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Home / programming language / Online Java Projects / Delay-Constrained Caching in Cognitive Radio Networks
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Delay-Constrained Caching in Cognitive Radio Networks

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SKU: PROJ7692 Categories: 2017 Projects, Final Year Projects, Mobile Computing Projects, Online Java Projects, programming language Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Delay-Constrained Caching in Cognitive Radio Networks

Abstract— In cognitive radio networks, unlicensed users can use under-utilized licensed spectrum to achieve substantial performance improvement. To avoid interference with licensed users, unlicensed users must vacate the spectrum when it is accessed by licensed (primary) users. Since it takes some time for unlicensed users to switch to other available channels, the ongoing da ta transmissions may have to be interrupted and the transmission delay can be significantly increased. This makes it hard for cognitive radio networks to meet the delay constraints of many applications. In this paper, we develop caching techniques to address this problem. We formulate the cache placement problem in cognitive radio networks as an optimization problem, where the goal is to minimize the total cost, subject to some delay constraint, i.e., the da ta access delay can be statistically bounded. To solve this problem, we propose a cost-based approach to minimize the caching cost, and design a delay-based approach to satisfy the delay constraint. Then, we combine them and propose a distributed hybrid approach to minimiaze the caching cost subject to the delay constraint. Simulation results show that our approaches outperform existing caching solutions in terms of total cost and delay constraint, and the hybrid approach performs the best among the approaches satisfying the delay constraint. < final year projects >

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