Novasinergia 2022 5(1), 17-30. https://doi.org/10.37135/ns.01.09.02 http://novasinergia.unach.edu.ec
Research article
Impact of backoff time on cellular IoT performance in massive
communication environments
Impacto del tiempo de backoff en el rendimiento de IoT celular en entornos de comunicación
masiva
Daniel Santander1, Karin Cicenia-Cárdenas , Fabián Astudillo-Salinas2, Juan Aranda3
1 Universidad Nacional de Chimborazo, Riobamba, Ecuador, 060104; dasantander.fie@unach.edu.ec
Quito, Ecuador, 170308; ciceniak@gmail.com
2 Universidad de Cuenca, Cuenca, Ecuador; 010107; fabian.astudillos@ucuenca.edu.ec
3 Universidad de La Sabana, Cundinamarca, Colombia; 250001; juan.aranda@unisabana.edu.co
*Correspondencia: juan.aranda@unisabana.edu.co
Citación: Santander, D., Cicenia-
Cárdenas, K., Astudillo-Salinas, F.,
& Aranda, J., (2022). Impact of
backoff time on cellular IoT
performance in massive
communication environments.
Novasinergia. 5(1). 17-30.
https://doi.org/10.37135/ns.01.09.02
Recibido: 14 diciembre 2021
Aceptado: 29 enero 2022
Publicado: 31 enero 2022
Abstract: This article evaluates the impact of backoff time through
the Backoff Indicator’s (BI) configuration on the random-access
channel (RACH) under different massive traffic scenarios
considering network performance metrics such as the probability
of successful access, the delay in access, and the average number of
preamble transmissions. A discrete-event simulation model of the
contention-based random-access procedure was developed using
MATLAB software. Based on the results, an optimal range of BI
values was characterized for each scenario through different
reliability conditions. It was observed that with a suitable
configuration of the RACH parameters, the performance of the
system network could reach a successful access probability more
significant than 85% with a moderate increase in the access delay.
It was concluded that dynamic modification of the backoff time
could alleviate channel congestion in delay-tolerant applications
with massive traffic.
Novasinergia
ISSN: 2631-2654
Keywords: Backoff time, cellular IoT networks, machine-type
communications, performance metrics, random access channel.
Copyright: 2022 derechos
otorgados por los autores a
Novasinergia.
Este es un artículo de acceso abierto
distribuido bajo los términos y
condiciones de una licencia de
Creative Commons Attribution
(CC BY NC).
(http://creativecommons.org/licens
es/by/4.0/).
Resumen: Este artículo evalúa el impacto del tiempo de backoff a
través de la configuración del Indicador de Backoff (BI) en el canal
de acceso aleatorio (RACH) bajo diferentes escenarios de tráfico
masivo considerando métricas de rendimiento de la red como la
probabilidad de acceso satisfactorio, el retardo en el acceso y el
número promedio de transmisiones de preámbulo. Se desarrolló un
modelo de simulación por eventos discretos del procedimiento de
acceso aleatorio basado en contención empleando el software
MATLAB. Con base a los resultados obtenidos, se caracterizó un
rango óptimo de los valores de BI para cada escenario a través de
diferentes condiciones de fiabilidad. Se observó que con una
configuración adecuada de los parámetros del RACH, el
rendimiento de la red sistema puede alcanzar una probabilidad de
acceso satisfactorio mayor al 85% con un aumento moderado en el
retardo de acceso. Se concluyó que la modificación dinámica del
tiempo de backoff puede aliviar la congestión del canal en
aplicaciones tolerantes a retardos con tráfico masivo.
Palabras clave: Tiempo de backoff, redes IoT celular,
comunicaciones tipo máquina, métricas de rendimiento, canal de
acceso aleatorio.
Novasinergia 2022, 5(1), 17-30 18
List of Symbols
Random-Access Channel
Base Station
Physical RACH
Machine-type Communication
Random-Access Opportunities
Massive MTC
Long-term Evolution
Human-type Communication
LTE-A
LTE Advanced
Internet of Things
Fifth Generation
3rd Generation Partnership Project
Call Detail Records
Cellular IoT
Preambles reserved for contention-based
RAP
Quality of Service
Total number of MTC devices
Random-Access Response
Total number of HTC devices
Random-Access procedure
Maximum number of Uplink grants per slot
1. Introduction
Today, we find ourselves in a world that moves beyond standalone devices into a
new technological age where virtually everything is connected. Machine Type
Communication (MTC) can be defined as the ubiquitous and automated interaction of
information between devices (computers, actuators, sensors, mobile devices, automobiles)
without human intervention within a shared network, also called the Internet of Things
(IoT) (Guo, 2021). This ubiquitous network collects information from the environments (i.e.,
interacts with the physical world). It provides information transfer services and emerging
applications using the existing Internet infrastructure (Tello-Oquendo, Lin, Akyildiz, & Pla,
2019a). It is expected that by 2023 there will be 14.7 billion MTC connections worldwide
(Cisco, 2020). The Third Generation Partnership Project (3GPP) has incorporated standards
in cellular networks for MTC (3GPP, TR 37.868, 2011). For this reason, cellular networks
such as Long-Term Evolution (LTE), LTE-Advanced (LTE-A), and fifth-generation (5G)
networks have been considered suitable to provide connectivity for MTC, a technology
called cellular IoT (CIoT). Among the main characteristics offered by the cellular network
for this type of communication is having an adequate and well-deployed infrastructure that
encompasses comprehensive coverage, large capacity, scalability, spectral efficiency, low
latency, security solutions, and guaranteed Quality of Service (QoS) (Dakhilallah, Othman,
Kamariah, & Mohd, 2020).
The diverse massive MTC (mMTC) services are electronic health care, electronic commerce,
consumer goods, electronic transport, and control systems. These services will exhibit
different traffic patterns, which combined with a large number of devices, makes the
problem of resource allocation very challenging (Dutkiewicz, Costa, Kovacs, & Mueck,
2017), resulting in collisions and even the blocking of connection requests on the Random-
Access Channel (RACH) (Kim, Lee, & Chung, 2018). However, not all devices are connected
all the time. They may be just receiving data, transmitting it, or being in an idle state (Sahoo,
Chou, Weng, & Wei, 2018). The standard mode for network access in cellular
communications is through the random-access procedure, from now on RAP, based on
contention (3GPP, TS 36.321, 2017). The device must perform this procedure as the first step
in acquiring initial access to network resources. This procedure is performed through the
RACH and consists of four stages: Preamble Transmission, Random Access Response
(RAR), Connection Request, Contention Resolution.
Novasinergia 2022, 5(1), 17-30 19
According to 3GPP standards, a backoff procedure is performed in the event of an access
failure or collision by a device, regardless of the cause (3GPP, TS 36.321, 2017). After this
backoff procedure is finished, the device will start the RAP again. This backoff procedure
computes a predetermined random time following a uniform distribution and the base
station (BS) communicates it to the devices by system information blocks. A collided device
must wait this time before retrying the RAP using a preamble. This time interval is called
the backoff time (TBO), and it is calculated based on an indicator called Backoff Indicator, from
now on BI, whose value varies between 0 to 960 ms.
Most of the studies carried out regarding the evaluation of the RACH performance (Ouaissa,
Benmoussa, Rhattoy, Lahmer, & Chana, 2016; Gursu, Vilgelm, Kellerer, & Reisslein, 2017),
a fixed value is assumed for the BI parameter, generally 20 ms. However, the BI value may
vary as specified in the 3GPP standards. Therefore, this research aims to assess the impact
of BI variation and the benefits of a dynamic configuration in different massive traffic
scenarios based on network performance metrics. Furthermore, the conducted evaluation
applies to LTE / LTE-A and 5G networks, whose grant-based RAP is similar.
A discrete-event simulation model is devised using MATLAB software. The RAP is
implemented, and the network performance is evaluated considering main metrics such as
the probability of successful access, access delay, and the average number of preamble
transmissions. Different mMTC traffic conditions are considered in the RACH by a traffic
model that emulates massive communication environments. This allows analyzing the
impact of the BI variation on the network performance through varying several RACH
parameters.
2. Methodology
2.1. Overview and Theoretical Considerations
In cellular networks such as LTE / LTE-A and 5G, the standard mode for network
access is via the contention-based RAP (3GPP, TS 36.321, 2017) that is performed in the
RACH. This research evaluates the impact of varying the BI on the network performance
through metrics such as the probability of successful access (Ps), the access delay (D), and
the average number of transmitted preambles (k). The results are obtained through the
configuration and variation of the RACH parameters, such as the periodicity of the random-
access opportunities (RAOs), the BI, and massive traffic scenarios. These results allow
determining a range of optimal BI values assuming reliability conditions based on the
periodicity settings of the RAOs, from now on TRAO, for each massive traffic scenario, to
comply with the 3GPP specifications (3GPP, TS 36.321, 2017).
The contention-based RAP is performed through the RACH and consists of four steps:
Preamble transmission, Random Access Response (RAR), Connection Request, and
Contention Resolution, as illustrated in figure 1 (Tello-Oquendo, et al., 2018a). The
periodicity of the RAOs, TRAO, is periodic sequences reserved in the uplink channel to
transmit access requests to the network. That is determined by the PRACH configuration
index (prach-ConfigIndex) (Pacheco-Paramo & Tello-Oquendo, 2020). Downlink and uplink
transmissions are organized into radio frames of 10 ms each. Each frame is divided into ten
Novasinergia 2022, 5(1), 17-30 20
equally sized subframes. The duration of each subframe is 1 ms. Moreover, each subframe
is further divided into two equally sized time slots; each slot is 0.5 ms. There is a total of 64
PRACH configurations available, starting from a minimum periodicity configuration of one
RAO for every two frames (every 20 ms) to a maximum of one RAO for each subframe
(every 1 ms) (3GPP, TS 36.211, 2020), as can be seen in figure 2.
Figure 1: Contention-based random-access procedure (Tello-Oquendo, et al., 2018a).
Figure 2: Radio frame structure with different PRACH index settings (3GPP, TS 36.211, 2020).
The backoff process is a collision control technique in the RACH, proposed by the 3GPP
standards in case of failure in access to the network (3GPP, TS 36.321, 2017). This control
technique is performed within the contention-based RAP. This procedure is applied to all
the devices that failed in the first attempt to access the network. After that, devices must
wait a predetermined time before retrying the RAP and be able to retry a preamble
Novasinergia 2022, 5(1), 17-30 21
transmission at the next available RAO. This time is known as the backoff time, TBO (ms),
and it is chosen randomly by the UEs according to equation (1):
( 1 )
where means uniform distribution, BI is the backoff indicator defined by the BS, and its
value varies from 0 to 960 ms. The BI value is sent in the RAR message (step 2), which is
read by all devices that sent a preamble in a previous RAO. That means that each device
that did not obtain confirmation with the RAR is considered a failed attempt (a collision
originated). Then, it uses the BI to compute the TBO before attempting to start from the first
step again (Vidal, Tello-Oquendo, Pla, & Guijarro, 2019). In the same way, it happens with
the devices that do not receive the contention resolution message (step 4), which guarantees
successful access to network resources. They compute the TBO before making another
attempt, starting again from the first step. Figure 3 illustrates how the backoff procedure is
applied in the RACH when a collision occurs in the steps mentioned above, respectively.
Figure 3: Description of backoff procedure. a) Collision in the first message when multiple devices use the same preamble
(preamble (i)) and it is not decoded; the backoff time is applied to make a new attempt. b) Collision in the third message
when multiple devices use the same uplink physical resources; the backoff time is applied to make a new attempt.
2.2. System Model
A discrete event simulation model was implemented in MATLAB software. The
procedure followed for the analysis is contemplated in five blocks, as described in figure 4.
The system model assumes a single cell or BS where both MTC and Human Type
Communication (HTC) devices coexist. That allowed obtaining the different network
performance metrics considering background HTC traffic. The value of each performance
metric is obtained as the mean of 100 simulation runs. Each simulation run uses a different
random seed and ends when all the UEs have completed their random-access procedure.
The RAP, jointly with the traffic model, is characterized in the “CIoT and MTC Traffic
Model” block. MTC traffic will use the model described in the specification (3GPP, TR
37.868, 2011), which follows a Beta distribution of parameters (3,4) for 10 seconds. For HTC
Novasinergia 2022, 5(1), 17-30 22
traffic, the studies (Tello-Oquendo, Pacheco-Paramo, Pla, & Martinez-Bauset, 2018b) were
taken as a reference, where they describe that HTC traffic follows a uniform distribution
over time. This traffic is based on data provided by the Call Detail Records (CDR) granted
by the telecommunications company “Telecom Italia,” whose average rate of access
requests arrivals per second was 55 during 10 min time.
Figure 4: Procedure for evaluation and results analysis.
Table 1 lists the parameters used throughout our evaluation and analysis. We assume two
PRACH configurations. The typical one, using the prach-ConfigIndex6, whose TRAO = 5 ms.
The second one, using the prach-ConfigIndex14, whose TRAO = 1 ms.
Table 1: Configuration of the random-access channel parameters.
Parameters
Configuration
Total number of devices MTC
10000, 15000, 20000, 25000, 30000
Total number of devices HTC
33000
Setting the PRACH Index (prach-ConfigIndex)
6, 14
RAOs periodicity
5 ms, 1 ms
Subframe Length (Subframe length)
1 ms
Preambles reserved for contention-based RAP
54
RAR window size
5 subframes
Maximum number of Uplink grants per subplot
3
Maximum number of preamble transmission attempts ()
10
Backoff indicator
(20 a 960 ms) variable
Contest Resolution Timer
48 ubframes
2.3. Performance Metrics
2.3.1. Successful access probability
The variable Ps denotes the successful access probability, and it is defined as the
relationship between the devices that successfully complete the contention-based RAP and
the total number of devices that entered the BS during the simulation time, as described in
equation (2).
(2)
Novasinergia 2022, 5(1), 17-30 23
where is the total number of MTC devices with successful access and is the total
number of MTC devices that entered the BS.
2.3.2. Access delay
It is denoted by variable D, and it is the time elapsed between the first access attempt
(preamble transmission) and the successful completion of the RAP, also considering the
delay generated by the BI, which is a function of the number of attempts made until
obtaining successful access. We start from a cumulative distribution function (CDF)
described in equation (3). This equation is used to obtain a probability distribution based on
the delay generated by each device after getting successful access to the network and the
total number of devices that completed the RAP, as follows:
(3)
where is the cumulative probability of “” delay expressed in ms and
is the set of MTC devices that obtained successful access in “n” time delay. The 95th
percentile is used for this metric since this percentile is closer to the maximum delay
generated.
2.3.3. Average number of preamble transmissions
It is defined by the variable and is the statistic of the number of transmissions that
a device uses a preamble until it can complete the RAP after its first failed attempt. To
evaluate this indicator, a CDF is first applied, described in equation (4), for each of the
transmitted preambles until the maximum number of attempts is reached ().
(4)
where is the cumulative probability of “” preamble transmission, is the set
of MTC type devices that successfully accessed the network in “n” preamble transmission.
Once the different probability data have been obtained, as the next step, the weighted
arithmetic mean is used, as described in equation (5), to calculate the mean or average value
of the number of transmissions necessary to successfully carry out the RAP and obtain
access to the network. This value will depend on the maximum number of preamble
retransmissions that is set ()
(5)
where is the transmitted preamble number and is the sum of the accumulated
probabilities in the different preambles.
3. Results
This section presents the simulation results to evaluate the impact that the different
BI values cause on the performance of the RACH based on the RAOs periodicity settings,
Novasinergia 2022, 5(1), 17-30 24
the different massive traffic scenarios, and the performance metrics mentioned above.
Figure 5a and figure 5b depict the successful access probability for the prach-ConfigIndex
configurations 6 and 14, respectively.
Figure 5: The successful access probability
based on different massive MTC traffic scenarios with different BI values
(20 to 960 ms). a) Random access opportunity periodicity setting of 5 ms (prach-ConfigIndex6). b) Random access
opportunity periodicity setting of 1 ms (prach-ConfigIndex14).
For the most critical scenario (30000 MTC devices), on the one hand, when the maximum BI
value is used (960 ms) with TRAO=5 ms, a successful access probability of 77% (Figure 5a) is
reached. On the other hand, when the maximum BI value with TRAO = 1ms is used, a
successful access probability of 89% is achieved (Figure 5b).
The access delay based on the 95th percentile for the 6 and 14 prach-ConfigIndex
configurations in the different massive traffic scenarios are shown in figure 6a and figure
6b, respectively. This performance metric indicates that the BI value is directly proportional
to the delay generated; the higher the BI value, the greater the delay caused. For example,
when making a comparison based on the most critical mass traffic scenario (30000 MTC
devices), it was determined that when TRAO = 1ms is used, a lower delay (around 8.98% less)
is achieved compared to the configuration with TRAO = 5ms. Then, it is suggested to use the
prach-ConfigIndex = 14 (i.e., TRAO = 1ms) when the traffic load in the RACH is high.
Novasinergia 2022, 5(1), 17-30 25
Figure 6: Access delay based on the 95th percentile for the different massive traffic scenarios. a) Random access
opportunity periodicity setting of 5 ms (prach-ConfigIndex6). b) Random access opportunity periodicity setting of 1 ms
(prach-ConfigIndex14).
The average number of preamble transmissions for the prach-ConfigIndex configurations 6
and 14 under different massive traffic scenarios are shown in figure 7a and figure 7b,
respectively. This performance metric indicates the number of attempts made by devices
that successfully access the network. The range used for transmissions is 0 to 10, the latter
being the maximum number of possible preamble retransmissions. The number zero
indicates that a device was successfully accessed on the first attempt and did not retransmit.
Figure 7: Average number of preamble transmissions (k) under different massive MTC traffic scenarios. a) Random access
opportunity periodicity setting of 5 ms (prach-ConfigIndex6). b) Random access opportunity periodicity setting of 1 ms
(prach-ConfigIndex14).
Novasinergia 2022, 5(1), 17-30 26
Based on the results presented above, two reliability conditions were assumed based on the
successful access probability and the most critical massive traffic scenario (30000 MTC
devices) to determine an optimal range of BI values, which guarantee the connectivity of
the devices to the network. The first condition will be for all BI values that obtain a
successful access probability greater than 75% when working with TRAO = 5ms (standard set).
The second condition will be for all BI values that obtain a probability of successful access
greater than 85% when working with TRAO = 1ms. Figure 8 illustrates the minimum value of
the backoff indicator that meets the reliability condition called BI* for each RAO periodicity
configuration in each massive traffic scenario.
Figure 8: Minimum BI values that meet the reliability conditions (BI*).
As can be observed in figure 8, when a configuration of prach-ConfigIndex = 14
is used, a broader range of massive traffic scenarios can be supported, and this is
understandable because the devices use all the frames to transmit, unlike when using a
setting of prach-ConfigIndex = 6 . These results show how the performance in
the cellular network could be affected when handling massive traffic, particularly in the
access delay. The determined ranges will be crucial for future research to dynamically
determine the optimal BI value around the massive traffic level connecting to the BS.
4. Discussion
The binary exponential backoff (BEB) technique discussed in Kwak, Song, & Miller
(2005) is a frequently used collision resolution mechanism. Kwak, Song, & Miller (2005)
presents analytical findings on throughput and medium access delay. Fan, Gao, Wang, &
Dong (2008) apply theoretical analysis to evaluate the performance of a different type of
backoff algorithm in IEEE 802.11 ad hoc networks. Finally, Zhang & Liu (1993) employed
recursive formulas to examine the multi-channel slotted Aloha algorithm to determine the
probability of packets being successfully transported via parallel channels.
On the other hand, many studies have been conducted to alleviate RACH congestion under
massive access scenarios. However, the backoff approach for RACH collision resolution has
been studied moderately. Seo & Leung (2011) examine multi-channel slotted Aloha
Novasinergia 2022, 5(1), 17-30 27
algorithms in the context of LTE and IEEE 802.16 based systems, combining the ideas
introduced in Kwak, Song, & Miller (2005) and Zhang & Liu (1993). In addition, Seo &
Leung, (2011) implies that at least one packet is sent in each slot. This assumption is no
longer valid under low traffic conditions, such as those found in LTE (3GPP, TS 36.211,
2020). Therefore, tolerating the vast access of M2M communications necessitates optimizing
cellular networks’ standard backoff mechanism.
This article studied the backoff procedure as a control technique to deal with collisions
originating in the random-access channel (RACH) with massive traffic. In addition, the
impact produced by varying the backoff indicator (BI) concerning the configuration of the
periodicity of RAOs was explored for different massive traffic scenarios. The successful
access probability was assumed to be the primary performance metric. Therefore, the study
focused on characterizing the BI values that meet the reliability conditions for both RAOs
periodicity configurations [5 ms (prach-ConfigIndex = 6) and 1 ms (prach-ConfigIndex = 14)].
As a result, an optimal range of BI values has been characterized for each type of massive
traffic scenario entering the BS. This range would allow compliance with the provisions of
the 3GPP standards corresponding to the random choice of a backoff time.
The value of BI has a significant impact on the RACH’s overall performance. A small BI
value, for example, permits devices to retry access after a short time, thereby increasing the
number of collisions during burst arrivals. A high value of BI, on the other hand, may
increase the successful access probability at the expense of access latency. As a result,
finding the appropriate BI value that maximizes access success probability while
maintaining a reasonable access delay is critical.
5. Conclusions
An evaluation study was conducted to analyze the impact of the BI variation on
network performance metrics such as the probability of successful access, access delay, and
the average number of preamble transmissions under different mMTC traffic conditions
using a discrete-event simulation model. A comparison was made considering the most
suitable configuration for the different massive traffic scenarios evaluated using the critical
configuration parameters of the RACH. An optimal range of BI values was determined
through reliability conditions that will comply with the provisions of the 3GPP standards.
It was observed that for the massive traffic scenarios of 10000 and 15000 MTC devices, any
of the two RAOs periodicity configurations could be implemented since both cases obtain a
Ps ≥ 0.98; while, for other massive traffic scenarios, the TRAO = 1ms setting should be used,
since it has a higher optimal BI range and a Ps ≥ 0.85. The performance metrics were also
compared in the most critical massive traffic scenario evaluated. The RAOs periodicity
setting of 1 ms was the most appropriate due to the better results obtained, and it can be
incorporated in delay-tolerant applications.
In conclusion, the access delay is increased to have a reliable transmission without
incorporating control techniques and considering only the backoff procedure—
consequently, the number of preamble transmissions increases. Therefore, using these types
of solutions is reasonable in delay-tolerant applications. This research can be used as a
Novasinergia 2022, 5(1), 17-30 28
starting point for future studies in which it is proposed to use mechanisms to update the
backoff time dynamically based on the mMTC traffic that enters the BS. These mechanisms
can be implemented using machine learning so that the network can adapt to stringent QoS
requirements.
Authors’ contributions
In accordance with the internationally established taxonomy for assigning credits to
authors of scientific articles (https://casrai.org/credit/). The authors declare their
contributions in the following matrix:
Acknowledgments
The authors acknowledge the contribution and review of Dr. Diego Pacheco-Páramo
in this study.
Conflict of Interest
The authors declare that there are no conflicts of interest of any nature.
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