IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i8p1550147720951337.html
   My bibliography  Save this article

Hypergraph-based resource allocation for Device-to-Device underlay H-CRAN network

Author

Listed:
  • Pan Zhao
  • Wenlei Guo
  • Datong Xu
  • Zhiliang Jiang
  • Jie Chai
  • Lijun Sun
  • He Li
  • Weiliang Han

Abstract

In the hybrid communication scenario of the Heterogeneous Cloud Radio Access Network and Device-to-Device in 5G, spectrum efficiency promotion and the interference controlling caused by spectrum reuse are still challenges. In this article, a novel resource management method, consisting of power and channel allocation, is proposed to solve this problem. An optimization model to maximum the system throughput and spectrum efficiency of the system, which is constrained by Signal to Interference plus Noise Ratio requirements of all users in diverse layers, is established. To solve the non-convex mixed integer nonlinear optimization problem, the optimization model is decomposed into two sub-problems, which are all solvable quasi-convex power allocation and non-convex channel allocation. The first step is to solve a power allocation problem based on solid geometric programming with the vertex search method. Then, a channel allocation constructed by three-dimensional hypergraph matching is established, and the best result of this problem is obtained by a heuristic greed algorithm based on the bipartite conflict graph and µ -claw search. Finally, the simulation results show that the proposed scheme improves the throughput performance at least 6% over other algorithms.

Suggested Citation

  • Pan Zhao & Wenlei Guo & Datong Xu & Zhiliang Jiang & Jie Chai & Lijun Sun & He Li & Weiliang Han, 2020. "Hypergraph-based resource allocation for Device-to-Device underlay H-CRAN network," International Journal of Distributed Sensor Networks, , vol. 16(8), pages 15501477209, August.
  • Handle: RePEc:sae:intdis:v:16:y:2020:i:8:p:1550147720951337
    DOI: 10.1177/1550147720951337
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147720951337
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1550147720951337?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:16:y:2020:i:8:p:1550147720951337. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.