IoT as a Service. 4th EAI International Conference, IoTaaS 2018, Xi’an, China, November 17–18, 2018, Proceedings

Research Article

Learning-to-Rank Based Strategy for Caching in Wireless Small Cell Networks

Download
168 downloads
  • @INPROCEEDINGS{10.1007/978-3-030-14657-3_12,
        author={Chenxi Zhang and Pinyi Ren and Qinghe Du},
        title={Learning-to-Rank Based Strategy for Caching in Wireless Small Cell Networks},
        proceedings={IoT as a Service. 4th EAI International Conference, IoTaaS 2018, Xi’an, China, November 17--18, 2018, Proceedings},
        proceedings_a={IOTAAS},
        year={2019},
        month={3},
        keywords={Wireless networks Caching Learning-to-rank},
        doi={10.1007/978-3-030-14657-3_12}
    }
    
  • Chenxi Zhang
    Pinyi Ren
    Qinghe Du
    Year: 2019
    Learning-to-Rank Based Strategy for Caching in Wireless Small Cell Networks
    IOTAAS
    Springer
    DOI: 10.1007/978-3-030-14657-3_12
Chenxi Zhang,*, Pinyi Ren,*, Qinghe Du,*
    *Contact email: zcx110708@hotmail.com, pyren@mail.xjtu.edu.cn, duqinghe@mail.xjtu.edu.cn

    Abstract

    Caching in wireless network is an effective method to reduce the load of backhaul link. In this paper, we studied the problem of wireless small cell network caching when the content popularity is unknown. We consider the wireless small cell network caching problem as a ranking problem and propose a learning-to-rank based caching strategy. In this strategy, we use the historical request records to learn the rank of content popularity and decide what to cache. First, we use historical request records to cluster the small base stations (SBS) through the k-means algorithm. Then the loss function is set up in each cluster, the gradient descent algorithm is used to minimize the loss function. Finally we can get the ranking order of the content popularity for each SBS, and the files are cached to the SBS in sequence according to the order. From Simulation results we can see that our strategy can effectively learn the ranking of content popularity, and obtain higher cache hit rate compared to the reference strategies.