=Paper=
{{Paper
|id=Vol-2525/paper11
|storemode=property
|title=Investigation radio resource control failure in LTE networks
|pdfUrl=https://ceur-ws.org/Vol-2525/ITTCS-19_paper_25.pdf
|volume=Vol-2525
|authors=Ivan Ashaev,Vladimir Fadeev,Artur Gaysin
|dblpUrl=https://dblp.org/rec/conf/ittcs/AshaevFG19
}}
==Investigation radio resource control failure in LTE networks==
https://ceur-ws.org/Vol-2525/ITTCS-19_paper_25.pdf
Investigation Radio Resource Control Failure in LTE Networks*
Ashaev Ivan Vladimir Fadeev Artur Gaysin
Kazan National Research Kazan National Research Technical Kazan National Research
Technical University University Technical University
named after A. N. Tupolev named after A. N. Tupolev - KAI named after A. N. Tupolev
- KAI Kazan, Russia - KAI
Kazan, Russia vladimir_fadeev1993@mail.ru Kazan, Russia
AshaevIP@stud.kai.ru AKGaysin@kai.ru
Abstract
In this paper the analyze of changing probability Radio Resource Control connection failure was conducted for short-
term and longterm period. The result of Time Series Decomposition and correlation between probability of failure and
active users is presented.
1 Introduction
Guarantee of quality of working network is very important for providers to offer the best service for users. Failure of
connection can happen on any level of LTE, and it is necessary to detect a failure, classify it and on base of this information
improve the stability. Failure on the level of Connection Setup is one of the main parameters of Key Performance Identifiers
to understanding of quality of network. In this paper analysis of KPI is conducted for failure connection for mobile users.
Data was provided by local operator with total amount of users about 300 thousands.
Radio Resource Control is a protocol which is used for transmission of a common Non Access Stratum (NAS) (for all
users) and dedicated NAS information (for certain user). The main function of Radio Resource Control (RRC):
- The sending of common broadcast information for UEs.
- RRC Connection Control which include paging, establishment, release or change RRC connection, integrity
protection and ciphering.
- Establishment, release or change Resource Blocks carrying user information.
- Management of handover procedure. Measurement and reporting.
The failure of RRC Connection Setup can occurs on different steps of RRC Connection Setup[1]. The possible failure
is shown on the Figure 1.
The errors of RRC protocol can be divided in several types:
- Type 1. ENodeB does not send RAR or RAR is lost. It could be connected with RAR is not transferred if BSβs
CPU is overloaded. Another reason is message was sent, but user did not get signal because it is too low on the
receivers side due path loss or not enough coverage of the cell [2].
- Type 2. UE receive Connection Reject after sending Connection Request. This type of message is transmitted
when eNodeB does not have necessary resources to serve UE or cell is overloaded.
- Type 3. Reestablishment procedure failure. RRC Connection Reestablishment Reject is used when eNodeB decide
to start reconnection with UE with new configuration, but UE doesnβt accept the new configuration. In the standard
ETSI TS 136 331 V15.3.0 (2018-10) is specified only 3 case of reestablishment: handover failure, reconfiguration
failure.
- Type 4. Failure due errors on RLC level. The failure of RRC Connection can be caused of bit or bytes error on
lower level.
____________
* Copyright Β© 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution
4.0 International (CC BY 4.0).
Figure 1: The cases of RRC failure [1]
These parameters affect on one cumulative KPI parameters - RRC setup access rate.
2 Analysis of Networks KPI
RRC Failure this is a percent unsuccessful RRC connection establishment. It is equated like ratio between
unsuccessful and total attempts connection establishment is multiplied on 100:
β _
π
π
πΆ_πΉπ΄πΌπΏππ
πΈ = 1 β β Γ 100 (1)
_
The statistic is provided by local operator during 3 years for each hour. Providerβs network is constructed on base
of hardware of vendor Huawei. On the Figure 2 the graphs of changing KPI during all time observation. In addition, there
is histogram of distribution the rate of RRC failure and approximation of Gaussian distribution is presented. The mean and
variance of distributions is shown in Table 1.
Table 1: Parameters of Approximation
Year Mean Variance
2016 0.2390 0.0478
2017 0.1475 0.027
1βst part of
0.1056 0.000709
2018
2βnd part of
0.19106 0.000739
2018
According to result we can see that the highest rate of RRC failure was in 2016, the lowest in 2017. Also, there
are several picks of high failure which, probably, connected with accidents in network. The frequency of high load became
lower, but the values is increasing. In 2018 we have two components because of some anomaly during 05.2018-11.2018.
To understand the reason of the growing we have to analyze not integer parameters like KPI, but amount of internal counts
inside hardware.
Figure 2: Measurements and histogram of distribution value with approximation
For analyze the trend and short term changing of failure the Time Series Decomposition was used. Time Series
Decomposition represents time series like a combination of 4 components [4]:
- Level β the mean value.
- Trend β changing of value in data set.
- Seasonality is a component characterized the short-term changing.
- Noise β random variation.
This method is contained two main models of representation of series β Additive and Multiplicative models. Additive
model is a linear because components are presented like a sum. Seasonality in this case has the same frequency.
π¦(π‘) = πΏππ£ππ + πππππ + ππππ ππππππ‘π¦ + ππππ π (2)
Multiplicative model suggest that the components are multiplied and has nonlinear behavior. Frequency of seasonality
can change.
π¦(π‘) = πΏππ£ππ β πππππππππ ππππππ‘π¦ β ππππ π (3)
The time series was decomposed according to the Additive model, because it has a clear behavior of the seasonality in 24
bins (1 day).
Seasonality part shows how probability of failure change during the average day (fig.3). The highest chance of
error of RRC connection is about 10-12 p.m. According to trend we can see that the highest failure probability during a
week in the Wednesday and Thursday. The time series was decomposed according to the Additive model, because it has a
clear behavior of the seasonality in 24 bins (1 day).
Figure 3: Result of Time Series Decomposition during one month
The Scattering is presented on Figure 4. The approximation was got by using linear regression. According to the
result we can see that in short-term perspective we can see the dependence between number of users and probability of
RRC connection failure.
For the long-term analyze the average values during a day was founded for each quarter of the year. After it Time
Series Decomposition was conducted for resulted data set. According to the result, the trend for 3 year period is decreasing
the mean amount of RRC Connection failure except the period from May 2018 to November 2018, after this the probability
is also falling (Figure 5).
Figure 4: Scattering and approximation by linear regression during one year
Figure 5: Result of Time Series Decomposition for average day for each quarter of the year
On the Scatter plot Figure 6 we can see that in long-term perspective the correlation between active users and
frequency of RRC Connection failure becomes less. Moreover, the highest amounts of failure is about average the number
of users.
Figure 6: Scattering between active users and RRC failure during one year
According to the correlation between numbers of active users and probability of RRC Connection failure, we can
suppose that the overloaded is not the main reason of RRC Connection failure. On the scatter plot for long-term the highest
rate of RRC failure on the average number of active users. It means that the probability of RRC failure is more affected of
coverage and signal strength, not overloaded. The interference between users in high loaded cell also can increase the
percent of unsuccessful RRC connection, but influence of this much lower than propagation loss.
Further analysis on a long-term sample of three years showed that there is a strong correlation between the number
of active users and RRC Connection failure with coefficient of 0.77. This may indicate the development of the network
following an increase in the number of subscribers. For more accurate and deep analyze of causes RRC Connection failure
the information of internal signalization and other radio channel parameters is necessary.
References
1. Ralf Kreher and Karsten Gaenger, β LTE Signaling, Troubleshooting and Performance Measurementβ, 2nd ed,
ETSCOUT β Mobile Access.
2. Harri Holma Antti Toskala Jussi ReunanenββLTE small cell optimizationβ, 2016 John Wiley & Sons.
3. LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification
(3GPP TS 36.331 version 15.3.0 Release 15).
4. Rob J Hyndman and George Athanasopoulos, βForecasting: Principles and Practiceβ, Monash University, Australia.