- Open Access
Automatic clustering algorithms for indoor site selection in LTE
© Hernández et al. 2016
Received: 1 August 2015
Accepted: 13 March 2016
Published: 22 March 2016
Small cell systems are a cost-effective solution to provide adequate coverage inside buildings. Nonetheless, the addition of any indoor site requires evaluating the trade-off between the coverage and capacity gain provided by the new site and its monetary cost. In this paper, a new automatic indoor site selection algorithm based on clustering techniques is presented. The algorithm calculates the number of antennas, radio heads, and baseband units needed for the area under study. Then, a clustering algorithm groups several radio heads of different buildings in a single pooled baseband unit, reducing deployment costs. The proposed clustering algorithm is based on a local refinement algorithm, whose starting point considers a new baseband unit for every new site, and then, possible reallocation to existing units is checked. To assess the method, the proposed indoor site selection algorithm is included in a network planning tool. The algorithm is tested in a real heterogeneous network scenario, taking into account vendor specifications and operator constraints. Results show that the use of the proposed clustering algorithm can reduce the total network cost by up to 58 % in a real scenario.
Mobile data traffic is expected to increase considerably in the coming years. Specifically, a tenfold increase of mobile traffic from 2014 to 2019 is envisaged by several equipment vendors [1, 2]. In parallel, surveys predict that more than 70 % of this traffic will be generated indoors, but almost half of the houses and premises have poor indoor coverage currently [3, 4]. It is therefore necessary to develop a cost-effective solution that provides adequate coverage in buildings.
Providing indoor coverage and capacity has been a challenge since the start of mobile networking. Femtocell solutions [5–7] based on short-range low-cost low-power base stations are generally used to fulfill indoor capacity needs. Unfortunately, these systems only work correctly for small deployments, because cell planning and radio coordination become unmanageable when the number of cells increases . For medium-to-large buildings, venues, and areas, macrocell features like coordination, seamless mobility, and interference management are needed. For the last two decades, these needs have been overcome by distributed antenna systems (DAS) [9–11], where a cell’s antenna is divided into several antennas covering the same area with a higher power efficiency, but still defining only one cell. This solution is still considered for multi-operator cases and neutral host applications. However, this approach becomes limited when new requirements for higher capacity and more advanced services appear, which is typically the case of medium-to-large buildings. In response to these new requirements, a promising solution is the deployment of small cells .
Unlike femtocells, which have limited functionality, small cell solutions provide to the indoor segment the same functionality as the outdoor macrocell, i.e., the entire structure behaves as a macrocell enabling coordination with the rest of the mobile network, thus simplifying network operation and maintenance. At the same time, small cell solutions enhance system scalability and improve radio system performance compared to DAS systems.
Indoor site selection can be formulated as the discrete optimization problem known as Capacitated Facility Location Problem (CFLP) [13, 14]. Similarly to macrocellular network planning, the addition of a small cell system in an existing network is a trade-off between coverage/capacity improvement in the indoor area and the additional expenses due to the small cell infrastructure (also called In-Building Solution, IBS). Several works have studied the impact of site locations on cellular network performance in terms of coverage and/or signal to interference plus noise ratio (SINR). Thus, many different methods have been proposed to find the best location for macrocellular sites to improve network coverage, user connection quality, and/or network capacity [15–19]. Similar methods have been used for the optimal location of wireless access points inside buildings [20–27]. All these approaches build a system model, over which a classical optimization algorithm is applied to find the location of access points maximizing some overall network performance indicator. In , an analysis of the properties of the optimal femtocell layouts for different radio performance criteria is presented. However, few studies have paid attention to deployment cost. Adding an IBS requires certain network resources (e.g., antennas, baseband units, and cabling), whose cost depends on decisions made during network planning. The current practice is to assign a baseband unit to every single building with an IBS. When medium/small buildings are considered, IBS resources are wasted because the majority of its ports are empty. For these cases, a clustering algorithm can reduce deployment costs by assigning the radio heads of different buildings into a single baseband unit. As a counterpart, there is an increase of cabling costs, which can be less than the initial solution with more baseband units, especially when linked buildings are close enough. To the authors’ knowledge, no study has considered the use of clustering for sharing resources between buildings in the context of indoor site selection.
In this paper, a new clustering algorithm for the assignment of radio heads to baseband units in Long Term Evolution (LTE) is presented. The proposed algorithm is designed to be integrated in the site selection algorithm of a network planning tool. The algorithm is tested with a dataset constructed from a real heterogeneous network scenario including macrocellular and microcellular sites (i.e., outdoor and indoor areas), vendor specifications, and operator constraints. The main contributions of this work are (a) a new clustering algorithm that minimizes indoor solution deployment costs, which can be integrated into a classical site location algorithm and (b) the results of the clustering algorithm in a real LTE heterogeneous environment. The rest of the paper is organized as follows. Section 2 formulates the indoor site selection problem from the operator perspective. Section 3 describes the proposed solution method. Then, Section 4 presents the performance analysis of the proposed method carried out with a system-level simulation tool. Finally, Section 5 presents the concluding remarks.
2 Problem formulation
Figure 2 shows the RDS architecture and interfaces between elements. The DU provides pooled baseband processing for the system. It uses the Common Public Radio Interface (CPRI) standard to transfer synchronization, radio signals, and operation and management signals to the IRU. The IRU is a newly designed radio unit that incorporates existing macro software features (e.g., interference coordination, traffic management, and LTE combined cell), extending them with new indoor features (e.g., real-time traffic steering). The IRU connects to each Radio Dot using a proprietary IRU-Radio Dot interface over a conventional twisted pair local area network (LAN) cable. When co-located with the DU, an electrical CPRI interface is used, whereas a CPRI fiber interface is used for remote connection with the DU. Finally, the Radio Dot has two integrated antennas in a 10-cm form factor and weighs under 300 g. Each Radio Dot is connected to the IRU through a dedicated LAN cable and remotely powered by Power over Ethernet (PoE). As in ethernet networks, the system employs a star topology, as opposed to the tree topology used in DAS. The ultra-compact design and use of LAN cabling simplify installation.
The use of the latter structure can be generalized to small buildings that would not deserve their own DU in the proximity of large buildings that already have a DU. In these cases, a clustering algorithm is needed to evaluate when this shared structure is possible. When assigning the IRUs/Radio Dots of a building to a DU of another building, three factors must be taken into account: (a) the amount of required Radio Dots per building (coverage aspects), (b) the amount of Radio Dots per IRU and IRUs per DU (equipment capacity), and (c) the maximum distance between IRUs (cabling limit and expenses). The aim of the clustering algorithm is to group buildings into DUs as much as possible so that deployment costs are minimized, while satisfying coverage, capacity, and cabling constraints.
3 Solution method
The dimensioning algorithm is applied independently to each building in the scenario. The aim of dimensioning is to estimate the number of antennas and IRUs needed per building. For the downlink of LTE, dimensioning is mainly driven by coverage issues [30, 31]. In this work, indoor coverage planning is based on simple geometrical assumptions to reduce the computational load in the network planning tool.
where N Dot(i), N Dot/floor(i), and N floor(i) are the number of Radio Dots, the number of Radio Dots per floor, and the number of floors in building i, respectively; S floor is the floor area (in square meters) of building i; and Dot cov is the Radio Dot coverage (in square meters), not depending on the shape of the building under study. Dot cov is a parameter included in the technical specification of the product, and the floor area can be computed with the Gauss area formula  from the base coordinates of the building.
Once the number of Radio Dots has been estimated, the number of IRUs is calculated. Either of the two topologies shown in Fig. 3 a, b might be used, leading to 6 or 12 IRUs per DU and eight Radio Dots per IRU. In practice, the star configuration is the typical configuration and is, therefore, considered hereafter.
Once the number of IRUs per building has been estimated in the dimensioning step, the clustering algorithm groups IRUs into DUs. The output of clustering is the assignment of every IRU to some DU.
Note that some of these decision variables are fixed, since there might already exist some sites implemented in the network. Some of them may have their own DU (i.e., X ii =1), while others do not (i.e., X ii =0 and X ij =1 for some j). The variable ω i denotes the number of IRUs needed in building i (estimated by the dimensioning algorithm) and B aw is the maximum available connection for IRUs in a DU.
The objective function in (3) consists of a first term reflecting equipment cost and a second term reflecting cabling costs. The first constraint in (4) forces that each building must be assigned to only one DU (i.e., single homing). The second constraint (5) reflects hardware limitations so that no more than B aw IRUs can be connected to the same DU.
If d ij ≤d max, the candidate site does not require a DU (as it is cheaper to assign its IRUs to the DU of a surrounding building with spare capacity) and the associated cost is due to fiber deployment (i.e., C cost=K fiber/m ∗d ij ). In contrast, if d ij >d max, the candidate site needs a new DU (since cabling cost are more expensive than buying a new DU or there is no building with a DU with enough spare capacity in the surroundings) and the cost is that of a new DU (C cost=K DU).
3.3 Indoor site ranking and selection
where C cov and C cap denote the coverage and capacity gains obtained, respectively, if that candidate indoor site is added and C cost is the deployment cost, including all radio and backhaul equipment. Parameters ω cov, ω cap, and ω cost are weights to prioritize objectives defined according to operator policies. Once all possible candidates are evaluated, the site with the largest FoM is selected.
The site selection algorithm works iteratively by selecting the next best candidate site. Normally, the operator runs several iterations of the site selection algorithm to define a set of new indoor sites that will be deployed in the future. The size of that set (and hence the number of iterations) depends on the budget.
As candidate sites are selected, new DUs may appear. After adding a site with a new DU, a reclustering algorithm checks if (a) that new DU can be avoided by relocating nearby DUs and (b) previously planned IRUs in the surroundings should be reallocated to the new DU to reduce cabling costs. If both options are feasible, the solution with the lowest cost is chosen.
4 Performance analysis
In this section, different tests are performed to assess the algorithms described previously. A real scenario obtained from a live heterogeneous network has been used. For clarity, the analysis setup is first introduced and results are then presented.
4.1 Analysis setup
Simulation tool parameters
System-level, static (grid-based)
Grid resolution (m)
Spatial traffic distribution
Irregular, based on PRB utilization ratio and TA measurements
Overall network PRB utilization = 31 %
Antenna configuration MIMO (2×2)
System bandwidth (MHz)
Number of PRBs
Propagation model (dB)
Outdoor: P L=A−13.82∗log10(h BS[m])+
A=157.5, B=44.9, h BS[m]=30
Shadowing log-normal fading, 8 dB std.
Correlation distance 20 m
Outdoor to indoor: 10-dB penetration losses
P tx =46 dBm, G=13 dB
Antenna height 1.5 m
Noise floor −114.45 dBm (per PRB)
The indoor site selection algorithm is tested with four different clustering approaches of increasing complexity in the same scenario: (a) the baseline solution considering one DU per building (i.e., no clustering); (b) the proposed heuristic clustering algorithm with no reclustering (i.e., once a building is assigned to a DU, it cannot be reallocated); (c) the previous approach including the reclustering feature, where the assignment can be modified after the selection of every new site; and (d) the exact (i.e., optimal) solution, obtained by solving the ILP model in (3–5) with the Gurobi solver , provided that the set of sites to be added is known.
First, all methods are evaluated assuming that only monetary cost is used for site selection (i.e., ω cov=0, ω cap=0, and ω cost=1, and thus, FoM≡C cost). This can be done outside the simulator, since no coverage and capacity computations are needed. Later, all methods are included in the simulator to compute coverage and capacity estimations for selecting the best candidates. For ease of analysis, all weights are set the same (i.e., ω cov=ω cap=ω cost). Thus, it is evaluated how site selection based on other parameters impacts on monetary cost.
As the focus of this work is the clustering method, the site selection algorithm is run in all cases until all the candidate buildings in the scenario (449) have been selected. Thus, the main performance indicator to assess the clustering methods is the total monetary cost, C cost. For ease of comparison, the cost of each solution is normalized by that of the baseline approach (i.e., without clustering). Note that coverage and capacity criteria are only included to quantify the impact of these factors on the clustering algorithm. A thorough analysis of coverage and capacity performance in the simulated heterogeneous network is beyond the scope of the work.
4.2 Analysis results
All the methods have been executed in a personal computer with a Intel(R) Core(TM) processor, 2.6-GHz clock frequency and 8 GB of RAM. When only monetary costs are considered, the proposed heuristic (re)clustering algorithm only takes less than 3 min to find a near-optimal solution with small deployment costs for the 449 buildings. In contrast, the exact solution built with Gurobi takes approximately 2 h.
In this paper, a novel automatic clustering algorithm for deploying IBS in a cost-effective manner has been proposed. In an initial dimensioning stage, the algorithm estimates the required number of elements in every building. Then, in a second stage, the clustering algorithms looks for the deployment solution with minimum cost by assigning different buildings into the same DU. The clustering solution is updated by a reclustering algorithm after adding every new site. The proposed clustering approach has been integrated into a site selection tool and tested with a dataset of a real heterogeneous scenario in a static system-level LTE simulator. Simulation results show that, when coverage, capacity, and monetary costs are considered, the inclusion of the proposed clustering algorithm can reduce the total network cost by up to 49 % in a real scenario.
This work has been funded by the Spanish Ministry of Economy and Competitiveness (TIN2012-36455) and Optimi-Ericsson and Agencia IDEA (Consejería de Ciencia, Innovación y Empresa, Junta de Andalucía, ref. 59288), co-funded by FEDER.
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