SCIENCE CHINA Information Sciences, Volume 60, Issue 4: 042303(2017) https://doi.org/10.1007/s11432-016-0249-9

Energy-aware deployment of dense heterogeneous cellular networks with QoS constraints

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  • ReceivedJul 15, 2016
  • AcceptedAug 11, 2016
  • PublishedNov 16, 2016


The base station (BS) configuration is a key factor to improve energy efficiency (EE). In this paper, we focus on designing the network deployment parameters (i.e., BS densities) for biased $K$-tier heterogeneous cellular network (HCN) with quality of service (QoS) provisioning. Using appropriate approximations, we derive the closed-form expressions of optimal BS density across all tiers to minimize the area power consumption (APC) by applying the stochastic geometry theory, while satisfying the users' QoS requirements. These results are used to obtain some new insights into the EE performance of biased HCN deployment. With the aid of this approach, the best type of BSs to be deployed or switched off for energy saving purposes can be identified from the perspectives of BS transmission power. More precisely, if the BS transmission power ratio between an arbitrary pair of tiers of $K$-tier HCN, e.g., the small cell BS and macro BS tiers, is higher than a threshold which is a function of path loss exponent, bias factor and power consumption, the small cell BSs are preferred. The opposite situation takes place otherwise. Furthermore, it is also shown that, compared to the unbiased HCN scenario, significant energy savings are possible by appropriately biasing the HCN and optimizing the BS density, subject to the QoS constraints among all tiers.

Funded by

Hong Kong Macao and Taiwan Science and Technology Cooperation Projects(2014DFT10320)

Beijing Nova Program(xx2012037)

National Natural Science Foundation of China(61471058)

International Cooperation NSFC Program(61461136002)



This work was supported by National Natural Science Foundation of China (Grant No. 61471058), Beijing Nova Program (Grant No. xx2012037), International Cooperation NSFC Program (Grant No. 61461136002), and Hong Kong, Macao and Taiwan Science and Technology Cooperation Projects (Grant No. 2014DFT10320).


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