IEEE Computational Intelligence Magazine - May 2018 - 64

1.0

CDF

0.8
0.6
0.4
BDA-NSGA-II
MLB
MRO

0.2
0.0
0.00

0.05

0.10
0.15
Average THR (Mb/s)

0.20

0.25

Figure 11 cdF of the average throughput values in the whole network.

values have the highest rate. On the
other hand, for the TTT, which delays
handover execution, just 8 values out of
the 16 possibilities defined by the 3GPP
are found, with 128 ms being the most
likely one. It is important to note that all
the solutions in the Pareto front are
non-dominated and constitute a global
solution to the SON conflict in the
form of trade-off. That is to say, the
tool provides a set of solutions, and
the operator must choose one or another
depending on the objective to be prioritized. It is a usual practice to define
thresholds for each of the performance
metrics and to choose a solution respecting all of them (or at least the closest one). In this particular example, we
choose one of the solutions having an
RLF ratio < 1%, ping-pong rate < 1%,
average SINR > 15 dB and the average
number of allocated PRBs > 2. Figure 11 shows the Cumulative Distribution Function (CDF) of the cell average
throughput. From this figure, we observe
that our proposed scheme is able to find
the operating point that provides the
best performance trade-off.

mance. We build a prediction model
based on historical UE measurements,
and we apply regression analysis techniques to predict network performance.
The built model is then used as an input
of multi-objective evolutionary algorithm to solve the potential conflicts by
finding a set of solutions that satisfy the
objectives at an acceptable level without
being dominated by any other solution.
To evaluate the performance of the
proposed scheme, we focus on the
MLB-MRO SON conflict. The simulation results demonstrate the ability of the
proposed scheme to solve conflicts based
on a prediction of network performance,
which is obtained from a proper analysis
of UE measurements. As a result, the
proposed scheme learns from past experience to predict network performance
according to the target of each SON
function and then solve the conflict
based on non-dominated solutions.
Acknowledgment

This work was supported by the Spanish
National Science Council and ERFD
funds under TEC2014-60258-C2-2-R
and TEC2017-89429-C2-2-R projects.

VI. Concluding Remarks

In this paper, we have addressed the
issue of SON conflict. In particular, we
focus on the SON conflict that results
from the concurrent execution of multiple SON functions. The main contribution of this work is to present a
framework that is able to take advantage
of big data analytics, i.e., we exploit the
huge amount of data already available in
the network to predict future perfor-

64

References

[1] "Evolved Universal Terrestrial Radio Access Network
(E-UTRAN); Self configuring and self optimizing network uses case and solutions (Release 9)," 3GPP, Tech.
Rep. TR 36.902, v9.2.0, 2009.
[2] J. Johansson, W. A. Hapsari, S. Kelley, and G. Bodog,
"Minimization of drive tests in 3GPP release 11," IEEE
Commun. Mag., vol. 50, no. 11, pp. 36-43, Nov. 2012.
[3] "Study of implementation alternative for SON coordination," 3GPP TSG SA WG5 (Telecom Management) Meeting 85, Tech. Rep. TSG S5-122330, 2012.
[4] Z. Altman, M. Amirijoo, F. Gunnarsson, H. Hoffmann, I. Z. Kovcs, D. Laselva, B. Sas, K. Spaey, A. Tall,
H. van den Berg, and K. Zetterberg, "On design princi-

IEEE ComputatIonal IntEllIgEnCE magazInE | may 2018

ples for self-organizing network functions," in Proc. 11th
Int. Symp. Wireless Communication Systems, pp. 454-459,
Aug. 2014.
[5] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A
fast and elitist multiobjective genetic algorithm: NSGAII," IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197,
Apr. 2002.
[6] T. Bandh, H. Sanneck, and R. Romeikat, "An experimental system for son function coordination," in Proc.
IEEE 73rd Vehicular Technology Conf., pp. 1-2, May 2011.
[7] J. Moysen and L. Giupponi, "Self-coordination of
parameter conf licts in D-SON architectures: A Markov
decision process framework," EURASIP J. Wireless Commun. Netw., vol. 2015, no. 1, pp. 18, Mar. 2015.
[8] J. Chen, H. Zhuang, B. Andrian, and Y. Li, "Difference-based joint parameter configuration for MRO and
MLB," in Proc. IEEE 75th Vehicular Technology Conf., pp.
1-5, May 2012.
[9] P. Muñoz, R. Barco, and I. de la Bandera, "Load balancing and handover joint optimization in LTE networks
using fuzzy logic and reinforcement learning," ELSIVER
Comput. Netw., vol. 76, pp. 112-125, Jan. 2015.
[10] O. Iacoboaiea, B. Sayrac, S. Ben Jemaa, and P. Bianchi, "SON Coordination for parameter conf lict resolution: A reinforcement learning framework," in Proc.
IEEE Wireless Communications Networking Conf. Workshops, pp. 196-201, Apr. 2014.
[11] C. Szepesvári, Algorithms for Reinforcement Learning,
Ser. G: Reference Information and Interdisciplinary Subjects
Series. Morgan Claypool, 2010.
[12] N. Baldo, L. Giupponi, and J. Mangues-Bafalluy,
"Big data empowered self organized networks," in Proc.
20th European Wireless Conf., May 2014, pp. 1-8.
[13] A. Imran, A. Zoha, and A. Abu-Dayya, "Challenges
in 5G: How to empower SON with big data for enabling
5G," IEEE Netw., vol. 28, no. 6, pp. 27-33, Nov. 2014.
[14] J. Moysen, L. Giupponi, and J. Mangues-Bafalluy, "A
mobile network planning tool based on data analytics,"
Mobile Inform. Syst., vol. 2017, Feb. 2017.
[15] J. Moysen, L. Giupponi, N. Baldo, and J. ManguesBafalluy, "Predicting QoS in LTE HetNets based on location-independent UE measurements," in Proc. IEEE
20th Int. Workshop Computer-Aided Modeling Analysis and
Design Communication Links and Networks, Sept. 2015,
pp. 124-128.
[16] J. Moysen, L. Giupponi, and J. Mangues-Bafalluy,
"On the potential of ensemble regression techniques for
future mobile network planning," in Proc. IEEE Symp.
Computers and Communications, June 2016, pp. 477-483.
[17] K. van Moffaert and A. Nowé, "Multi-objective
reinforcement learning using sets of Pareto dominating
policies," J. Mach. Learn. Res., vol. 15, no. 1, pp. 3483-3512,
Jan. 2014.
[18] T. G. Dietterich, "An experimental comparison of
three methods for constructing ensembles of decision
trees: Bagging, boosting and randomization," Mach.
Learn., vol. 40, pp. 139-157, Aug. 2000.
[19] V. N. Vapnik, The Nature Statistical Learning Theory.
New York: Springer-Verlag, 1995.
[20] E. M. Jordaan and G. F. Smits, "Estimation of the
regularization parameter for support vector regression,"
in Proc. Int. Joint Conf. Neural Networks, 2002, vol. 3, pp.
2192-2197.
[21] N. Cristianini and J. Shawe-Taylor, An Introduction
to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press,
2000.
[22] A. Smola and B. Schölkopf, "A tutorial on support
vector regression," Statist. Comput., vol. 14, no. 3, pp.
199-222, Aug. 2004.
[23] J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. of Michigan Press, 1975.
[24] "Technical Specif ication Group Radio Access
Network; Evolved Universal Terrestrial Radio Access
(E-UTRA); Radio Resource Control (RRC); Protocol Specification (Release 10)," 3GPP, Tech. Rep. TS
36.331, v10.7.0, 2010.
[25] "Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (EUTRA); Physical layer procedures (Release 10)," 3GPP,
Tech. Rep. TS 36.213, v10.2.0, 2011.



Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - May 2018

Contents
IEEE Computational Intelligence Magazine - May 2018 - Cover1
IEEE Computational Intelligence Magazine - May 2018 - Cover2
IEEE Computational Intelligence Magazine - May 2018 - Contents
IEEE Computational Intelligence Magazine - May 2018 - 2
IEEE Computational Intelligence Magazine - May 2018 - 3
IEEE Computational Intelligence Magazine - May 2018 - 4
IEEE Computational Intelligence Magazine - May 2018 - 5
IEEE Computational Intelligence Magazine - May 2018 - 6
IEEE Computational Intelligence Magazine - May 2018 - 7
IEEE Computational Intelligence Magazine - May 2018 - 8
IEEE Computational Intelligence Magazine - May 2018 - 9
IEEE Computational Intelligence Magazine - May 2018 - 10
IEEE Computational Intelligence Magazine - May 2018 - 11
IEEE Computational Intelligence Magazine - May 2018 - 12
IEEE Computational Intelligence Magazine - May 2018 - 13
IEEE Computational Intelligence Magazine - May 2018 - 14
IEEE Computational Intelligence Magazine - May 2018 - 15
IEEE Computational Intelligence Magazine - May 2018 - 16
IEEE Computational Intelligence Magazine - May 2018 - 17
IEEE Computational Intelligence Magazine - May 2018 - 18
IEEE Computational Intelligence Magazine - May 2018 - 19
IEEE Computational Intelligence Magazine - May 2018 - 20
IEEE Computational Intelligence Magazine - May 2018 - 21
IEEE Computational Intelligence Magazine - May 2018 - 22
IEEE Computational Intelligence Magazine - May 2018 - 23
IEEE Computational Intelligence Magazine - May 2018 - 24
IEEE Computational Intelligence Magazine - May 2018 - 25
IEEE Computational Intelligence Magazine - May 2018 - 26
IEEE Computational Intelligence Magazine - May 2018 - 27
IEEE Computational Intelligence Magazine - May 2018 - 28
IEEE Computational Intelligence Magazine - May 2018 - 29
IEEE Computational Intelligence Magazine - May 2018 - 30
IEEE Computational Intelligence Magazine - May 2018 - 31
IEEE Computational Intelligence Magazine - May 2018 - 32
IEEE Computational Intelligence Magazine - May 2018 - 33
IEEE Computational Intelligence Magazine - May 2018 - 34
IEEE Computational Intelligence Magazine - May 2018 - 35
IEEE Computational Intelligence Magazine - May 2018 - 36
IEEE Computational Intelligence Magazine - May 2018 - 37
IEEE Computational Intelligence Magazine - May 2018 - 38
IEEE Computational Intelligence Magazine - May 2018 - 39
IEEE Computational Intelligence Magazine - May 2018 - 40
IEEE Computational Intelligence Magazine - May 2018 - 41
IEEE Computational Intelligence Magazine - May 2018 - 42
IEEE Computational Intelligence Magazine - May 2018 - 43
IEEE Computational Intelligence Magazine - May 2018 - 44
IEEE Computational Intelligence Magazine - May 2018 - 45
IEEE Computational Intelligence Magazine - May 2018 - 46
IEEE Computational Intelligence Magazine - May 2018 - 47
IEEE Computational Intelligence Magazine - May 2018 - 48
IEEE Computational Intelligence Magazine - May 2018 - 49
IEEE Computational Intelligence Magazine - May 2018 - 50
IEEE Computational Intelligence Magazine - May 2018 - 51
IEEE Computational Intelligence Magazine - May 2018 - 52
IEEE Computational Intelligence Magazine - May 2018 - 53
IEEE Computational Intelligence Magazine - May 2018 - 54
IEEE Computational Intelligence Magazine - May 2018 - 55
IEEE Computational Intelligence Magazine - May 2018 - 56
IEEE Computational Intelligence Magazine - May 2018 - 57
IEEE Computational Intelligence Magazine - May 2018 - 58
IEEE Computational Intelligence Magazine - May 2018 - 59
IEEE Computational Intelligence Magazine - May 2018 - 60
IEEE Computational Intelligence Magazine - May 2018 - 61
IEEE Computational Intelligence Magazine - May 2018 - 62
IEEE Computational Intelligence Magazine - May 2018 - 63
IEEE Computational Intelligence Magazine - May 2018 - 64
IEEE Computational Intelligence Magazine - May 2018 - 65
IEEE Computational Intelligence Magazine - May 2018 - 66
IEEE Computational Intelligence Magazine - May 2018 - 67
IEEE Computational Intelligence Magazine - May 2018 - 68
IEEE Computational Intelligence Magazine - May 2018 - 69
IEEE Computational Intelligence Magazine - May 2018 - 70
IEEE Computational Intelligence Magazine - May 2018 - 71
IEEE Computational Intelligence Magazine - May 2018 - 72
IEEE Computational Intelligence Magazine - May 2018 - 73
IEEE Computational Intelligence Magazine - May 2018 - 74
IEEE Computational Intelligence Magazine - May 2018 - 75
IEEE Computational Intelligence Magazine - May 2018 - 76
IEEE Computational Intelligence Magazine - May 2018 - 77
IEEE Computational Intelligence Magazine - May 2018 - 78
IEEE Computational Intelligence Magazine - May 2018 - 79
IEEE Computational Intelligence Magazine - May 2018 - 80
IEEE Computational Intelligence Magazine - May 2018 - Cover3
IEEE Computational Intelligence Magazine - May 2018 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202311
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202308
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202305
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202302
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202211
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202208
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202205
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202202
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202111
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202108
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202105
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202102
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202011
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202008
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202005
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_202002
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201911
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201908
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201905
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201902
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201811
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201808
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201805
https://www.nxtbook.com/nxtbooks/ieee/computationalintelligence_201802
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring17
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring16
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring15
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring14
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_summer13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_spring13
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_winter12
https://www.nxtbook.com/nxtbooks/ieee/computational_intelligence_fall12
https://www.nxtbookmedia.com