Emerging ultra-dense networks (UDN) can increase the network coverage and improve the overall throughput which makes it a promising network technology. However, the massive deployment of low power, small coverage micro base stations makes the traditional cell selection algorithm more complex and resource allocation less efficient. To solve these problems, this paper proposes a joint cell selection and hierarchical resource allocation algorithm. To improve the overall system performance, the proposed cell selection algorithm is executed according to the throughput of users. Meanwhile, a heuristic sub-channel allocation algorithm is proposed to improve the resource utilization. In addition, the different service requirements of mobile devices significantly increase the burden of power consumption. So the power allocation process takes into account the balance between the throughput and power consumption. Simulations demonstrate that the proposed hierarchical algorithm achieves a large performance improvement compared with the other algorithm in system throughput and energy efficiency (EE).


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Hierarchical Resource Allocation in Ultra-Dense Networks


    Contributors:
    Liu, Yuanfei (author) / Wang, Ying (author) / Sun, Ruijin (author) / Huang, Rui (author)


    Publication date :

    2017-09-01


    Size :

    574759 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Resource Allocation for CoMP-NOMA Transmission in Ultra Dense Networks

    Zhao, Shixian / Xie, Jianli / Li, Cuiran | IEEE | 2023



    Test Resource Allocation in Hierarchical Systems Using Bayesian Networks

    Sankararaman, Shankar / McLemore, Kyle / Mahadevan, Sankaran et al. | AIAA | 2013


    Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network

    Cheng, Zhipeng / LiWang, Minghui / Chen, Ning et al. | IEEE | 2020