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Dynamic Resource Allocation for Heterogeneous Services in Cognitive Radio Networks With Imperfect Channel Sensing
Resources in cognitive radio networks (CRNs) should dynamically be allocated according to the sensed radio environment. Although some work has been done for dynamic resource allocation in CRNs, many works assume that the radio environment can perfectly be sensed. However, in practice, it is difficul...
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Published in: | IEEE transactions on vehicular technology 2012-02, Vol.61 (2), p.770-780 |
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container_end_page | 780 |
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container_title | IEEE transactions on vehicular technology |
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creator | Renchao Xie Yu, F. Richard Hong Ji |
description | Resources in cognitive radio networks (CRNs) should dynamically be allocated according to the sensed radio environment. Although some work has been done for dynamic resource allocation in CRNs, many works assume that the radio environment can perfectly be sensed. However, in practice, it is difficult for the secondary network to have the perfect knowledge of a dynamic radio environment in CRNs. In this paper, we study the dynamic resource allocation problem for heterogeneous services in CRNs with imperfect channel sensing. We formulate the power and channel allocation problem as a mixed-integer programming problem under constraints. The computational complexity is enormous to solve the problem. To reduce the computational complexity, we tackle this problem in two steps. First, we solve the optimal power allocation problem using the Lagrangian dual method under the assumption of known channel allocation. Next, we solve the joint power and channel allocation problem using the discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution. Another advantage of this method is that it can track the changing radio environment to dynamically allocate the resources. Simulation results are presented to demonstrate the effectiveness of the proposed scheme. |
doi_str_mv | 10.1109/TVT.2011.2181966 |
format | article |
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First, we solve the optimal power allocation problem using the Lagrangian dual method under the assumption of known channel allocation. Next, we solve the joint power and channel allocation problem using the discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution. Another advantage of this method is that it can track the changing radio environment to dynamically allocate the resources. 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To reduce the computational complexity, we tackle this problem in two steps. First, we solve the optimal power allocation problem using the Lagrangian dual method under the assumption of known channel allocation. Next, we solve the joint power and channel allocation problem using the discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution. Another advantage of this method is that it can track the changing radio environment to dynamically allocate the resources. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.</description><subject>Applied sciences</subject><subject>Base stations</subject><subject>Channel allocation</subject><subject>Channel estimation</subject><subject>Cognitive radio</subject><subject>Complexity theory</subject><subject>discrete stochastic optimization</subject><subject>Exact sciences and technology</subject><subject>heterogeneous services</subject><subject>imperfect channel sensing</subject><subject>Joints</subject><subject>mixed-integer programming</subject><subject>Radiocommunication specific techniques</subject><subject>Radiocommunications</subject><subject>Resource management</subject><subject>Sensors</subject><subject>Telecommunications</subject><subject>Telecommunications and information theory</subject><issn>0018-9545</issn><issn>1939-9359</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNo9kE1LAzEQhoMoWKt3wUsuHrdmkmy2Ocr6UUEUatXjks3O1ug2qcmq9N-7UulpGGae94WHkFNgEwCmLxYviwlnABMOU9BK7ZERaKEzLXK9T0aMwTTTucwPyVFK78MqpYYR-bzaeLNyls4xha9okV52XbCmd8HTNkQ6wx5jWKLH8JXoE8ZvZzFR52kZlt717hvp3DQu0Afsf0L8SPTV9W_0brXG2KLtaflmvMduYH1yfnlMDlrTJTz5n2PyfHO9KGfZ_ePtXXl5n1muRZ_V0NYMsGkKZRljqI1qsAAxnUreNrYupFQohQYGIhfatEYappRqCs5rEFqMCdvm2hhSithW6-hWJm4qYNWfsmpQVv0pq_6VDcj5FlmbZE3XRuOtSzuO57nK2dA2JmfbP4eIu7MC4AWX4hdk_XYT</recordid><startdate>20120201</startdate><enddate>20120201</enddate><creator>Renchao Xie</creator><creator>Yu, F. 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In this paper, we study the dynamic resource allocation problem for heterogeneous services in CRNs with imperfect channel sensing. We formulate the power and channel allocation problem as a mixed-integer programming problem under constraints. The computational complexity is enormous to solve the problem. To reduce the computational complexity, we tackle this problem in two steps. First, we solve the optimal power allocation problem using the Lagrangian dual method under the assumption of known channel allocation. Next, we solve the joint power and channel allocation problem using the discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution. Another advantage of this method is that it can track the changing radio environment to dynamically allocate the resources. 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subjects | Applied sciences Base stations Channel allocation Channel estimation Cognitive radio Complexity theory discrete stochastic optimization Exact sciences and technology heterogeneous services imperfect channel sensing Joints mixed-integer programming Radiocommunication specific techniques Radiocommunications Resource management Sensors Telecommunications Telecommunications and information theory |
title | Dynamic Resource Allocation for Heterogeneous Services in Cognitive Radio Networks With Imperfect Channel Sensing |
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