Centralized Self-Optimization in LTE-A Using

Active Antenna Systems

Yasir Khan, Berna SayracOrange Labs

38, rue du Général Leclerc

92794, Issy les Moulineaux, France

Email: {yasir.khan,berna.sayrac}@orange.com

Eric MoulinesDépartement Traitement du Signal et des Images,

Télécom Paris Tech, 46, Rue Barrault

75634, Paris Cedex 13, France

Email: moulines@telecom-paristech.fr

Abstract—Active Antenna Systems (AAS) have the flexibilityto electronically orient the antenna beams thereby providing aflexible and efficient control over the performance of the networkin terms of coverage, capacity and Quality-of-Service (QoS). Inthis paper we propose a new centralized self-optimizing methodwhich adapts antenna tilts according to a specified objective.The proposed method uses a surrogate function to model thefunctional relationships between noisy Key Performance Indica-tors (KPIs) and antenna parameters, and subsequently performsoptimization of the model using a pattern search algorithm in aniterative manner. The methodology is applied to solve severalmulti-parameter self-optimization problems in a Long TermEvolution Advanced (LTE-A) network. Results obtained using aflow level simulator shows the advantages of the proposed AASoptimization method in terms of fast convergence, performanceand flexibility in multi-parameter network optimization.

Index Terms—Automated Optimization; Active Antenna Sys-tems; Load Balancing; Self-optimization; Kriging; PatternSearch

I. INTRODUCTION

Increased complexity in existing LTE and LTE-A networks

has forced the network operators to ensure operational effi-

ciency over the entire life cycle of a network while also evolv-

ing with implementation of new technologies and features [1].

Operators are required to ensure optimum Quality of Service

(QoS) demand of the customers while at the same time looking

for opportunities to reduce OPerational EXpenditures (OPEX)

for immediate gains and CAPital EXpenditures (CAPEX)

for long term gains. Self Organizing Networks (SON), a

concept evolved to meet this demand involves self-planning

for pre-deployment phase, self-optimization for automation

of the manual optimization process during network operation

and self-healing mechanisms to reduce impacts from failures.

Self-optimization process improves capacity, quality, reduce

operational costs and delays infrastructural investments [1].

SON mechanisms are architecture and algorithm dependent.

A distributed SON architecture involves algorithms located

closer to the base stations (i.e. control plane) and which

makes frequent parameter changes (in the order of seconds

to minutes). A centralized architecture implements functions

in the Operations and Maintenance Center, OMC (i.e. the

management plane), makes less reactive parametric changes

(hours to days) and is susceptible to less noisy observations

due to averaging over a longer time compared to a distributed

architecture. Several centralized SON use cases have been

investigated so far in literature such as for Mobility Load

Balancing (MLB) [2] and Enhanced InterCell Interference

Coordination (eICIC) [3]. Recently there has been a growing

interest in Active Antenna Systems (AAS) [1] which have the

potential to provide better network performance by changing

the antenna tilt, azimuth or beam shape. Antenna tilt is defined

by 3rd Generation Partnership Project (3GPP) as an important

network optimization parameter that can be used to carry out

Load Balancing (LB) through optimal coverage boundary ad-

justment of neighbouring cells [1]. Several studies for antenna

tilt based load balancing has shown to provide performance

gains in inhomogeneous user traffic distribution [4], [5]. To

the best of our knowledge, a centralized self-optimization

framework using antenna tilts has not been addressed for LTE-

A networks.

The focus of this paper is centralized self optimization

for LTE-A networks based on antenna tilt optimization. In

contrast to a decentralized approach, a centralized approach is

important for the operators due to the availability of control

at the management plane. It is in an operator’s best interest to

develop cost efficient and robust centralized self-optimization

solutions to control (at the management plane) a multi-vendor

network in which several disjoint distributed solutions are

running independently at the control plane.

The rest of the paper is organized as follows. Section

II provides a description of the AAS based optimization

formulation. Section III details the surrogate technique used

for model development and a pattern search algorithm for

optimization on the developed surrogate. Section IV provides

the LTE-A simulator description and antenna tilt radiation

pattern formulation. The results are presented in Section V

and the paper is concluded in Section VI.

II. ACTIVE ANTENNA SYSTEM BASED

SELF-OPTIMIZATION IN LTE-A

Let us assume a cluster of target cells represented by the

set T , comprising of a central cell surrounded by its first

tier neighbours. A collection of observation cells represented

by the set O, surround this cluster and form the second tier

neighbours of the central cell. A typical self optimization

scenario consists of optimizing a predetermined KPI for the

978-1-4799-0543-0/13/$31.00 ©2013 IEEE

target cells by adjusting each of the antenna tilts in T while

monitoring the Key Performance Indicators (KPI) performance

of the observation cells. Thus, the self optimization algorithm

performs optimization by adjusting each of the antenna tilts

and proposes an optimum vector of antenna tilt combination.

Denoting the antenna tilt of cell s as �s (˚) and the vector

of antenna tilts by x =[

�1 �2 . . . �s . . . �∣T ∣]

, ∀s ∈ T , the

objective function of self-optimization can be written as:

x∗ = argmin f (x)

x

s.t. c (x) ≤ Tℎc, �min ≤ �s ≤ �max, ∀s ∈ T(1)

where f (x) and c (x) are the objective functions to be opti-

mized and the constraint function respectively, which are both

functions of one or more predetermined KPIs defined over

T . c (x) = max[BCR1 (x) ..BCR2 (x) ..BCRs (x)] where

BCRs is the Blocked Call Rate (BCR) of cell s, ∀s ∈ T , ∣ T ∣being the cardinality of T . Tℎc is the constraint threshold on

BCR, �min, �max are the minimum and maximum allowable

antenna tilt values to prevent coverage issues such as coverage

holes, pilot pollution etc. Note that the above optimization

problem is formulated as a minimization problem and it can

be formulated as a maximization without loss of generality.

Based on the desired optimization objective, we define a

load balancing objective function f (x) = max[load1 (x) ..

load2 (x) .. loads (x)] for the minimization of the maximum

cell load in T (i.e. a Load Balancing scenario), where loads is

the load of cell s, ∀s ∈ T . Studies were carried out for several

optimization objective functions, for brevity only results of

load balancing objective are included in this work.

III. STOCHASTIC MODELING AND OPTIMIZATION

We propose to solve the above optimization problem by

defining stochastic models that describe the relationships f (x)and c (x). This stochastic modeling is carried out through

a technique known as Kriging [6]. Statistical models devel-

oped using Kriging act as surrogates that replaces the real

relationship, and are then used in a sequential optimization

technique called as Search & Poll algortihm [7]. Detailed

analysis of Kriging and Search & Poll algortihm for iterative

self-optimization are provided in [2], [3].

IV. SIMULATOR DESCRIPTION

A. LTE-A Network and System Parameters

A regular LTE sub-network consisting of ∣ T ∣=7 target cells and ∣ O ∣= 12 observation cells are

considered, where T ≡ {1, 2, 3, 5, 6, 9, 13} and O ≡{4, 7, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19}. Two traffic zones are

considered: G1 with a regular traffic intensity �1 and G2 with a

higher traffic intensity �2, such that the 1st, 5tℎand 6tℎ target

cells are in G2, the other 4 target cells and all observation

cells are in G1. A flow-level simulator which computes cell

level KPIs such as loads, BCRs, 5%-ile throughputs, 50%-ile

throughputs and File Transfer Times (FTTs) is used [8]. The

simulator computes KPIs based on the traffic demand and the

antenna tilts of the cells in T , �s, ∀s ∈ T . The minimum and

Parameters Settings

Number of tilt changes per cell ℜ 6 (2˚ to 12˚ in steps of 2˚)

Antenna height (h) 32m

Bandwidth 20 MHz

Inter site distance (ISD) 500 m

Link model SISO, AWGN + Rayleigh fading

PRBs per eNB 100

Path loss L=128.1+37.6log10(R), R in Km

Thermal noise density -174 dBm/Hz

Shadowing standard deviation 6 dB

Traffic model FTP

File size 10 MBytes

�1 (over the entire area of G1) 275 Mbps

�2 (over the entire area of G2) 330 Mbps

Table I: System level simulation parameters

maximum allowable antenna tilts are chosen as �min = 2˚ and

�max = 12˚ to ensure full coverage without pilot pollution.

Simulations are carried out for the downlink and no mobility

is assumed. Table I lists the simulation parameters used.

B. Antenna Tilt Model

AAS is based on the concept of electronically steering

the beam orientation along the azimuth and elevation thereby

changing the gain recieved at a user location in the network.

The tilt � determines the coverage extent of the cell and is

an important parameter, which can be used to balance load or

reduce intercell interference thereby increasing capacity of the

network. The radiation pattern of an antenna determines the

gain at a fixed location ( ,') where is the elevation angle

computed using the antenna height and the ground distance

from the antenna to that location; ' is the azimuth angle

computed using the antenna and the location coordinates. For

a trisectorial site, 3GPP defines azimuth, elevation and the

total radiation patterns at any location ( ,') and the same is

adopted for simulations in this work [9].

C. Iterative Self-Optimization Process

The iterative self-optimization process involves a Simu-

lation block which accepts as input the antenna tilts, and

computes the corresponding observations (KPIs). The antenna

tilts together with the corresponding KPIs constitue a data

point and is input to the Self-Optimization block. The Self-

Optimization block comprises of two sub-blocks viz. Model

and Optimization. The Model sub-block takes in the data point,

appends it to the existing data set and updates the Kriging

model. The updated model is used by the Optimization sub-

block to carry out a Search and Poll (S&P) for the optimum

antenna tilt settings which are then fed into the Simulator block

to obtain the next data point in an iterative manner [2], [3].

V. RESULTS

A. Prediction quality of Kriging model

First, the prediction quality of Kriging on the above de-

scribed AAS-based optimization problem is evaluated. For

this, classical prediction quality metrics, such as the coefficient

of determination R2 = 1 − SSres

SStot, the normalized mean

R2 NMSE CV (%)

f(x) 0.9124 100e-05 2.89

c(x) 0.9260 1770e-5 13.28

Table II: Kriging prediction quality

squared error NMSE = 1n

∑n

i=1(yi−myi)

2

ymyand coefficient

of variation CV =

√

∑

ni=1

(yi−myi )2

n

y× 100 has been used

as performance indicators, where SSres =∑n

i=1 (yi −myi)2

is the residual sum of squares, SStot =∑n

i=1 (yi − y)2

is

the total sum of squares, y = 1n

∑n

i=1 yi is the mean of

the observations and my = 1n

∑n

i=1myiis the mean of the

predictions. R2 is an indicator of how well the model fits

the data (R2 = 1 indicating a perfect fit), CV indicates

the dispersion of noise around the model and NMSE is

an estimator of the overall deviations between predicted and

measured values.

A quota of 400 out of the possible 279, 936 (67) design

points (antenna tilt value combinations) were set aside for

model building and prediction. Out of this 400 points, 205was used as a training set of random design points to build the

surrogate model. The remaining 195 was used as a validation

set to evaluate the prediction quality of the built model. Table

II lists the quality indicators for different objective functions

as well as the constraint functions. These measures indicate

a higher confidence on the initial model for carrying out

subsequent iterative optimization.

B. Optimization performance

A surrogate model built using 205 random design points

and a Gaussian covariance kernel is used for the proposed self-

optimization method. It is assumed that the base stations are

operating at an unoptimized default antenna tilt combination of

{6˚, 6˚, 6˚, 6˚, 6˚, 6˚, 6˚}. Tℎc is fixed at 5%. Figure 1 depicts

the unoptimized (black) and optimized (white) cell KPIs for

f (x). The lowest right barchart shows the optimized KPI for

the observation cells, and the other five barcharts show the

cell load, BCR, FTT, 5%-ile throughput (�5%) and 50%-ile

throughput (�50%) for the observation and target cells. It is

clear that the algorithm attempts to offload from cell 1 to

neighbouring cells thereby causing an improvement of QoS in

cell 1 and tolerable degradation in some of the neighbouring

cells as shown. Optimization leads to reduction in cell 1

load by 19.20%, FTT by 46.51% and BCR by 91.23%. �5%

and �50% of cell 1 are improved by 118.09% and 146.62%respectively. Observe that the constraint on BCR keeps the

BCR of cell 1 below Tℎc. The load in the observation cells O

are almost unaffected by the optimization as can be seen in the

figure. The optimization results in a 10.76% improvement in

the f(x) objective function value. Only 41 network evaluations

were needed to confirm the global optimum.

VI. CONCLUSION

We presented in this paper a centralized self-optimization

framework for AAS-based load balancing in LTE-A. The

1 2 3 5 6 9 130

0.5

1

Load

4 7 8 10 11 12 14 15 16 17 18 190

0.5

1

Load

Cell Index

1 2 3 5 6 9 130

0.05

0.1

BC

R

1 2 3 5 6 9 130

1

2

3

FT

T (

s)

1 2 3 5 6 9 130

2

4x 10

7

ρ5%

(M

bps)

Cell Index

1 2 3 5 6 9 130

5

10

15x 10

7

ρ50%

(M

bps)

Figure 1: Cell KPIs for f(x) objective optimization

framework uses a statistical surrogate model for the parameter-

KPI relationship and a pattern search algorithm for surrogate

based constrained optimization. The proposed approach has

been applied to antenna tilt based constrained network op-

timization. The optimization results in best network KPI im-

provements upto 146.62% under constraints thereby signifying

the relative efficiency of the approach. The proposed approach

converges fast with significantly low network evaluations and

therefore well suited for centralized self-optimization on an

operational network.

ACKNOWLEDGMENT

The authors would like to thank Mr. Richard Combes who

developped the Matlab simulator.

REFERENCES

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[2] Y. Khan et al., “Surrogate based centralized automatedoptimization applied to LTE mobility load balancing", toappear in Proc. of 78th IEEE VTC, Fall 2013. Preprint:https://sites.google.com/site/yasirkhan380/research

[3] Y. Khan et al., “Surrogate based centralized SON: Application to interfer-ence mitigation in LTE-A HetNets”, to appear in Proc. of 77th IEEE VTC,Spring 2013. Preprint: https://sites.google.com/site/yasirkhan380/research

[4] Vlad-Ioan Bratu et al., “Antenna Tilt Load Balancing in Self-OrganizingNetworks,” International Journal of Research in Wireless Systems(IJRWS), Vol. 2, No. 1, pp. 21-26, March, 2013.

[5] Abou-Jaoude R et al., “Self-optimization of antenna tilt and pilot powerfor dedicated channels”, Proceedings of the 8th International Symposiumon Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks(WiOpt), 196 - 203, 2010.

[6] V. Picheny et al., "A benchmark of kriging-based infill criteria for noisyoptimization", Structural and Multidisciplinary Optimization, 2012.

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