Universidad Nacional de Chimborazo
NOVASINERGIA, 2018, Vol. 1, No. 2, junio-noviembre, (38-44)
ISSN: 2631-2654
https://doi.org/10.37135/unach.ns.001.02.04
Research Article
Extended Access Barring for handling massive Machine Type
Communication (mMTC) deployments
Restricci
´
on de Acceso Extendida para manejar despliegues masivos de
Comunicaciones Tipo M
´
aquina (mMTC)
Luis Tello-Oquendo
12
*, Jos
´
e Ram
´
on Vidal
2
, Vicent Pla
2
, Jorge Martinez-Bauset
2
1
College of Engineering, Universidad Nacional de Chimborazo, Riobamba, Ecuador, 060108
2
ITACA Institute, Universitat Polit
`
ecnica de Val
`
encia, Valencia, Spain, 46022; jrvidal@upv.es; vpla@upv.es;
jmartinez@upv.es
* Correspondence: luis.tello@unach.edu.ec
Recibido 30 octubre 2018; Aceptado 03 diciembre 2018; Publicado 10 diciembre 2018
Abstract:
Massive machine type communication (mMTC) has presented a promising moment
to generate powerful and ubiquitous connections that face plenty of new challenges.
Cellular networks are the potential solution owing to their extensive infrastructure
deployment, reliability, security, and efficiency. In cellular-based mMTC networks, the
random access channel is used to establish the connection between MTC devices and
base stations (eNBs), where the scalable and efficient connectivity for a tremendous
number of devices is the primary challenge. To deal with this, the Third Generation
Partnership Project (3GPP) has suggested the extended access barring (EAB) as a
mechanism for congestion control. The eNBs activate or deactivate EAB using a
congestion coefficient. In this paper, an approach to implementing the congestion
coefficient is presented so that EAB can operate thus handling congestion episodes
in mMTC scenarios. Moreover, the performance of EAB is examined under different
MTC traffic loads and paging cycle configurations concerning network key performance
indicators (KPIs). Numerical results demonstrate the effectiveness of the proposed
method to detect congestion episodes. Also, it is shown that increasing the value of
the paging cycle configuration influence on the network behavior under EAB.
Keywords:
Extended access barring (EAB), cellular networks, internet of things, performance
analysis, random access.
Resumen:
La comunicaci
´
on masiva tipo m
´
aquina (mMTC) ha presentado un momento prometedor
para generar conexiones potentes y ubicuas que enfrentan muchos desaf
´
ıos nuevos.
Las redes celulares son la soluci
´
on potencial debido a su amplio despliegue de
infraestructura, confiabilidad, seguridad y eficiencia. En las redes mMTC basadas
en comunicaci
´
on celular, el canal de acceso aleatorio se utiliza para establecer
la conexi
´
on entre los dispositivos MTC y las estaciones base (eNBs), donde el
principal desaf
´
ıo es la conectividad escalable y eficiente para una enorme cantidad
de dispositivos. Para hacer frente a esto, el Third Generation Partnership Project
(3GPP) ha sugerido la restricci
´
on de acceso extendida (EAB) como un mecanismo
para el control de la congesti
´
on. Las eNBs activan o desactivan EAB utilizando un
coeficiente de congesti
´
on. En este documento se presenta un enfoque para implementar
el coeficiente de congesti
´
on de modo que EAB pueda operar y as
´
ı manejar los episodios
de congesti
´
on en escenarios de mMTC. Tambi
´
en se examina el rendimiento de EAB
bajo diferentes cargas de tr
´
afico de MTC y configuraciones de ciclo de paginaci
´
on
en t
´
erminos de indicadores clave de rendimiento de la red (KPIs). Los resultados
num
´
ericos demuestran la efectividad del m
´
etodo propuesto para detectar episodios de
congesti
´
on. Adem
´
as se demuestra que el aumento del valor de la configuraci
´
on del
ciclo de paginaci
´
on influye en el comportamiento de la red bajo EAB.
Palabras clave:
Restricci
´
on de acceso extendida (EAB), redes celulares, internet de las cosas, an
´
alisis
de rendimiento, acceso aleatorio.
http://novasinergia.unach.edu.ec
1 Introduction
Internet of Things (IoT) is one of the most
transformative and disruptive technologies of the
upcoming wireless systems that has the potential
to change the world radically. It is predicted
that billions of heterogeneous IoT devices use
cellular connections by 2022 (Ericsson, 2017),
which empowers individuals and industries to
achieve their full potential. Machine type
communication (MTC) is becoming the dominant
communication paradigm for a wide range of
emerging IoT applications including health-care,
smart cities, smart grids, smart transportation, and
environmental monitoring. In these applications, a
vast number of devices are deployed in a specific
area to provide ubiquitous services with minimal (or
without) human intervention. Thus, the network has
to face an increased load and surges of MTC traffic.
The 5th generation (5G) cellular networks will
support this huge number of devices generating
sporadic small packets at random times. In this
context, the random access channel (RACH) is
used to start the communication sessions, aimed
at delivering this kind of traffic. The RACH
is accessed through a four-message handshake
contention-based procedure. First, the devices
(named UEs hereafter) wait to the next random
access opportunity (RAO) and sends a Msg1
using a randomly chosen preamble from a pool
of preambles. Msg1 is detected at the eNB if
the preamble is chosen by just one UE in the
current RAO; if not, a collision occurs. For each
detected preamble, the eNB sends a random access
response (RAR) message, Msg2, which includes
one uplink grant, from a few grants available.
Msg2 is used to assign time-frequency resources
to the UEs for the transmission of the connection
request. UEs that received an uplink grant send
their connection request message, Msg3, using
the resources specified by the eNB. Finally, the
eNB responds to each Msg3 transmission with
a contention resolution message, Msg4. The
interested reader is referred to (Tello-Oquendo
et al., 2018; 3GPP, 2017b,d,a) for further details.
A fundamental issue is the efficient management
of network resources in overload situations; they
are produced when many MTC devices react to
the same event generating mass concurrent data
and signaling transmission. As result network
congestion is engendered including both radio
access network congestion and signaling network
congestion as defined in (3GPP, 2017f). This may
cause intolerable delays, packet loss or even service
unavailability.
The 3GPP proposes the extended access barring
(EAB) as one mechanism to guarantee network
availability and help network to meet performance
requirements under such MTC load (3GPP,
2017e). EAB selectively restricts the UEs’
access attempts to the RACH. Each UE configured
for EAB is allocated an access class (AC) in the
range 0–9. When the network determines that
it is appropriate to apply EAB (using a congestion
coefficient), it barres all UEs except one in a given
set of ACs, and broadcasts a system information
block type 14 (SIB14) containing a 10-bit barring
bitmap. The barring is of simple on/off type, where
access to each AC is either allowed or not. EAB
may be effective whenever the congestion occurs
sparingly and during short periods of time (in the
order of several seconds). This fact goes in line
with the bursty traffic behavior of MTC described
in (3GPP, 2011).
In the literature, several studies address the
EAB mechanism. Some of them misinterpret
the EAB behavior or do not conform to 3GPP
specifications (Kim et al., 2017; Hwang et al.,
2016). On the other hand, studies such as (Phuyal
et al., 2012; Larmo & Susitaival, 2012; Cheng
et al., 2015; Toor & Jin, 2017) analyze EAB mainly
in terms of access success probability and access
delay. In such studies, a practical way to implement
the congestion coefficient remains unclear since the
number of preamble transmissions is not known at
the eNB.
In this paper, a realistic method to implement the
congestion coefficient is proposed for the proper
functioning of EAB. Then, a thorough performance
analysis of this mechanism is conducted and
the impact of the paging timing on the EAB
performance is evaluated. The main contributions
of this study are summarized as follows:
• The EAB scheme is implemented and evaluated
in massive MTC scenarios following the 3GPP
directives for this kind of studies.
• A method to estimate the congestion coefficient
from the number of used preambles at every
RAO is proposed, this number of used
preambles is effectively known at the eNB so
that our proposed solution conforms to the
network specifications (3GPP, 2017b,d, 2014,
2017e) and can be successfully integrated into
the system.
• The impact of the paging timing on the EAB
performance is evaluated. For doing so, a
realistic situation is considered in which the
http://novasinergia.unach.edu.ec 39
congestion coefficient is estimated as mentioned
above.
The rest of the paper is organized as follows.
Section 2 presents the EAB operation mode; then, a
method to compute the congestion coefficient used
by the network to turn on or off this mechanism
is proposed. Section 3 analyzes in-depth the
performance of EAB in terms of network key
performance indicators (KPIs) and evaluate the
impact of the paging timing. Finally, Section 4
draws the conclusions.
2 Extended Access Barring
Mechanism
In the following, the operation mode of EAB is
presented. Then, a method to implement the EAB
congestion coefficient es proposed for its proper
functioning.
2.1 EAB operation mode
EAB is activated when congestion is detected.
For this purpose, the 3GPP defines a congestion
coefficient (CC
W
) for a moving time-window of
W ms, as detailed in Section 2.2. With a periodicity
given by a modification period parameter, CC
W
s are
used to update the EAB state. If CC
W 1
for W 1 =
1000 ms exceeds 0.4, EAB is turned on and all ACs
except one are barred. From here, every time that
CC
W 2
for W 2 = 500 ms is under 0.4, barring state is
released for one AC. The release of ACs proceeds
in cyclic order until all ACs are unbarred. Then,
if CC
W 1
is under 0.2, EAB is turned off. Fig. 1
illustrate the state diagram of the operation mode
of EAB.
The SIB14 contains the bitmap of barred
ACs; the eNB broadcasts messages containing
SIB14s with a periodicity of T
SIB14
∈
{80, 160, 320, 640, 1 280, 2 560, 5120} ms (3GPP,
2017e). Every time the bitmap has to be changed,
the eNB notifies it to the UEs through a system
information change parameter contained in the
paging messages (3GPP, 2017g). Paging messages
are sent at specific radio frames and subframes,
namely paging frames (PF)s and paging occasions
(PO)s, within a paging cycle (T
P
). UEs in idle state
wake up at their respective PO and read the paging
message. UEs calculate their POs from their local
identifiers, in order that the POs of the different
UEs are distributed homogeneously throughout
T
P
(3GPP, 2017g). When a UE reads a paging
off
barring ACs
unbarring
AC
[CC
W 1
< T H
1
]
[CC
W 1
≥ T H
1
]
[CC
W 2
< T H
1
]
[CC
W 2
≥ T H
1
]
[CC
W 1
≥ T H
1
]
[CC
W 2
< T H
1
]
ACs barred
[CC
W 1
< T H
2
]
No ACs barred
EAB ACCESS CONTROL
Figure 1: State diagram of EAB operation mode.
MTC UE
init
EAB access
control
random
access
procedure
data [AC
barred]
data [AC
unbarred]
SIB14 [AC
unbarred]
SIB14 [AC
barred]
Figure 2: State diagram of the network with EAB access
control.
message with system information change set to on,
it reads the next message containing the SIB14. To
make sure that all UEs are notified of all changes
and have a chance to update their EAB information,
the modification period is set to the maximum of T
P
and T
SIB14
, and changes on the bitmap are notified
when they are produced but the SIB14 update is
delayed up to the next modification period. When
a UE has to access to the RACH, it checks its
barring state from the bitmap contained in the latest
updated SIB14 as illustrated in Fig. 2.
http://novasinergia.unach.edu.ec 40
2.2 Computing the congestion
coefficient
The CC
W
is defined as (WG2, 2012)
CC
W
= 1 −
nRAR
W
nPT
W
(1)
where nRAR
W
is the number of RARs sent during
W ms and nPT
W
is the number of preamble
transmissions during W ms. To calculate CC
W
and therefore update its value at every RAO, the
eNB would need to know the number of preamble
transmissions at each RAO. But in the commonly
assumed collision model defined by the 3GPP,
this number is unknown, because those preambles
transmitted by more than one UE are not decoded.
Therefore, the value of nPT
W
defined in (1) should
be estimated from the number of preambles used (by
at least one UE) at each RAO. This estimation was
obtained as follows.
Let Y
j
(i) ∈ {0, 1} be the random variable that
denotes the transmission of preamble j at
RAO(i) given that the total number of preamble
transmissions at RAO(i) is n
t
(i). Then, Y
j
(i) = 0
when the preamble j has not been transmitted by
any UE at RAO(i), and Y
j
(i) = 1 otherwise. Its
probabilities are
(
P{Y
j
(i) = 0} =
1 −
1
R
n
t
(i)
P{Y
j
( j) = 1} = 1 −
1 −
1
R
n
t
(i)
(2)
where R is the number of available preambles, and
E{Y
j
(i)} = 1 −
1 −
1
R
n
t
(i)
(3)
Then, the number of used preambles at RAO(i),
n
u
(i), is
n
u
(i) =
R
∑
j=0
Y
j
(i) (4)
and its expected value is
E{n
u
(i)} = R
"
1 −
1 −
1
R
n
t
(i)
#
(5)
Since n
u
(i) is known at the eNB, and assuming
that E{n
u
(i)} changes slowly, it can be estimated
from a short term time average of n
u
(i). Let ˆn
u
(i)
Figure 3: CC
W 1
operation. N
M
= 30000; (T
P
, T
SIB14
) =
(2560, 320) ms.
be an estimate of E{n
u
(i)} at RAO(i) obtained by
exponential smoothing of n
u
(i),
ˆn
u
(i) = α ˆn
u
(i − 1) + (1 − α)n
u
(i) (6)
with α < 1. Finally, from Eq. (5), the estimated
value of the number of transmitted preambles used
to calculate CC
W
is
n
t
(i) =
log
1 −
ˆn
u
(i)
R
log
1 −
1
R
(7)
Several simulations were conducted to check that
the values of CC
W
obtained using this estimator
are very close to those obtained using the real
number of preambles transmitted. Fig. 3 illustrates
an example of the values of CC
W 1
for W 1 =
1000 ms during a congestion episode induced
by the MTC traffic benchmark described in
Section 3 with N
M
= 30000 MTC UEs arrivals and
(T
P
, T
SIB14
) = (2560, 320) ms. The CC
W 1
obtained
from the estimated number of transmissions is
compared with the obtained from the exact value
of transmissions. As can be seen, the error in the
estimated CC
W 1
is minimal and can be used as
real-time congestion coefficient.
3 Performance Analysis
A single cell environment is assumed in which the
access requests of MTC UEs follow a Beta(3, 4)
distribution over a period of 10 s, according to the
traffic model 2 specified by the 3GPP in (3GPP,
2011). This traffic model can be seen as an extreme
scenario in which a vast number of MTC UE
arrivals (ranging from N
M
= 5000 to N
M
= 30000)
http://novasinergia.unach.edu.ec 41
occur in a highly synchronized manner (e.g., after
an alarm that activates them).
Three network KPIs were measured, namely the
probability to successfully complete the random
access procedure, P
s
; the mean number of preamble
transmissions needed by the UEs to successfully
complete the random access procedure, K; and
the access delay (mean and percentiles) of the
successful accesses, D. These KPIs are in
conformance to the 3GPP directives (3GPP, 2011)
to assess the efficiency of the LTE-A random access
procedure with MTC.
To obtain the above KPIs, a discrete-event simulator
was developed; it fully reproduces the behavior
of UEs, eNB, and RACH during the random
access procedure. A typical physical RACH
configuration, prach-ConfigIndex 6 (3GPP, 2017c),
is assumed where the subframe length is 1 ms
and the periodicity of RAOs is 5 ms. R = 54
out of the 64 available preambles are used for
the contention-based random access procedure and
the maximum number of preamble transmissions
of each UE, preambleTransMax, is set to 10.
Additional system configuration parameters can be
found in (Tello-Oquendo et al., 2018, Table III).
First, an overload situation produced when many
MTC devices (N
M
= 30000) attempt to access
the network is illustrated. Fig. 4 depicts the
total preamble transmissions per RAO, preambles
with collision (collided), and successful preamble
transmissions (no collided); no access control is
implemented. It can be seen that when N
M
= 30 000,
traffic model 2 leads to network congestion, as the
Beta(3, 4) distribution of UE arrivals exceeds the
physical RACH capacity [c(54) = 20.05 UE arrivals
per RAO as calculated using (Tello-Oquendo et al.,
2018, Eq. (4))] from the 343rd to the 1 329th RAO.
This massive number of UE arrivals results in a
congestion period of T
c
= 4.93 s, where up to 300
average preamble transmissions per RAO occur at
the 800th RAO. As a result, the average number
of successful accesses sharply decreases during
this period, and the access success probability is
severely affected: P
s
= 31.305 %.
In the following, how the standard EAB mechanism
handles congestion episodes as the one described
above is shown in detail. Fig. 5 plots the
temporal distribution of preamble transmissions
when EAB is in operation and (T
P
, T
SIB14
) =
({640, 1280, 2560}, 320) ms. It can be seen that,
when congestion builds up, at t ≈ 4 s, EAB is
enabled. Then, ACs start to be unbarred, one at a
time, with a periodicity of modification period = T
P
.
Every time that an AC is unbarred, a number of UEs
Figure 4: Temporal distribution of preamble
transmissions, collided preambles, and successful
preambles; traffic model 2, N
M
= 30 000.
access the RACH in bursts of periodicity T
SIB14
=
320 ms. Each burst corresponds to those UEs whose
POs are between two consecutive broadcasts of the
SIB14; these UEs access the RACH simultaneously
because they update their SIB14 simultaneously.
As the T
P
configuration value increases, the time
required to relieve a congestion episode is longer
(e.g., the network is able to handle N
M
= 30000
access requests in ≈ 37 s with T
P
= 2560 ms,
whereas ≈ 19 s are required with T
P
= 640 ms.)
In Fig. 6, the network KPI results are presented
for different configurations of T
P
. Considering
feasible parameter values in the LTE-A standard,
several configurations for the combination of
T
P
and T
SIB14
were tested and it was verified
that, as in (WG2, 2012; Cheng et al., 2015),
combinations in which T
P
< T
SIB14
, result in very
poor performance in terms of P
s
. Hence, the results
for T
P
∈ {640, 1280, 2560} ms and T
SIB14
= 320 ms
are shown.
Fig. 6a shows that P
s
increases as the T
P
duration
increases. This is because the greater the T
P
,
the greater the number of information updates per
T
P
, which results in lower intensity of the traffic
burst after each SIB14 update. However, the
cost of increasing T
P
is that ACs are unbarred at
a slower rate (one AC per T
P
), thus increasing
D as can be seen in Fig. 6c. In terms of K,
Fig. 6b illustrates that increasing T
P
reduces this
metric particularly in light-loaded MTC scenarios
(i.e., N
M
< 16 000) thus decreasing the energy
consumption; however, in heavy-loaded MTC
scenarios (i.e., 16 000 ≤ N
M
) this metric increases
gradually as T
P
increases. This is because the
http://novasinergia.unach.edu.ec 42
(a)
(b)
(c)
Figure 5: UE access attempts. (a) T
P
= 640 ms. (b) T
P
=
1280 ms. (c) T
P
= 2560 ms.
delay caused by the paging mechanism: long paging
cycle limits the performance of the barring phase.
As a result, some devices cannot receive the EAB
updated information on time and their accesses will
probably fail; therefore, they should start the access
attempt again.
To sum up, increasing T
P
rises P
s
at the cost of
longer access delay while diminishing the number
of preamble transmissions in light-loaded MTC
scenarios which translates in energy savings for
(a)
(b)
(c)
Figure 6: Key performance indicators. (a) Successful
access probability. (b) Preamble transmissions. (c)
Access Delay.
power-constrained MTC devices.
4 Conclusion
In this paper, a practical method to implement the
congestion coefficient used by extended access
barring (EAB) was proposed. It allows EAB
http://novasinergia.unach.edu.ec 43
functioning properly for handling congestion in
massive machine-type communication (mMTC)
scenarios. Through extensive discrete-event
simulations, the EAB scheme was analyzed
using our proposed congestion coefficient
implementation. Then, the impact of the paging
timing on the performance of EAB was studied.
Numerical results show that a limiting factor of
EAB as defined by the 3GPP is that, when a
barred AC is released, its UEs initiate their access
procedure in bursts of periodicity T
SIB14
. These
bursts cause many preamble collisions during the
first RAOs, deteriorating overall performance. On
the other hand, increasing the paging cycle rises
the successful access probability at the cost of
longer access delay while diminishing the number
of preamble transmissions in light-loaded MTC
scenarios. This results in energy savings useful
for the rather power-constrained devices in MTC
applications.
Interest Conflict
Authors declare that there is no conflict of interest
in this research.
Acknowledgment
This work was supported in part by the Ministry
of Economy and Competitiveness of Spain
under Grants TIN2013-47272-C2-1-R and
TEC2015-71932-REDT.
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