Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization Perspective

The throughput-delay trade-off problem for cooperative spectrum sensing (CSS) is investigated. It is proved that the maximum achievable throughput and the minimum transmission delay cannot be obtained simultaneously. An efficient algorithm is proposed to optimize the sensing bandwidth and the final...

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Main Authors: Hang Hu, Hang Zhang, Hong Yu
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2014/430696
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author Hang Hu
Hang Zhang
Hong Yu
author_facet Hang Hu
Hang Zhang
Hong Yu
author_sort Hang Hu
collection DOAJ
description The throughput-delay trade-off problem for cooperative spectrum sensing (CSS) is investigated. It is proved that the maximum achievable throughput and the minimum transmission delay cannot be obtained simultaneously. An efficient algorithm is proposed to optimize the sensing bandwidth and the final decision threshold jointly such that the throughput is maximized while the delay is constrained. It is demonstrated that convex optimization plays an essential role in solving the problem in an efficient way. Simulation results show that the proposed optimal scheme can significantly improve the throughput of the secondary users (SUs) under the constraint that the delay Quality of Service (QoS) requirements of the SUs are satisfied.
format Article
id doaj-art-5e7efb86bd174fdeaf15b9f69490bcd6
institution Kabale University
issn 1085-3375
1687-0409
language English
publishDate 2014-01-01
publisher Wiley
record_format Article
series Abstract and Applied Analysis
spelling doaj-art-5e7efb86bd174fdeaf15b9f69490bcd62025-08-20T03:34:32ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/430696430696Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization PerspectiveHang Hu0Hang Zhang1Hong Yu2College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, ChinaCollege of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, ChinaCollege of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, ChinaThe throughput-delay trade-off problem for cooperative spectrum sensing (CSS) is investigated. It is proved that the maximum achievable throughput and the minimum transmission delay cannot be obtained simultaneously. An efficient algorithm is proposed to optimize the sensing bandwidth and the final decision threshold jointly such that the throughput is maximized while the delay is constrained. It is demonstrated that convex optimization plays an essential role in solving the problem in an efficient way. Simulation results show that the proposed optimal scheme can significantly improve the throughput of the secondary users (SUs) under the constraint that the delay Quality of Service (QoS) requirements of the SUs are satisfied.http://dx.doi.org/10.1155/2014/430696
spellingShingle Hang Hu
Hang Zhang
Hong Yu
Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization Perspective
Abstract and Applied Analysis
title Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization Perspective
title_full Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization Perspective
title_fullStr Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization Perspective
title_full_unstemmed Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization Perspective
title_short Throughput-Delay Trade-Off for Cognitive Radio Networks: A Convex Optimization Perspective
title_sort throughput delay trade off for cognitive radio networks a convex optimization perspective
url http://dx.doi.org/10.1155/2014/430696
work_keys_str_mv AT hanghu throughputdelaytradeoffforcognitiveradionetworksaconvexoptimizationperspective
AT hangzhang throughputdelaytradeoffforcognitiveradionetworksaconvexoptimizationperspective
AT hongyu throughputdelaytradeoffforcognitiveradionetworksaconvexoptimizationperspective