Automatic clustering algorithms for indoor site selection in LTE

Rocío Acedo-Hernández,Carlos Úbeda,Salvador Luna-Ramírez,Matías Toril,María Josefa Vera

doi:10.1186/s13638-016-0587-3

Rocío Acedo-Hernández, Carlos Úbeda + Show 3 more

Open Access

https://doi.org/10.1186/s13638-016-0587-3

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Abstract

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.

Highlights

Mobile data traffic is expected to increase considerably in the coming years
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 digital unit (DU) per building; (b) the proposed heuristic clustering algorithm with no reclustering; (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 solution, obtained by solving the Integer Linear Programming (ILP) model in (3–5) with the Gurobi solver [34], provided that the set of sites to be added is known
When coverage and capacity factors are considered, sites are selected from distant locations in the network, which has a strong impact on clustering algorithms

Summary

Introduction

Mobile data traffic is expected to increase considerably in the coming years. a tenfold increase of mobile traffic from 2014 to 2019 is envisaged by several equipment vendors [1, 2]. Femtocell solutions [5,6,7] based on short-range low-cost low-power base stations are generally used to fulfill indoor capacity needs These systems only work correctly for small deployments, because cell planning and radio coordination become unmanageable when the number of cells increases [8]. 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. In the case of an existing nearby Radio Base Station (RBS), the DU can be co-located (e.g., a rooftop site on the same building) This structure enables advanced LTE coordination between outdoor and indoor coverage. The star configuration is the typical configuration and is, considered hereafter

Clustering

Analysis results

Findings

Conclusions