Machine Learning Based Session Drop Prediction in LTE Networks and its SON Aspects

Machine Learning Based Session Drop Prediction in
LTE Networks and its SON Aspects
Bálint Daróczy†, Péter Vaderna∗, András Benczúr†
∗Ericsson Research, Management & Operation of Complex Systems, Budapest, Hungary
†Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), Budapest, Hungary
corresponding author: peter.vaderna@ericsson.com
Abstract—Abnormal bearer session release (i.e. bearer session
drop) in cellular telecommunication networks may seriously
impact the quality of experience of mobile users. The latest mobile
technologies enable high granularity real-time reporting of all
conditions of individual sessions, which gives rise to use data
analytics methods to process and monetize this data for network
optimization. One such example for analytics is Machine Learning
(ML) to predict session drops well before the end of session. In
this paper a novel ML method is presented that is able to predict
session drops with higher accuracy than using traditional models.
The method is applied and tested on live LTE data offline. The
high accuracy predictor can be part of a SON function in order
to eliminate the session drops or mitigate their effects.
Keywords—Session drop; Machine Learning; Self-Organizing
Network (SON)
I. INTRODUCTION
Management of Mobile Telecommunication Networks
(MTN) is a complex task. Setting up, operation and optimiza-
tion of MTNs such as those defined by the 3rd Generation
Partnership Project (3GPP) need high-level expert knowledge.
Therefore it is important for network operators that as many
processes in network deployment and operation are automated
as possible, thus reducing the cost of operation.
MTNs consist of network elements connected to each
other with standard interfaces and communicating via stan-
dard protocols. MTNs are managed by Network Management
System (NMS) running separately from the network elements.
NMS provides functions for network configuration via Con-
figuration Management (CM) and operation supervision via
Performance Management (PM) and Fault Management (FM).
There are specific functions in the CM, PM and FM systems
providing automatic configuration and optimization, usually
called self-configuration, self-optimization or self-healing. The
common name of these functions in the 3GPP standard is Self-
Organizing Network (SON) functions.
The present paper focuses on PM and performance op-
timization. With the evolution of the generations of the radio
and core networks ranging from 2G to 4G, PM reporting func-
tions of the network elements have become higher granularity
and more detailed, thus providing better observability. In 2G
systems PM relies mostly on counters providing aggregated
measurements over a given Reporting Output Period (ROP,
usually 15 minutes) within a certain node, in 3G systems it is
possible to get higher granularity measurements where per-user
events (e.g. Radio Resource Control (RRC) connection setup,
sending handover (HO) request, paging, etc.) and periodic per-
user measurement reports (sent from the User Equipment (UE)
to the nodeB indicating the current radio signal strength and
interference conditions) might appear in node logs. In LTE the
granularity grows even higher with the possibility of frequent
periodic (ROP=1.28 second) measurements per-user and/or
per-cell in eNodeBs [1]. Moreover, it is also possible to get the
event reports and periodic reports as a data stream, making it
possible to process the incoming measurements real-time. The
detailed, frequent, high-granularity, real-time reporting enables
further processing and analyzing the data and applying them
in data-driven techniques to be used in network functions,
especially in SON functions. If the SON function is placed at
a central network element (e.g. NMS) then the data should be
streamed from the eNodeBs. However, if the SON function is
placed in the eNodeBs then the PM trace should not necessarily
be up and running all the time. In this paper we follow a setup
where a Machine Learning (ML) model is trained offline based
on detailed node logs and applied real-time where the input can
be both from node logs or directly from the internal variables
of the node.
In LTE in order to enable communication between the UE
and the eNodeB a radio bearer is established. The main metric
of interest in the present paper is retainability in LTE systems
which is defined as the ability of a UE to retain the bearer
once connected, for the desired duration. The release of radio
bearers between the UE and the eNodeB can have multiple
reasons. There are normal cases such as release due to user
inactivity (after expiry of an inactivity timer), release initiated
by the user, release due to successful HO to another radio cell
or successful Inter Radio Access Technology (IRAT) HO, etc.
However, there can be abnormal releases (also called drops)
due to e.g. low radio quality either in downlink or uplink
direction, transport link problems in the eNodeB, failed HO,
etc. Unexpected session drops may seriously impact the quality
of experience of mobile users, especially those using real-time
services such as Voice-over-IP (VoIP).
The aim of this paper is to introduce and evaluate a ML
method to predict session drops before the end of session
and investigate how it can be applied in SON. The authors
in [2], [3] use traditional ML models, AdaBoost and Support
Vector Machine (SVM), to predict call drops in 3G network
and use the prediction result to either avoid them or mitigate
their effects. The features of the ML model in these studies
are aggregated values of certain radio events and reports in
a fixed time window preceding the drop. While the settings
greatly differ in these studies, the accuracy of our results is
much better than in [3] and comparable to [2]. In both papers,
prediction is only made where the session is dropped in the

Fig. 1. User session and reporting.
next second. In our paper, we address the SON aspects by
evaluating the power of our methods for predicting several
seconds before session termination.
We provide an improved ML methodology where the high
granularity of the performance reports is exploited and the time
evolution of the main features is used as extra information to
increase prediction accuracy. We deploy and extend techniques
of time series classification [4]. For single parameter series,
nearest neighbor classifiers perform the best for time series
classification where the distance between two time series is
defined by Dynamic Time Warping (DTW) [5]. For session
drop prediction, however, we have six simultaneous data
sets and hence nearest neighbor methods cannot be directly
applied. We define a “similarity kernel” and use Support Vector
Machine (SVM) [6] for classification.
II. NETWORK MEASUREMENTS
A. Constituting user sessions
The analysis is based on raw logs of multiple eNodeBs
from a live network containing elementary signaling events
indicating e.g. RRC connection setup, UE context release,
successful HO to/from the cell, and periodic reports having
per-user radio and traffic measurements. The basic unit of
information is a Radio Bearer session within a cell. The session
is constituted from the elementary signaling events (see Fig. 1).
The session is started with setting up an RRC connection or
successful HO into the cell from an adjacent cell, and it is
ended with a UE context release or successful HO out of the
cell. At the end of the session the reason code of the release is
reported. Periodic reports are logged during the session every
1.28s containing various radio quality and traffic descriptors.
B. Session records
The essential variables are collected in session records
as shown in Table I. There is one variable of interest, the
release category that is derived from the release reason code.
There are 20 different reason codes, half of them indicating
normal release and the other half indicating abnormal release
(drop). Each session can have only one reason code. The other
variables are contained in the periodic reports and have a time
evolution within the session.
The variable to predict is release category that is a binary
variable indicating session drop. The variables contributing
most to the session drops are selected from a larger set. It
contains downlink and uplink parameters. Channel Quality
Index (CQI) ranging from 1 to 15 characterizes the quality
of the radio channel in downlink direction. Error correction
and retransmission mechanisms are operating on different
layers of the radio protocols. The retransmission ratio of
TABLE I. OVERVIEW OF A SESSION RECORD.
Variable Range Comment
release
category
0 (no drop), 1
(drop)
Derived from the release cause
cqi avg 1–15 Channel Quality Index
harqnack dl 0–1 HARQ NACK ratio in downlink
harqnack ul 0–1 HARQ NACK ratio in uplink
rlc dl 0–1 RLC NACK ratio in downlink
rlc ul 0–1 RLC NACK ratio in uplink
sinr pusch -4–18 Signal to Interference plus Noise Ratio
on Uplink Shared Channel
sinr pucch -13–3 Signal to Interference plus Noise Ratio
on Uplink Control Channel
hybrid automatic repeat request (HARQ) and radio link control
(RLC) protocols are reported periodically for both downlink
and uplink direction. Signal to Interference plus Noise Ratio
on Uplink Shared/Control Channel (sinr pusch/sinr pucch)
characterizes the quality of the uplink shared/control channel.
The sinr pucch having a constant value in almost the whole
dataset, has been removed from the analysis.
C. Time evolution of the variables
The values of the essential variables preceding the end of
session have most impact on the release category. However,
1 or 2 seconds before the drop the session is already in a
state where the quality is extremely low, making the service
unusable. Fig. 2 shows examples for sessions with normal
and abnormal release. In the dropped session the sinr pusch
decreases and the HARQ NACK ratio increases, indicating
uplink problem.
The objective of this paper is to provide a ML method to
predict the release category (drop or no-drop) of the session
based not only on the features measured directly preceding
the end of session but also the time evolution of the features.
We consider each session record as a set of time series
for the six technical parameters, along with a label of drop
or no-drop. For each of the time series, we compute five
statistical attributes: minimum, maximum, most frequent item
(mode), mean, variance and for each, we compute the gradient.
Overall, we obtain a statistical descriptor for a session with 60
attributes: for six time series, we have five statistics and for
each, we also have the gradient.
III. MACHINE LEARNING METHODS
In this section we first give an overview of AdaBoost, our
baseline method also used in [2]. Then we describe our new
method that aggregates the Dynamic Time Warping time series
distance [7] of the six measurement series corresponding to
each radio bearer session by an appropriate Support Vector
Machine kernel [6]. Finally we describe our evaluation metrics.
A. AdaBoost
AdaBoost [8] is a machine learning meta-algorithm that
“boosts” base learners by computing their weighted sum.
In each iteration, subsequent weak learners are trained by
increasing the importance of the LTE session samples that were
misclassified previously.
Our base learners consist of single attributes with a thresh-
old called decision stump. For example, a stump can classify
sessions with maximum uplink RLC NACK ratio above certain

Fig. 2. Typical examples for time evolution of no-drop and drop scenarios.
value as drop, otherwise no drop. We use the AdaBoost
implementation of Weka [9] for performing the experiments.
B. Time Series
By an extensive comparative study of time series classifi-
cation methods [4], the overall best performing time series
distance measure is the Euclidean distance of the optimal
“dynamic” time warping (DTW) of the two series [5]. Next
we define DTW.
Our time series consist of discrete periodic reports. If the
length of two series X = (x1, . . . , xn) and Y = (y1, . . . , yn)
is identical, we can define their Euclidean distance as
L2(X, Y ) =
n
i=1(xi − yi)2. (1)
By Dynamic Time Warping (DTW), we may define the dis-
tance of series of different length. In addition, DTW warps
the series by mapping similar behavior to the same place.
For example, peaks and valleys can be matched along the
two series and the optimal warping will measure similarity
in trends instead of in the actual pairs of measured values. For
illustrations of DTW and Euclidean distance, see [5], [4].
The optimal warping is found by dynamic programming.
Let the distance of two single-point series be their difference,
DTW((x1), (y1)) = |x1 − y1|. The DTW of longer series is
defined recursively as the minimum of warping either one or
no endpoint,
DTW2
((x1, . . . , xn), (y1, . . . , ym)) = (2)
min (DTW2
((x1, . . . , xn−1), (y1, . . . , ym−1)) + (xn − ym)2
,
DTW2
((x1, . . . , xn−1), (y1, . . . , ym)),
DTW2
((x1, . . . , xn), (y1, . . . , ym−1)))
C. SVM and our Similarity Kernel
The DTW distance in the previous subsection can be used
for classifying time series by any distance based method, e.g.
nearest neighbors [4]. In our problem of predicting mobile
sessions, however, we have six time series and for a pair of
sessions, six distance values need to be aggregated. In addition,
we would also like to combine time series similarities with
similarity in the statistical features.
A natural idea to handle distances of pairs of observation
is to use kernel methods. A kernel acts as an inner product
between two observations in certain large dimensional space
where Support Vector Machine, a form of a high dimensional
linear classifier, can be used to separate the data of dropped and
normal sessions [6]. Under certain mathematical conditions,
we have a freedom to define the kernel function by giving the
formula for each pair of observations.
In order to combine the six distance functions and the
statistical features for classification, we use our idea [10] to
randomly select a set R of reference sessions and characterize
all sessions by their distance from each session in R. For
each session s, we obtain 6|R| distances from the pairs of
the six measurement time series for s and the elements of
R. By considering the statistical parameters, we may obtain
|R| additional Euclidean distance values between the statistical
parameters of s and elements of R, resulting in 7|R| distances
overall. We use the methods of Jaakkola and Haussler [11]
to derive the natural distance metric over the distances. By
calculations omitted from this paper, we may obtain that the
natural kernel (Fisher kernel) over the 6|R| or 7|R| distances is
the linear kernel over the standard normalized distances. The
mean and the variance of the distances can be approximated
by the training data.
Over the final feature set, we use SVM for classification
[6]. We use LibSVM [12] for training the SVM model.
D. Classification quality measures
The quality of session drop prediction can be evaluated
via the class confusion matrix that has four elements cor-
responding to true drop, true no-drop for the correctly and
falsely predicted drop and falsely predicted no-drop for the
incorrectly classified sessions. By standard terminology, drop
is the positive and no-drop is the negative case and hence we
abbreviate the four cases, in order, by TP, TN, FP and FN.
As basic metrics, we use the Recall or True Positive Rate =
TP/(TP+FN) and False Positive Rate = FP/(TN+FP).
Our main metric for evaluation is derived from the Receiver
Operator Curve (ROC) that plots the True Positive Rate as the
function of the False Positive Rate by varying the decision
threshold. An example ROC curve is shown in Fig. 3. The
Area Under the ROC (AUC) [13] is a stable metric to compare

Fig. 3. The Receiver Operator Curve (ROC) of the best predictor for session
drop five periodic reports before the actual session drop occurs.
TABLE II. SIZE OF THE SESSION DROP EXPERIMENTAL DATA SET.
full no drop, sample drop
All time series 27.4M 210,000 210,000
At least 15 measurement points 2.8M 23,000 27,440
different ML methods since it does not depend on the decision
threshold. As an intuitive interpretation, AUC is the probability
that a uniformly selected dropped session is ranked higher in
the prediction than a uniformly selected no-drop session.
IV. EXPERIMENTAL RESULTS
Our data consists of 210K dropped and 27.2M normal
sessions. To conduct our experiments over the same data for
all parameter settings, we consider sessions with at least 15
periodic reports as summarized in Table II. Part of our exper-
iments are conducted over a sample of the normal sessions.
We consider the number of periodic report measurements
both before and after the prediction. Data before prediction
may constitute in building better descriptors. On the other
hand, if we take the last k periodic reports before drop or
normal termination, the prediction model is required to look
farther ahead in time, hence we expect deterioration in quality.
Another parameter of the session is the duration till prediction:
very short sessions will have too few data to predict.
Overall, we observe best performance by the DTW based
similarity kernel method, followed by the baseline AdaBoost
over statistical descriptors. For all practically relevant param-
eters, DTW improves the accuracy of AdaBoost by at least
5% over the sample as in Table II. Over the full data set,
performance is similar: AUC 0.891 for AdaBoost and 0.908
for DTW, with five periodic reports before session termination
and at least ten before the prediction. Note that each descriptor
needs its own machine learning method: time series with
AdaBoost and statistical descriptors with SVM perform poor.
The possible typical physical phenomenon behind drop can
be explained by considering the output model parameters. The
best features returned by AdaBoost are seen in Table III. We
observe that the most important factor is the increased number
of packets retransmitted, most importantly over the uplink
control channel followed by HARQ over the downlink. Other
natural measures as the CQI or even SINR play less role.
In order to see how early the prediction can be made, the
performance as the function of the number of periodic reports
TABLE III. BEST FEATURES RETURNED BY ADABOOST.
drop no drop weight
score score
rlc ul max 0.93447 0.12676 1.55
rlc ul mean 0.11787 -0.11571 0.44 (twice)
harqnack dl max 0.02061 0.00619 0.29; 0.19
d/dtrlc ul mean 0.19277 0.18110 0.24
sinr pusch mean 1.92105 6.61538 0.33
Fig. 4. Performance of early prediction.
before session drop or normal termination is given in Fig. 4.
The figure shows the accuracy of early prediction: we observe
that we can already with fairly high quality predict drop five
measurements, i.e. more than 7 seconds ahead. Regarding the
necessary number of observations before prediction, in Fig. 5
we can see that already the first measurement point gives an
acceptable level of accuracy. Beyond three reporting periods,
most methods saturate and only the DTW based similarity
kernel shows additional moderate improvement.
The computational time of feature extraction and the pre-
diction depends linearly on the number parameters of the
methods, typically in the range of 1–5 ms per session.
V. SON ASPECTS
For online sessions, several drop avoidance or mitigation
mechanisms are proposed in [2], [3], e.g. coordination between
voice and data transmissions, switching to other RAT, content
pre-fetching, automatic re-connection. However, it is challeng-
ing to perform successful actuation on an ongoing session real-
time, especially when the session is already in a bad condition.
The time for the action may vary (1 - 10 seconds) depending
on the complexity of the mechanism. As it is shown in Fig. 4
the accuracy of drop prediction is higher if the model is trained
based on the features measured right before the end of session.
In a practical perspective of deploying a SON function it is
a tradeoff between accuracy and the time left to perform the
proper mitigation action.
Every action has its cost due to e.g. increased signaling
load, extra delay added to the normal process, misclassification
error, etc. On the other hand, if the drop is predicted correctly,
the successfully performed mitigation action has a gain. If the
costs and gains can be calculated in the same metric, then it
is possible to set up a gain matrix to represent the TP, TN, FP
and FN cases.
An example gain matrix is shown in Table IV. In this
specific model if the prediction result is no-drop and it is
correct (TN), then no action is taken and there is no gain

Fig. 5. Dependence of prediction performance on the number of observations.
TABLE IV. GAIN MATRIX OF A SON ACTION.
Predicted class
drop no-drop
Actual
class
drop +2 -2
no-drop -2 0
and no cost. However, if the prediction is no-drop and it is
incorrect (FN) or the prediction is drop and it is incorrect (FP)
then we have a cost for the incorrect classification (both set to
-2 in the matrix). An example for FP case is when the session
was unnecessarily forced to a lower RAT where the quality
of experience of the user decreased. An example for FN case
is when the session was dropped even though the model had
predicted another outcome. In case of correctly predicted drop
(TP) the overall gain is set to +2.
Once a predictor is up and running it has a certain realiza-
tion of the FPR and TPR values, indicated by one single point
on the ROC curve. Based on the gain matrix it is possible to
choose the optimal point of the predictor. Fig. 6 shows the
overall gain as the function of the false-positive rate that was
calculated from the ROC curve and the example gain matrix.
There is a clear optimum point (also indicated in Fig. 3) where
FPR=0.08 and TPR=0.83.
The gain matrix essentially expresses the network opera-
tor’s preference on (TPR, TNR, FPR, FNR) tuple. Knowing the
ROC curve of the predictor and the gain matrix the decision
threshold of the predictor can be set to an optimal point. For
example, if the false positive samples have higher cost then
the optimal FPR will be lower.
VI. CONCLUSIONS
High granularity measurements enable more accurate pre-
diction of session drops. A new machine learning approach is
shown where not only the aggregated value of the features but
the time evolution of the features is taken into account. It is
found that in most of the cases the session drop is caused by
uplink problem.
The new method outperforms those applied in the literature
so far. It is also reported how the accuracy changes if the
drops are predicted certain time before the end of session. This
enables a SON function using the predictor to balance between
how accurate or how early the prediction is performed. It is
shown how to optimize the predictor in order to get the highest
gain when a mitigation action is applied for sessions about to
be dropped.
Fig. 6. Gain optimization.
ACKNOWLEDGEMENTS
The first and third authors are supported from the grant OTKA NK
105645, the “Momentum - Big Data” grant of the Hungarian Academy
of Sciences and the KTIA AIK 12-1-2013-0037 project of the Hungarian
Government, managed by the National Development Agency, and financed by
the Research and Technology Innovation Fund.
REFERENCES
[1] “Subscriber and Equipment (S&E) Trace, 3GPP TS 34.421–423.”
[2] S. Zhou, J. Yang, D. Xu, G. Li, Y. Jin, Z. Ge, M. B. Kosseifi,
R. Doverspike, Y. Chen, and L. Ying, “Proactive call drop avoidance
in umts networks,” in Proc. INFOCOM. IEEE, 2013, pp. 425–429.
[3] N. Theera-Ampornpunt, S. Bagchi, K. R. Joshi, and R. K. Panta, “Using
big data for more dependability: a cellular network tale,” in Proc. 9th
Workshop on Hot Topics in Dependable Systems. ACM, 2013, p. 2.
[4] H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh,
“Querying and mining of time series data: experimental comparison
of representations and distance measures,” Proc. VLDB Endowment,
vol. 1, no. 2, pp. 1542–1552, 2008.
[5] D. J. Berndt and J. Clifford, “Using dynamic time warping to find
patterns in time series.” in KDD workshop, vol. 10, no. 16. Seattle,
WA, 1994, pp. 359–370.
[6] B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds., Advances in
kernel methods: support vector learning. Cambridge, MA, USA: MIT
Press, 1999.
[7] E. Keogh, “A decade of progress in indexing and mining large time
series databases,” in Proc. 32nd international conference on Very large
data bases. VLDB Endowment, 2006, pp. 1268–1268.
[8] Y. Freund and R. E. Schapire, “A desicion-theoretic generalization of
on-line learning and an application to boosting,” in Computational
learning theory. Springer, 1995, pp. 23–37.
[9] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning
Tools and Techniques, 2nd ed., ser. Morgan Kaufmann Series in Data
Management Systems. Morgan Kaufmann, June 2005.
[10] B. Z. Daróczy, D. Siklósi, and A. Benczúr, “Sztaki@ imageclef 2012
photo annotation,” in Working Notes of the ImageCLEF 2012 Workshop
at CLEF 2012 Conference. kn, 2012, pp. 1–6.
[11] T. S. Jaakkola and D. Haussler, “Exploiting generative models in
discriminative classifiers,” Advances in neural information processing
systems, pp. 487–493, 1999.
[12] C.-C. Chang and C.-J. Lin, LIBSVM: a library for
support vector machines, 2001, software available at
http://www.csie.ntu.edu.tw/ cjlin/libsvm.
[13] J. Fogarty, R. S. Baker, and S. E. Hudson, “Case studies in the use
of roc curve analysis for sensor-based estimates in human computer
interaction,” in Proc. of Graphics Interface 2005, ser. GI ’05: Canadian
Human-Computer Communications Society, 2005, pp. 129–136.

Machine Learning Based Session Drop Prediction in LTE Networks and its SON Aspects

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