. This is a multi-variable nonconvex optimization problem. Instead of solving it directly, we propose an iterative algorithm which jointly optimizes the threshold and sensing time together to decrease the effect of the error and to increase the achievable throughput. We first prove that the local error rate of the cognitive user is a convex function of energy threshold and determine a closed-form for the optimal threshold which minimizes the error rate. Then we show that the AND rule is the optimal fusion rule to maximize the achievable throughput. Furthermore we determine the least number of cooperating cognitive users that can guarantee a minimum target error rate. This initial nonconvex problem is converted into a single variable convex optimization problem which can be successfully solved by common methods e.g. Newton’s method. Simulation results illustrate the fast convergence and effectiveness of the joint iterative algorithm. 2013-08-16T03:44:32Z 2019-12-06T19:41:58Z 2013-08-16T03:44:32Z 2019-12-06T19:41:58Z 2012 2012 Journal Article Zhang, W.,& Yeo, C. K. (2012). Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks. Computer Communications, 36(1), 80-89. 0140-3664 https://hdl.handle.net/10356/97374 http://hdl.handle.net/10220/13149 10.1016/j.comcom.2012.07.015 en Computer communications |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Zhang, Wenjie Yeo, Chai Kiat Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks |
| title | Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks |
| title_full | Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks |
| title_fullStr | Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks |
| title_full_unstemmed | Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks |
| title_short | Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks |
| title_sort | joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/97374 http://hdl.handle.net/10220/13149 |
| work_keys_str_mv | AT zhangwenjie jointiterativealgorithmforoptimalcooperativespectrumsensingincognitiveradionetworks AT yeochaikiat jointiterativealgorithmforoptimalcooperativespectrumsensingincognitiveradionetworks |