# Green Small Cell Operation of Ultra-Dense Networks Using Device Assistance +
## 1 Introduction  
To cope with the mobile traffic explosion, upcoming 5G focuses on the gigabit-scale peak data rate with higher capacity. Deploying hyper-dense small cell networks is a strong candidate to enhance the capacity up to 100-times [1]. While dense small cells can meet the capacity requirement, total power consumption on the network increases in proportion to the number of small cell base stations (BS) [2]. In addition, we should consider that network infrastructures take 3% of the world's electrical energy consumption, and BSs consume about 80% of the energy consumption of mobile network operators (MNOs) [3,4]. In light of these illustrations, it is indispensable to enhance energy efficiency. Thus, many efforts are continued in the standards of 3 GPP and the research projects of EARTH, Green Touch, 5 GrEEn, etc. [5]. In the perspective of 5G, the authors of [6] recently suggested a design framework considering both energy efficiency (EE) and spectral efficiency (SE), called EE-SE co-design. In the literature, many on-going research works investigate green BS operations to enhance energy efficiency. In [7], the authors proposed a BS turning-off algorithm using the optimal cell coverage thatminimizes BS area power consumption. The authors in [8] proposed a cell zooming technique, such that the BS turning-off operation reduces the power consumption, and other turned-on BSs increase their coverage. In [9; 10; 11], the on/off methods based on flow-level dynamics are presented. The authors in [9] solved the problem encompassing dynamic BS operation and the related problem of user association. Related to [9], the authors in [10] presented the distributed threshold-based BS off algorithm based on an overlay network using Delaunay triangulation. The authors in [11] proposed the energy-efficient user association method by using a population game approach. In [12; 13; 14], stochastic geometry is used. The authors in [12] proposed macro BS turning-off strategies in homogeneous and heterogeneous networks and showed that the deployment of small cells can increase energy efficiency, but the energy efficiency gain is saturated when small cell density is increased. In a two-tier heterogeneous network, the authors in [13] proposed the repulsive cell activation scheme that activates small cells according to user density and offloading macro BS traffic. A deployment strategy considering the density and transmission power of BSs is proposed in [14]. The authors in [15] considered a separation architecture substituting the conventional macro BS into a coverage-providing BS and multiple small cells in order to increase energy efficiency. The authors derived the optimal intensity of small cells for the optimal deployment. In [16], the energy-saving problem is investigated while considering multiple sleep base station modes. Furthermore, a survey in [17] presents various existing green cellular techniques to show that energy saving can be achieved by turning on or off BSs. In this paper, we study a small cell BS on/off operation to enhance energy efficiency. Our approach is applicable to various types of small cells, e.g., pico BS, femto BS or even Wi-Fi BS. Hereafter, we use the terminology access point (AP) instead of BS to emphasize the small cell BS. Since we assume that users can directly deploy small cell APs, intractable randomness is engendered. In the densely-deployed small cell networks, the AP on/off operation brings changes to the network environment. While flow-level analysis considers the time-averaged information [9; 10; 11], we mainly focus on the state information of APs because a more frequent and dynamic AP on/off operation is expected. Thus, to know the state, devices' channel measurement is crucial. These assumptions motivate us to take a new approach for the dynamic AP on/off operation using user equipment (UE) feedbacks. Our approach basically exploits belief propagation (BP) to solve an optimization problem on a factor graph. This paper extends the results of its conference version [18].