Local caching of popular content files at the wireless edge is a promising way to address several challenges for the next mobile radio generation. Apart from reducing latency, it also helps save energy and better control the available end-to-end power budget. In this paper, we take advantage of dense small cell networks' architecture to further reduce the power consumption of the system, using a graph-theoretic approach. The basic idea is to download the requested content file from a neighboring small cell rather than macro cell or backhaul, if it is not cached at the serving base station. More precisely, we build a graph to fetch the requested content file from the nearest less power consuming small cells. Special attention is paid to the adequate number of small base stations involved in the graph to ensure an energy efficient solution. In addition, we apply our graph theory based approach to multicast transmissions and show that our built graph outperforms the conventional unicast/multicast caching transmission in terms of energy efficiency. Simulation results also showed the effectiveness of our approach for different cache size and steepness parameter.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Energy-Efficient Multicast/Unicast Edge Caching for Dense Small Cell Networks with Graph Theory


    Contributors:


    Publication date :

    2018-06-01


    Size :

    393438 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Cooperate Caching with Multicast for Mobile Edge Computing in 5G Networks

    Huang, Xiangyue / Zhao, Zhifeng / Zhang, Honggang | IEEE | 2017



    Fleet tracking system having unicast and multicast functionality

    KOEHRSEN CRAIG L / COLLINS DARRYL V / EVERETT BRYAN J | European Patent Office | 2015

    Free access

    Machine Learning-Driven Optimal Proactive Edge Caching in Wireless Small Cell Networks

    Lin, Pei-Ying / Chiu, Hsiao-Ting / Gau, Rung-Hung | IEEE | 2019