4.7 Article

Green 5G Heterogeneous Networks Through Dynamic Small-Cell Operation

Journal

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 34, Issue 5, Pages 1103-1115

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2016.2520217

Keywords

Green communication; heterogeneous networks (HetNets); small-cell operation; traffic offloading

Funding

  1. SUTD-ZJU Joint Collaboration Grant [SUTD-ZJU/RES/03/2014]
  2. National Basic Research Program of China [2012CB316002]
  3. Natural Science Foundation of China [61201192]
  4. Science Fund for Creative Research Groups of NSFC [61321061]
  5. 863 project [2014AA01A704]
  6. National ST Major Project [2014ZX03003003-002]
  7. Key grant Project of Chinese Ministry of Education [313005]
  8. Open Research Fund of National Mobile Communications Research Laboratory, Southeast University [2012D02]
  9. Tsinghua-Qualcomm Joint Research Program
  10. Tsinghua-Intel International S&T Cooperation Program (ICRI-MNC)

Ask authors/readers for more resources

Traditional macrocell networks are experiencing an upsurge of data traffic, and small-cells are deployed to help offload the traffic from macrocells. Given the massive deployment of small-cells in a macrocell, the aggregate power consumption of small-cells (though being low individually) can be larger than that of the macrocell. Compared to the macrocell base station (MBS) whose power consumption increases significantly with its traffic load, the power consumption of a small-cell base station (SBS) is relatively flat and independent of its load. To reduce the total power consumption of the heterogeneous networks (HetNets), we dynamically change the operating states (on and off) of the SBSs, while keeping the MBS on to avoid any service failure outside active small-cells. First, we consider that the wireless users are uniformly distributed in the network, and propose an optimal location-based operation scheme by gradually turning OFF the SBSs closer to the MBS. We then extend the operation problem to a more general case where users are nonuniformly distributed in the network. Although this problem is NP-hard, we propose a joint location and user density based operation scheme to achieve near-optimum (with less than 1% performance loss in our simulations) in polynomial time.

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