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Home / programming language / Online Matlab Projects / Dynamic Clustering and ON/ OFF Strategies for Wireless Small Cell Networks
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Dynamic Clustering and ON/ OFF Strategies for Wireless Small Cell Networks

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SKU: PROJ7491 Categories: 2017 Projects, Communications Projects, Final Year Projects, Online Matlab 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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Dynamic Clustering and ON/ OFF Strategies for Wireless Small Cell Networks

Abstract— A novel cluster-based approach for maximizing t he energy efficiency of wireless small cell networks is proposed. A dynamic mechanism is proposed to locally group coupled small cell base stations ( SBSs) into clusters based on location and traffic load. Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost f unction, which captures the tradeoffs between energy efficiency and flow level performance, while satisfying their users’ quality-of-service requirements. Due to the lack of intercluster communications, clusters compete with one another to improve the overall network’s energy efficiency. This intercluster competition is formulated as a n on-cooperative game between clusters that seek to minimize their respective cost functions. To solve t his game, a distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information. It is shown t hat t he proposed algorithm converges to a stationary mixed-strategy distribution, which constitutes an epsilon-coarse correlated equilibrium for the studied game. Simulation results show that the proposed approach yields significant performance gains reaching up to 36% of reduced energy expenditures and upto 41% of reduced fractional transfer time compared to conventional approaches. < final year projects >

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