Wireless Communications and Mobile Computing

Volume 2017, Article ID 6519709, 11 pages

https://doi.org/10.1155/2017/6519709

## Nonoverlay Heterogeneous Network Planning for Energy Efficiency

Electrical and Electronics Engineering, Bahçeşehir University, Istanbul, Turkey

Correspondence should be addressed to Alkan Soysal; rt.ude.uab.gne@lasyos.nakla

Received 27 July 2016; Revised 27 December 2016; Accepted 22 January 2017; Published 9 February 2017

Academic Editor: Simone Morosi

Copyright © 2017 Mahmut Demirtaş and Alkan Soysal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In this paper, we introduce nonoverlay microcell/macrocell planning that is optimally designed for improving energy efficiency of the overall heterogeneous cellular network. We consider two deployment strategies. The first one is based on a fixed hexagonal grid and the second one is based on a stochastic geometry. In both of our models, microcells are placed in those areas where the received signal power levels of macrocell common pilot channels are below a certain threshold. Thus, interference between microcells and macrocells is minimized. As a result, addition of microcells increases the achieved number of bits per unit energy. Under such deployment assumptions, we investigate the effects of certain parameters on the energy efficiency. These parameters include the user traffic, the Intersite Distance (ISD), the size of microcells and the number of microcells per macrocell for the grid model, and macrocell density and microcell density for the stochastic model. The results of our performance analyses show that utilizing microcells in a sparse user scenario is worse for the energy efficiency whereas it significantly improves both energy and spectral efficiencies in a dense user scenario. Another interesting observation is that it is possible to choose an optimum number of microcells for a given macrocell density.

#### 1. Introduction

For a long period of time, the main concern of cellular systems was to increase the spectral efficiency. Among several others, one way to increase the spectral efficiency is to overlay microcells on the existing macrocell coverage area. This two-tier approach guarantees the coverage and increases the spectral efficiency of the users that are close to microcell stations. However, the downside of this approach is that the energy efficiency of the network gets worse by the addition of new overlaid microcells.

Over the last couple of years, the energy efficiency of cellular networks has been an increasing concern because of its environmental and operational cost effects. In order to improve the energy efficiency, several solutions are proposed in the literature. Detailed approach to general energy efficiency problem can be found in [1–5] and the references therein.

The performance of an energy efficiency analysis depends strongly on the definition of energy efficiency metric [6]. Area power consumption and bit per joule are the two most common energy efficiency metrics that are considered in the literature. Similar to [7–10], we considered area power consumption metric in some previous studies [11, 12]. However, other studies report that bit per joule metric captures the energy efficiency in high-load conditions better than area power consumption metric [2]. In the literature, there are several works where the authors employed bit per joule as their efficiency metric [13–17]. We also use bit per joule metric in this study, since our focus is to obtain energy efficient methods for increasingly high demands of spectral efficiency.

Another important concept in the energy efficiency analysis of heterogeneous cellular networks is the base station deployment model. In the literature, generally, two different models are used to determine the locations of base stations: fixed hexagonal grid model and Poisson Point Process (PPP) based stochastic geometry model. Although stochastic geometry models are better fit to real base station deployments, fixed hexagonal grid models can provide a better insight to the mathematical problem.

In [7–12], fixed hexagonal grid model is employed and area power consumption metric is used for energy efficiency comparison. The authors in [7, 8] find the optimum ISD between macrocells when several microcells are overlaid on the macrocell coverage area. However, in such a scenario, microcell addition always increases the total power consumption of the system because of the overlay structure. In [9], overlaid microcells or reduced range omnidirectional macrocells are turned on and off depending on a traffic model. The authors find the optimum ISD for a range of path loss exponent values. In [10], microcell base station planning is considered; however the optimization problem is to minimize the number of microcell base stations over a set of candidate microcell base station positions and a traffic constraint. Therefore, improving energy efficiency is not the main goal in [10]. In [11, 12], we found optimum ISD in a nonoverlay microcell deployment.

There are also some studies that consider bit per joule metric for energy efficiency analysis. In [13, 15], the authors consider a single cell OFDMA network and investigate the trade-off between spectral efficiency and bit per joule energy efficiency. In [16, 17], the authors extend their results to a case where there is a single macrocell base station with several uniformly overlaid small cells and to multiple input multiple output (MIMO) broadcast channels, respectively. In a multicell scenario and for a fixed grid, [14] assumes a homogeneous network deployment with microcell or picocell base stations and calculates the effect of backhaul power consumption on the bit per joule energy efficiency. In [18], we considered a heterogeneous network with a fixed hexagonal grid and showed that bit per joule energy efficiency increases with increasing number of microcells.

Stochastic geometry based models are considered in [19–22]. In [19], macrocells are located according to a PPP in a Euclidean plane. The authors consider only homogeneous macrocell networks, and their goal is to find tractable coverage and rate expressions. These mathematical analyses of coverage and average rates are extended to heterogeneous networks in [20]. The model in [21] assumes a single macrocell and several small cells that are distributed according to a PPP. The authors find the optimal density of small cells that maximize energy efficiency. Similar results for multicell overlaid heterogeneous networks are derived in [22], where energy efficiency metric is area power consumption.

In this paper, we improve the energy efficiency through a nonoverlay planning of heterogeneous networks. We deploy microcells at the locations where the received signal strength is expected to be relatively low. In the fixed grid model, the microcell locations are chosen to be the cell edges of the hexagonal cell site. In the stochastic geometry model, we employ a two-stage deployment. In the first stage, macrocells are placed according to a PPP and the coverage regions are determined. In the second stage, we detect the regions where the received signal strength is lower than a certain limit. Then, we place the microcells on those regions according to a separate PPP and update the coverage regions.

Due to the nonoverlay nature of our deployment, a macrocell base station saves power when microcells are deployed in a site. Using this model, we calculate the energy efficiency as a function of ISD (or macrocell density), the number of microcells (or microcell density), and the size of microcells. For power consumption modeling, we use comprehensive power consumption models that are introduced in [23]. We consider bit per joule as our energy efficiency metric. Through our simulations, we observe that deploying microcells simultaneously increases both energy efficiency and spectral efficiency. Also, we conclude that it is possible to choose intervals for ISD (or macrocell density) and number of microcells (or microcell density) that improves the energy efficiency the most.

#### 2. System Model

In this paper, our goal is to improve the energy efficiency of a nonoverlay heterogeneous cellular network without compromising the spectral efficiency. Here, “heterogeneous cellular network” refers to a single technology network that contains different sizes of base stations. We assume a fixed coverage constraint that guarantees that a certain minimum percentage of a service area is covered. In addition, we assume that all base stations work under full-load condition.

In order to investigate the heterogeneous networks in terms of energy and spectral efficiency, we use two different models for base station deployment: a fixed hexagonal grid model and a stochastic geometry based model. In the first model, we consider a hexagonal grid of macrocells where each macrocell receives interference from a tier of neighboring macrocells. Due to the nonoverlay nature of microcell deployment and in order to save power, the radius of a macrocell might get smaller as the number of microcells increases. This can be observed in Figures 1(a), 1(c), and 1(e), where the coverage constraint is 100% for all subfigures. ISD determines the hexagonal cell size, and coverage area determines the macrocell radius, (see Figure 1(a)). Microcells are deployed along the edges of the hexagons (see Figures 1(c) and 1(e)). Our goal is to analyze the energy efficiency of such a deployment over certain parameters like ISD, number of microcells, and microcell radius. We also consider different user densities in order to observe the effect of microcell utilization. In the sparse scenario, we have 5 users/km^{2}, whereas in the dense scenario, we have 100 users/km^{2}.