Mobile Information Systems

Volume 2017 (2017), Article ID 9242058, 17 pages

https://doi.org/10.1155/2017/9242058

## A Planning and Optimization Framework for Ultra Dense Cellular Deployments

^{1}Department of Communications and Networks, Aalto University, Espoo, Finland^{2}Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia^{3}College of Electrical Engineering, Universidad Tecnológica de Panamá, Panamá, Panama

Correspondence should be addressed to Edward Mutafungwa

Received 2 November 2016; Revised 19 January 2017; Accepted 12 February 2017; Published 8 March 2017

Academic Editor: Massimo Condoluci

Copyright © 2017 David González González et al. 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

To accommodate the ever-expanding wireless data traffic volumes, mobile network operators are complementing their macrocellular networks by deploying low-power base stations (or small cells) to offload traffic from congested macrocells and to reuse spectrum. To that end, Ultra Dense Network (UDN) deployments provide means to aggressively reuse spectrum, thus providing significant enhancements in terms of system capacity. However, these deployments entail several challenges, including the increased complexity in network planning and optimization. In this paper, we propose a versatile optimization framework for planning UDN deployments. The planning and optimization framework is underpinned by metrics that consider scalability in terms of number of users, cost of densification, and fairness. The proposed methodology is evaluated using a real-world UDN planning case. The numerical results expose a number of interesting insights, including the impact of different bandwidth allocation strategies and spatial service demand distribution on the performance of various network topologies. Specifically, we provide a performance comparison of the optimized UDN topologies versus random (unplanned), regular grid, and heuristically derived UDN topologies. This comparison further underlines the need for flexible network planning and optimization frameworks as different operator performance metrics of interest may require different radio access networks configurations.

#### 1. Introduction

Mobile network operators face the continuous challenge of upgrading their networks which are rapidly expanding traffic volumes. This trend is mostly attributed to the increased adoption of smart devices (e.g., smartphones). Recent projections for global mobile traffic growth anticipate a tenfold increase in average monthly data consumption from the current 2–5 GB/month to 20–50 GB/month by 2020 [1]. Moreover, the average year-on-year subscriber growths of 5%–15% are expected to continue well into the next decade, notably with most of this demand from* emerging* markets [1, 2]. At the same time, user expectation on service quality also continues to increase, with high-speed connectivity becoming the baseline requirement for most users, regardless of their location or network load conditions.

To accommodate those projected traffic growths and meet user needs, mobile network operators are densifying their networks through heterogeneous deployment of low-power base stations (BSs) or* small cells* (typically less than 10 W transmit power) to complement the existing high-power (20 W or higher) macrocells (umbrella coverage) [3–6]. Nowadays, small cell is a term to refer to compact low-power BSs (e.g., microcells, picocells, or femtocells) and other macrocellular network extensions (e.g., relays, remote radio heads) that are deployed to enhance coverage and capacity in homes, enterprise environments, underserved areas, and other indoor and outdoor traffic hotspots [6]. This exponential data traffic growth is already setting an imperative requirement for* Ultra Dense Networks* (UDNs), identified as a key enabler for the 5th generation (5G) [1, 5, 7–9]. Indeed, 5G targets the operation in higher frequencies together with smaller cell sizes to achieve the envisioned extreme Mobile Broadband (xMBB) [1, 7]. To that end, UDNs are characterized by small cell deployments with intersite distances (ISD) of a few tens of meters for outdoor deployments (even shorter distances for indoor) and site density exceeding 100 sites/km^{2} in dense urban scenarios. This is in contrast to legacy 4th generation (4G) heterogeneous network deployments with typical site densities of less than 10 sites/km^{2} and ISD of a few hundred meters [1, 5, 7]. There is currently no commonly accepted definition on what network deployment constitutes a UDN. The definitions provided in different scientific literature have typically attempted to define UDNs in terms of cell density or the cell density relative to active user density (see [10] and references quoted therein). Other UDN definitions promulgated by industry include that of UDN being network with a small cell deployed outdoor on every lamp post or indoor with spacing of less than 10 m [1]. In this study, we adopt the pragmatic viewpoint from [1], whereby UDNs are considered to be an evolution from legacy dense networks and small cell network deployments with ISD of less than 100 m and are projected to become more prominent after year 2020.

However, UDNs are creating new and significant challenges for mobile network operators, with network planning and optimization notably becoming increasingly complex with denser network deployments [1, 7, 8]. In this context, planning refers to the process of determining the number, location, and configuration of base stations (e.g., small cells) to provide wireless access to users (and things) guaranteeing a certain targeted Quality of Service (QoS). In this process, dimensioning is the initial step used to solve the problem of estimating the required number of base stations needed to meet the capacity needs of a given service demand volume [11]. Thereafter, more precise network planning is carried out to evaluate cell site locations and initial cell parameters, and eventually optimization procedures in* live* networks are also used to continuously adjust cell’s parameters to further optimize both coverage and capacity. However, in practical scenarios, dimensioning and site positioning are nontrivial problems because the services are heterogeneous, that is, different QoS requirements, and the spatiotemporal distribution of the service demand is both nonuniform and dynamic. Furthermore, the challenges of site acquisition naturally scale with increased densification, thus obliging operators to consider leveraging base stations sites (mostly small cells) available in unplanned (suboptimal) locations [1, 4, 12]. Additionally, the ongoing evolution of radio access technologies, together with the new radio access concepts and paradigms expected for 5G, is blurring the traditional boundary between planning and optimization tasks. Indeed, as per discussion presented in [11], planning and optimization are iterative tasks that should be increasingly intertwined. These thoughts are echoed by the authors of [13], who also highlight the need for a rethink of planning and optimization in the context of dense heterogeneous networks, emphasizing that effective planning attains an even distribution of the load among cells, a goal that in the opinion of the authors of [11] (and corroborated by the authors of this paper) is a valid way to enhance system performance.

This paper addresses the aforementioned challenges by proposing an optimization framework for planning of UDN, which is suitable for real-world deployments. The corresponding research problem can be stated as follows.

*Research Problem*. Determine the set of network topologies with a certain number of access points (within an interval of interest, i.e., minimum and maximum node density) that is best compatible with a given spatial distribution of the service demand distribution (in statistical terms) and a certain performance metric.

Thus, the contribution of this paper, associated with the previous research problem, can be summarized as follows.

*Main Contribution*. A single- and multiobjective optimization framework for planning of UDN deployments: The optimization allows obtaining network topologies which can be optimized for any arbitrary spatial traffic distribution (STD) (hereafter, the terms “spatial traffic distribution” and “spatial service demand distribution” are used interchangeably) and performance metrics, such as spectral efficiency or cell-edge performance.

*Additional Contributions*. In addition, several other minor contributions include:(1)The comparative analysis of several bandwidth allocation policies in the context of network planning.(2)A simple heuristic for planning of UDNs.

The numerical results from a real-world planning case (evaluated under a variety of conditions) reveal a number of interesting insights:(i)Bandwidth allocation strategies can facilitate the identification of optimized UDN topologies that may enable an operator to flexibly prioritize either system capacity or cell-edge performance.(ii)The results from the benchmarking clearly indicate that, in case of nonuniform STD, optimization is mandatory as the performance of regular and user-deployed (random) topologies is poor, while quasi-optimal performances accompanied with significant gains can be attained through the use of heuristic planning and optimization.

The rest of the paper is organized as follows: the next section presents the system model. The performance metrics and proposed optimization formulations are introduced in Section 3. In Section 4, a background and description of the planning case study are presented together with the description of the spatial service demand distributions, benchmarks, and parameters and assumptions used in numerical evaluations. Section 5 provides a concise analysis of the numerical results. Finally, the concluding discussions and overview of potential research directions are provided in Section 6.

#### 2. System Model

As indicated previously, the goal is to plan an ultra dense cellular network composed of low-power BSs for a target service area . The service area is divided into small area elements or pixels (in this paper, the terms “area elements” and “pixels” will be used interchangeably) in which the average received power can be assumed to be constant.

In this study, the downlink of an Orthogonal Frequency Division Multiple Access- (OFDMA-) based cellular network with system bandwidth is considered. To carry out the planning, it is assumed that a set of candidate locations have been previously defined in the target service area. In each of these locations, a BS could be placed, and a maximum transmit power is assumed.

The radio propagation, that is, the network geometry, is captured by the matrix that indicates the average channel gain between each BS and area element. The vectors and , both , correspond to the transmit power of each BS in* Reference Signals* (RS) and data channels, respectively. The average RS received power can be calculated by means of the following expression:The operator denotes Hadamard (pointwise) operations. The binary vector indicates the allocation of a BS in the candidate locations, and hence, is referred to as “*network topology*” as it determines the number and location of BSs. Therefore, is the planning (optimization) variable. Hereafter, all the dependencies on are omitted for the sake of clarity. For instance, in (1). gives the average RS received power in the th pixel from the th BS.

Cell selection, the association of each pixel to a serving BS, is based on the average RS received power. Therefore, the th pixel (the th row in ) is served by cell ifThe coverage pattern associated with each network topology is represented by the binary coverage matrices and , both in . If the th area element is served by , then . is the binary complement of . It is assumed that each area element is either served by one cell or out-of-coverage. It is considered that the th area element is out-of-coverage if at least one of the following three conditions is not fulfilled: (i)The RS received power is larger than a minimum value: .(ii)The Signal to Interference plus Noise Ratio (SINR) is larger than a threshold: .(iii)The average channel gain between the area element and its serving BS is larger than .

The* outage* associated with a network topology is captured by the vector . If the th area element is out-of-coverage, then , and 0 otherwise.

A certain knowledge of the spatial distribution of the service demand is assumed. In practice, this is known by operators in statistical terms [14]. This information is stored in the vector . can be regarded as a Probability Density Function (PDF) in two dimensions, and hence, it indicates the probability, in the event of a new user, that the th pixel has the user on it. Thus, .

In this work, full load is assumed to model the intercell interference, which is a reasonable assumption for planning purposes. Other models, such as load-coupling [15], can be easily incorporated in the model, if needed. Thus, the vector representing the average SINR at each area element is given bywhere is the noise power. The operators and denote Hadamard (pointwise) operations. It is customary to define link performance as a nondecreasing function of the SINR. In this work, Shannon’s bound is considered, and hence, the resulting spectral efficiency is stored in the vector , and its elements are calculated as follows:In (4), the idea is to discard the contribution of the pixels that are out-of-coverage (by means of ), thus penalizing network topologies with significant coverage holes in the optimization procedure.

The list of symbols is provided in* Basic Notation* in Notation for convenience.

#### 3. Performance Metrics and Optimization

##### 3.1. Performance Metrics for Radio Access Network Planning

Generally speaking, planning is about determining the number and location of BSs in the service area. Evidently, the network deployment should be done such that the maximum benefit is obtained; that is, network capacity is maximized (more users) with minimal cost (less infrastructure deployment), while guaranteeing a certain level of coverage, QoS, and fairness. In order to address this problem by means of optimization, several metrics (and constraints) are required. In this work, the following objectives are considered.

*(i) Number of BSs (f*_{1}*)*. In principle, the deployment should be done with the minimum possible number of BSs to minimize both the Capital Expenditure (CAPEX) and the energy consumption that is part of the Operational Expenditure (OPEX).

*(ii) Network Capacity (f*_{2}*)*. This metric captures the average aggregate rate the network is able to deliver. Thus, represents a system-oriented performance indicator.

*(iii) Cell-Edge Performance (f*_{3}*)*. This metric captures the performance in* the weakest* zones of the service area. Thus, is a user-oriented performance indicator and promotes fairness.

The definition of the previous metrics is given next. The number of BSs () in a network topology is simply the number of ‘1’s in the corresponding , and hence,

From a planning point of view, it is important to consider the spatial distribution of the service demand. In other words, the planning should favor network topologies that provides more capacity to the zones of the service area where the traffic is more likely to appear. Given that this information is contained in the vector , it can be used to weight the different pixels according to* their importance*; that is, pixels with more traffic are more important. Thus, the weighted spectral efficiency vector is defined as follows: . Note that, indeed, the scalar represents the expected spectral efficiency at area element level because is a PDF.

In cellular networks, a very important aspect is the frequency reuse; that is, the system bandwidth can be reutilized at each cell. This is the most distinctive aspect of cellular networks that allows these systems to provide radio access to a large amount of users (and things). The way in which the bandwidth is allocated to the users largely determines the resulting system capacity and/or users’ satisfaction. In this sense, cell-edge performance [16] is a well-known, yet important, problem in OFDMA-based cellular networks that can affect (negatively) user’s experience. Broadly speaking, users at cell-edges are relatively more expensive in terms of radio resources, as their SINR is typically very low. In this work, this aspect is considered from the planning point of view, and consequently, two different bandwidth allocation strategies are considered and integrated in the performance metrics.

*(**1) Uniform Bandwidth Allocation (UBA)*. The objective is to evaluate the resulting aggregate capacity assuming that the bandwidth of each cell is equally distributed over its coverage area (pixels).

*(**2) Proportional Bandwidth Allocation (PBA)*. The objective is to evaluate the resulting aggregate capacity assuming that the bandwidth of each cell is distributed over its coverage area (pixels) proportionally to the SINR or, equivalently, the spectral efficiency of the pixels.

The vectors and , both in , indicate the bandwidth that would be allocated to each area element under the uniform and proportional bandwidth allocation, respectively. They are defined as follows:where the vector contains the inverse of the number of pixels associated with each BS. The proportional allocation is as follows:Equation (7) divides the bandwidth of each cell proportionally to the spectral efficiency of the area elements in the cell.

Thus, the network capacity metric is defined as follows: Hereafter, superscripts “” and “” are used to indicate UBA and PBA, respectively, as follows: and .

Cell-edge performance is defined, for planning purposes herein, as the aggregate rate of the worst of the service area. Given the vectors and , where the sorting is in ascending order, then the metric representing the cell-edge performance would be given by where . Since and can be evaluated for both UBA and PBA, then this study is able to utilize four possible objective functions for comparison purposes, namely, , , , and .

##### 3.2. Optimization Problem Formulation

In this work, two different optimization formulations are considered. They can be used depending on the network planning strategy of the operator. On the one hand, if the network operator’s target is to maximize the network aggregate capacity (), a multiobjective problem is proposed as this metric is in conflict with ; that is, generally speaking, the denser the network, the higher the capacity due to the more aggressive frequency reuse. On the other hand, if the operator’s target is to provide a more homogeneous coverage, that is, less variability at pixel level, a single-objective problem is proposed with as objective function, and the required number of cells as an input.

###### 3.2.1. Multiobjective Optimization

Multiobjective optimization [17] can be used when multiple conflicting objectives need to be simultaneously optimized (a brief introduction to multiobjective and evolutionary optimization is presented in Appendix A). This is the case of and in the planning framework presented herein. Thus, in order to obtain network topologies featuring the best trade-off between the number of BSs () and network capacity (), the following multiobjective optimization problem is proposed:

In problem (10a), (10b), (10c), and (10d), constraint (10b) guarantees that a minimum fraction () of the area elements has coverage. Constraint (10c) defines the* search space*, that is, the domain of the variable . In practice, and due to the nature of the environments in which UDNs are envisioned to be deployed, network operators usually have an estimate of the number of BSs that is required/feasible, and hence, the optimization can be further localized. This is accomplished by means of constraint (10d), where these limits are set.

Problem (10a), (10b), (10c), and (10d) is a combinatorial problem belonging to the class NP-complete. The search space defined by the optimization variable (the total number of network topologies) is a set of size , where , as indicated, is the number of candidate locations. Even for a small set of candidate locations, say , the number of network topologies would be larger than , which makes it infeasible to compare all possible topologies by means of time-consuming and computationally heavy system level simulations. For this reason, the proposed planning approach is a convenient strategy. The objective space (or* image*) is defined by the possible values of the objective functions. Due to the mathematical structure of and , the objective space is highly nonlinear, nonconvex, and full of discontinuities and local optima [18]. Thus, a multiobjective evolutionary algorithm (MOEA) [19], the Nondominated Sorting Genetic Algorithm II (NSGA-II) [20], is used to address (10a), (10b), (10c), and (10d). A brief description is provided in Appendix A.

###### 3.2.2. Single-Objective Optimization

Single-objective optimization is proposed if planning needs to be carried out following a max-min approach, such as the maximization of aggregate rate in the area elements with* weak* coverage. Thus, the problem of maximizing cell-edge performance (), for planning, can be written as follows:

Problem (11a), (11b), (11c), and (11d) and its constraints are similar to (10a), (10b), (10c), and (10d), except that it contains only one objective function. Constraints (11b) and (11c) are equal to (10b) and (10c), respectively. Constraint (11d) indicates that only solutions with BSs are accepted. This is so because, in general, the metric is proportional to due to the reduced level of intercell interference in network topologies with less BSs. However, the fact that the planning framework presented herein can consider both types of optimization (i.e., single-objective and multiobjective) is an indication of the versatility of the framework. Indeed, more than two objectives can also be considered, but at expense of significant increase in complexity. Moreover, in the opinion of the authors, the simultaneous consideration of more than two metrics would add complexity in the meaningful interpretation of the results.

#### 4. Planning Case Study

##### 4.1. Deployment Scenario

The network densification as planned by operators is both difficult and highly constrained in certain scenarios. This includes the fast expanding high-density urban and periurban settlements in emerging market areas. Indeed, 90% of the urban population growth by 2050 is expected to be concentrated in Asia and Africa. These settlements already have populations densities typically in the range of 40,000–200,000 people/km^{2} [21]. Mobile broadband networks continue to be the primary means for wireless connectivity in these densely populated areas [2, 22], which makes them a highly compelling target for the deployment of UDN. Unfortunately, some challenges related to UDN deployment are further exacerbated in these areas due to the limited availability of legacy infrastructure for small cell backhaul, energy scarcity, difficulties in site acquisition, need for securing network assets at sites, and relatively low Average Revenue Per User (ARPU) compared to more developed economies [22].

One of the interesting approaches is to leverage third-party nonoperator entities, such as individual end users, households, microenterprises, and public venue owners, to deploy shared-access small cells that will provide service as an integral part of the operator’s network. An example is the neighborhood small cell concept by Qualcomm promoting the use of privately deployed residential small cells as shared-access points [12]. A key distinction between third-party deployments and operator-led deployments is that, in the former case, the small cells deployments are* unplanned*; that is, the location in which small cells are deployed is not originally defined by the operator’s network planning procedures. However, although small cells are deployed autonomously by third-parties, the operator retains remote management via core network and the use of Self-Organizing Networks (SON) [23, 24].

Therefore, to contextualize the proposed UDN planning and optimization framework in a realistic setting, we consider a case study for UDN in a high-density urban settlement. To that end, we use the Hanna Nassif ward in Dar es Salaam, Tanzania, as the planning study case. Hanna Nassif has an estimated population density of 40000 people/km^{2}. The approximately 1 km^{2} Hanna Nassif area includes around 3000 buildings (mostly 3–6 m tall) and is located on a terrain with a topographical difference of 19 m. A three-dimensional (3D) representation of this scenario is shown in Figure 1. We assume that all candidate locations (indicated in white-blue dots) are outdoor at rooftop level. Rooftop deployed shared-access small cells provide improved outdoor coverage compared to indoor deployed small cells and enable line-of-sight (LOS) or near LOS (nLOS) conditions for the implementation of high-capacity wireless backhauling [25]. Moreover, the rooftop is also a convenient location for off-grid operation of the small cells through energy harvesting from ambient renewable energy sources (solar, wind, etc.) [26].