=Paper=
{{Paper
|id=Vol-2946/paper-07
|storemode=property
|title=
Model of radio admission control for URLLC and adaptive bit rate eMBB in 5G network
|pdfUrl=https://ceur-ws.org/Vol-2946/paper-07.pdf
|volume=Vol-2946
|authors=Anna Kushchazli,Anastasia Ageeva,Irina Kochetkova,Petr Kharin,Alexander Chursin,Sergey Shorgin
|dblpUrl=https://dblp.org/rec/conf/ittmm/KushchazliAKKCS21
}}
==
Model of radio admission control for URLLC and adaptive bit rate eMBB in 5G network
==
https://ceur-ws.org/Vol-2946/paper-07.pdf
Model of radio admission control for URLLC and
adaptive bit rate eMBB in 5G network
Anna Kushchazli1 , Anastasia Ageeva1 , Irina Kochetkova1,2 , Petr Kharin1 ,
Alexander Chursin1 and Sergey Shorgin2
1
Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow, 117198, Russian
Federation
2
Institute of Informatics Problems, Federal Research Center “Computer Sciences and Control” of the Russian Academy of
Sciences, 44-2 Vavilova St, Moscow, 119333, Russian Federation
Abstract
In today’s rapidly developing telecommunications technologies, mobile communication services are
widely penetrating into all segments of society. The 5th generation network (5G) will cover a broader
range of usage scenarios – enhanced mobile broadband (eMBB), ultra-reliable low-latency communica-
tions (URLLC), and massive machine-type communications (mMTC). This paper considers a model of
joint service of URLLC and eMBB traffic within a single base station. The first type of traffic is supposed
to have priority due to its high latency requirements, while the second type of traffic has a high speed.
If there are no available resources to serve URLLC traffic, the allocated resources for eMBB traffic will
decrease until they are completely withdrawn. We model the above mentioned system as a queuing
system with the bit rate degradation and service interruption.
Keywords
5G, URLLC, eMBB, radio admission control, priority, interruption, bit rate degradation, queuing system,
1. Introduction
The life of society has been rapidly developing due to mobile communications, that has become
an integral part of the everyday life of each person in society. Due to the rapid increase in
the number of users and devices connected to the network, there is the growth of the load on
communication networks. At the same time, network delays should be reduced and occur as
rarely as possible.
The paper addresses the usage scenarios in 5G networks and joint service of eMBB and
URLLC traffic. We apply methods of queuing theory and mathematical teletraffic theory. The
research tasks are the following:
1. to analyze works related to joint transmission of eMBB and URLLC traffic;
2. to build a mathematical model with adaptive change in the speed of eMBB traffic;
3. to derive the probabilistic-temporal characteristics of the model.
Workshop on information technology and scientific computing in the framework of the XI International Conference
Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems (ITTMM-2021),
Moscow, Russian, April 19–23, 2021
Envelope-Open aikushch@yandex.ru (A. Kushchazli); anastasia.ageeva.it@gmail.com (A. Ageeva); gudkova-ia@rudn.ru
(I. Kochetkova); gruzavjeg@mail.ru (P. Kharin); chursin-aa@rudn.ru (A. Chursin); sshorgin@ipiran.ru (S. Shorgin)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
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Anna Kushchazli et al. CEUR Workshop Proceedings 74–84
The paper is organized as follows. In Section 2, the characteristics of the coexistence of
serving narrow-band URLLC and broadband eMBB traffic are explained. Section 3 presents the
system model and probabilistic-temporal attributes for it. Furthermore, Section 4 shows us the
numerical analysis and the discussion. As a result, the conclusions are in Section 5.
2. State of The Art
2.1. 5G Usage Scenarios
According to ITU-R recommendation [1], 5G systems must support ultra-low latency and high-
reliability communication systems for both users and devices. The quality of service in 5G
networks should not degrade under conditions of high system load due to many active users.
The 5G usage scenarios include:
• Enhanced Mobile Broadband (eMBB): Mobile broadband addresses the human-centric
use cases to access multi-media content, services, and data. This type of scenario come
with new application areas and requirements in addition to existing mobile broadband
applications for improved performance and an increasingly seamless user experience;
• Ultra-reliable and low latency communications (URLLC): This use case has stringent
requirements for capabilities such as throughput, latency, and availability. Some exam-
ples include wireless control of industrial manufacturing, medical surgery, distribution
automation in a smart grid, transportation, etc.;
• Massive machine-type communications (mMTC): This use case is characterized by a huge
number of concurrent users usually transmitting a relatively small amount of data that is
not sensitive to latency.
It should be noticed that additional use cases are expected to emerge, which are may currently
not foresee. Nevertheless, 5G network will encompass many different features. Figure 1
illustrates some examples of envisioned usage scenarios.
2.2. Joint Scheduling of URLLC and eMBB
Since both eMBB and URLLC are essential components of communication traffic in 5G networks,
various studies have looked at the coexistence of these services. In terms of network bandwidth,
eMBB generates a massive amount of data traffic. Unlike eMBB, URLLC produces fewer data
because of its stringent latency and reliability requirements. Consequently, the coexistence of
these two services is associated with achieving the sufficient eMBB throughput while meeting
the URLLC requirements. Due to the time sensitivity of critical applications such as UAV
automation, autonomous vehicle control, and critical medical equipment management, URLLC
takes precedence over eMBB for scheduling. Typically, eMBB scheduling involves increasing
the network capacity to improve the spectral efficiency, while the packet delivery reliability is
ensured through re-transmissions. However, eMBB scheduling approaches may not guarantee
the reliability and latency thresholds required for URLLC and thus cannot be applied in URLLC
scheduling. In contrast, URLLC involves the transmission of short packets with specified latency
and reliability margins.
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URLLC
Self
VR/AR
driving car
Industry Cloud
automation computing
5G Voice
Smart city
Gigabytes
mMTC Smart house in a second eMBB
3D video
Medicine UHD screens
Figure 1: 5G usage scenarios
By the way, 3GPP has proposed a short and long transmission within the time interval
(TTI) frame allocation for these coexistence scenarios. In this frame allocation, eMBB traffic is
scheduled for a long TTI, and URLLC is automatically scheduled for a short TTI over existing
eMBB traffic by adopting a puncture or overlay scheme [2].
Exceptionally, the authors of [3] propose an efficient scheduling scheme for the coexistence
of eMBB and URLLC by dynamically applying to puncture or overlay schemes. The base station
performs eMBB scheduling at the start of a long TTI, and URLLC scheduling is performed for a
short TTI using a puncture or overlay scheme.
Regarding that paper, we consider that an incoming eMBB session arrives and takes the
whole slot while URLLC traffic takes a mini-slot. Indeed, one PRB equals one slot, and the
duration of it is 5 ms. We will describe the model with an adaptive change of eMBB traffic rate
in more detail in the next section.
2.3. Related Works
There are several approaches to URLLC and eMBB coexistence. So [4] proposes the resource
reservation for URLLC traffic. In [5], network slicing is used for both heterogeneous orthogonal
multiple access (H-OMA) and heterogeneous non-orthogonal multiple access (H-NOMA), which
was also studied in [6] and [7]. In addition to URLLC priority access , the authors of [8] and
[9] emphasize the eMBB quality of service, they use the methods of stochastic geometry and
queuing theory. A feature of paper [10] is a queuing system with random requirements.
In [11, 12], the authors consider the URLLC priority access with eMBB session interruption,
and in [13] we analyzed eMBB session delay. This paper comparing to [11, 12] also proposes a
preliminary transmission speed reduction of the eMBB session before its interruption.
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Figure 2: eMBB bit rate degradation and service interruption.
3. Queuing Model
3.1. Assumptions and Parameters
Let us consider a model where resources are allocated within one base station to service URLLC
and eMBB traffic. eMBB traffic requests are distributed by the scheduler to the resource units
of each slot. When a URLLC request is received, it can be assigned to any free resource unit
of any subsequent mini-slot due to the delay requirements. Furthermore, the structure of the
frame is presented in Figure 2.
Sessions of eMBB traffic occupy one resource block or 𝑏1 resource units (RU), while URLLC
sessions occupy one resource unit. There are 𝑁 resource blocks in the system, i.e. the maximum
number of eMBB sessions. Then the maximum number of URLLC sessions in the system is
𝐶 = 𝑏1 ⋅ 𝑁.
Sessions of both traffic arrive according to the Poisson process, and the arrival rates are equal
to 𝜆𝑚 and 𝜆𝑢 respectively. An eMBB session occupies the maximum speed. However, if all the
resources of the system are busy when a URLLC session arrives, the rate of a eMBB session can
be reduced since the second traffic is in priority. At the same time, the maximum speed will
first decrease, and then the lower ones in descending order. If several applications are served at
the same speed, then the choice is made randomly.
Let us denote 𝑛 the total number of URLLC sessions active at a certain moment of time. To
designate the number of eMBB sessions, we introduce the vector 𝑚 ⃗ = (𝑚1 , .., 𝑚𝐾 ), where 𝐾 the
number of speeds at which eMBB sessions can be serviced, equal to the number of resource
units 𝑏1 . Also, we will use a service rate vector ⃗𝑏 = (𝑏1 , .., 𝑏𝐾 ) such that 𝑏1 > 𝑏2 > ... > 𝑏𝐾 . Then
the state of the system will take the form (𝑚1 , ..., 𝑚𝐾 , 𝑛) = (𝑚, ⃗ 𝑛) = 𝑥.
⃗ Since the requests are
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Table 1
System parameters
Parameter Description
𝜆𝑚 / 𝜆𝑢 Session arrival rate of eMBB / URLLC
𝜇𝑚 −1 / 𝜇𝑢 −1 Average eMBB / URLLC session duration
𝑏1 Number of resource units
𝑁 Total number of resource block
𝐶 = 𝑏1 𝑁 Total number of resource units
𝑚
⃗ = (𝑚1 , .., 𝑚𝐾 ) Number of eMBB active sessions on K speed
𝑛 Number of URLLC active sessions
⃗𝑏 = (𝑏1 , .., 𝑏𝐾 ) eMBB session service speed
Figure 3: Model scheme
served according to an exponential distribution, the eMBB service rate will be 𝜇𝑚 , and URLLC –
𝜇𝑢 . All the main parameters of the system used in this work are presented in Table 1.
3.2. Admission Control and State Space
The scheme of the described model is presented in Figure 3. The system state space is:
(𝑚1 , … , 𝑚𝐾 , 𝑛) ∶ 𝑛 ≥ 0, 𝑚𝑘 ≥ 0, 𝑘 = 1, … , 𝐾 ,
⎧ ⎫
𝒳 = 𝐾 𝐾 (1)
⎨ ∑ 𝑚𝑘 ≤ 𝑁 , 𝑛 + ∑ 𝑏𝑘 𝑚𝑘 ≤ 𝐶 ⎬
⎩ 𝑘=1 𝑘=1 ⎭
If the system is in a state (𝑚,⃗ 𝑛), then various events are possible to occur with different
intensities. All possible situations are presented in Table 2. For the convenience of describing
transitions, we introduce the unit vector 𝑒⃗𝑘 = (0, ..., 0, 1, 0, ..., 0), where 1 is at the 𝑘 𝑡ℎ place.
First, consider the possible situations occurring with the intensity 𝜆𝑚 , i.e. when a new request
eMBB arrives:
• If the system has free resources, namely a free resource block, the request will be accepted
for service. In Table 2, this transition from the central state is presented as number 1.
• If there are no free resources in the system, then the session will be blocked.
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In the case when an eMBB session service ends, i.e. an event with intensity 𝜇𝑚 which is
presented as under number 2 from Table 2 occurs, the request leaves the system, and the
resources are released.
We turn to events that occur with intensity 𝜆𝑢 , i.e. upon an receipt of the URLCC application:
• If the system has at least one free resource unit, the session is accepted for service. In
Table 2 it is presented as number 3.
• If there are no free resources, but at least one eMBB session served not at the minimum
speed, then the service speed of the eMBB session decreases, and the URLLC session is
accepted for service. In Table 2 it is displayed as number 4.
• If there are no free resources, but at least one eMBB session served at the minimum
speed, then an eMBB session service is interrupted, and the URLLC session is accepted
for service. In Table 2 this is confirmed as number 5.
• If there are no free resources and no eMBB sessions in the system, the session will be
blocked.
When a URLLC session leaves the system, events occur with an intensity 𝜇𝑢 :
• If there are no eMBB sessions in the system or they are served at the maximum speed,
then the URLLC session leaves the system and frees up resources. In Table 2 it is presented
as number 6.
• If the system has at least one eMBB session that is not served at full speed, then after
the termination of the URLLC session service, the speed of the active eMBB session is
restored. In Table 2 it is shown as number 7.
3.3. Performance Measures
Now let us talk about performance indicators of priority service of URLLC traffic. Having formed
an infinitesimal generator matrix and solving the resulting system of equilibrium equations, we
can find the stationary probability distribution 𝑝(𝑚,
⃗ 𝑛), (𝑚,
⃗ 𝑛) ∈ 𝒳 . Based on it, we obtain the
following probabilistic-temporal characteristics of the model:
1. Average number of eMBB sessions 𝑚𝑘 and URLLC sessions 𝑛:
𝑚𝑘 = ∑ 𝑚𝑘 ⋅ 𝑝 (𝑚1 , … , 𝑚𝐾 , 𝑛), 𝑘 = 1, … 𝐾 ; (2)
𝑥∈𝒳
⃗
𝑛 = ∑ 𝑛 ⋅ 𝑝 (𝑚1 , … , 𝑚𝐾 , 𝑛); (3)
𝑥∈𝒳
⃗
2. Blocking probability of eMBB sessions 𝐵𝑚 and URLLC sessions 𝐵𝑢 :
𝐾
𝐵𝑚 = ∑ 𝑝 (𝑚1 , … , 𝑚𝐿 , 𝑛), ℬ2 = {𝑥⃗ ∈ 𝒳 ∶ 𝑛 + ∑ 𝑏𝑘 𝑚𝑘 + 𝑏1 > 𝐶} ; (4)
𝑥∈ℬ
⃗ 2 𝑘=1
𝐵𝑢 = 𝑝(0, … 0, 𝐶). (5)
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Table 2
Transitions
No. Output state Intensity Conditions
𝐾
1 (𝑚
⃗ + 𝑒⃗1 , 𝑛) 𝜆𝑚 ∑𝑘=1 𝑚𝑘 ⋅ 𝑏𝑘 + 𝑛 + 𝑏1 ≤ 𝐶;
2 (𝑚
⃗ − 𝑒⃗𝑘 , 𝑛) 𝑚 𝑘 ⋅ 𝜇𝑚 𝑚𝑘 > 0;
𝐾
3 (𝑚,
⃗ 𝑛 + 1) 𝜆𝑢 ∑𝑘=1 𝑚𝑘 ⋅ 𝑏𝑘 + 𝑛 + 1 ≤ 𝐶;
𝐾
⎧ ∑ 𝑚𝑘 ⋅ 𝑏𝑘 + 𝑛 + 1 > 𝐶,
⎪
𝑘=1
4 (𝑚
⃗ − 𝑒⃗𝑘 + 𝑒⃗𝑘+1 , 𝑛 + 1) 𝜆𝑢
⎨ 𝑘−1
⎪ ∑ 𝑚𝑖 = 0, 𝑚𝑘 > 0, 𝑘 = 1, 𝐾 − 1;
⎩ 𝑖=1
𝐾
⎧ ∑ 𝑚𝑘 ⋅ 𝑏𝑘 + 𝑛 + 1 > 𝐶,
⎪
𝑘=1
5 (𝑚
⃗ − 𝑒⃗𝐾 , 𝑛 + 1) 𝜆𝑢
⎨ 𝐾 −1
⎪ ∑ 𝑚𝑘 = 0, 𝑚𝐾 > 0;
⎩ 𝑘=1
𝑛 > 0,
6 (𝑚,
⃗ 𝑛 − 1) 𝑛 ⋅ 𝜇𝑢 { 𝐾
∑ 𝑚𝑘 = 0;
𝑘=2
𝐾
𝑛 > 0, ∑ 𝑚𝑖 = 0, 𝑚𝑘 > 0,
7 (𝑚
⃗ − 𝑒⃗𝑘 + 𝑒⃗𝑘−1 , 𝑛 − 1) 𝑛 ⋅ 𝜇𝑢 { 𝑖=𝑘+1
𝑘 = 2, 𝐾;
Table 3
System parameters for numerical analysis
Parameter Scenario 1 Scenario 2
𝑁 5 5
𝐾 5 5
𝜆𝑚 0 - 100 10, 50, 100
𝜆𝑢 5, 20, 100 0 - 100
𝜇𝑚 −1 1−1 1−1
𝜇𝑢 −1 3−1 3−1
4. Numerical Results
4.1. Input Data
In this section, we present the results of a numerical analysis, namely the average number of
eMBB and URLLC sessions and the blocking probability of eMBB/URLLC sessions. We will use
two scenarios, which are presented in Table 3. The idea is that firstly 𝜆𝑚 is gradually increased
for various constant values of 𝜆𝑢 , and vice versa. So we have that 𝑁 = 5 which is the total
number of the resource blocks. We have 𝐾 = 5, which is the number of speeds, so we will have
𝑏1 = 5, 𝑏2 = 4, 𝑏3 = 3, 𝑏4 = 2, 𝑏5 = 1.
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(a) Scenario 1 (b) Scenario 2
Figure 4: Average number of eMBB sessions
4.2. Average Number of Sessions
Figures 4.a-4.b show graphs of changes in the average number of eMBB requests in the system,
which are served at various speeds: the first one is the highest, while the fifth one is the lowest.
With a fixed arrival rate of URLLC traffic, an increase in the number of eMBB requests in
the system is observed with an increase in their arrival rate. At a low arrival rate of URLLC
sessions, almost all eMBB applications are served at maximum speed. When there are not
enough resources to serve eMBB at maximum speed, the speed of servicing eMBB sessions
begins to decrease, so the average number of sessions served at the maximum speed decreases,
and the average number of sessions served at the remaining four speeds increases. This happens
until there are so many URLLC requests that the system does not begin to block the connection
of eMBB sessions, and those that still enter the system are served at the minimum speed.
Now consider the average number of eMBB sessions in the system for the second scenario
presented. In Figure 4.b, almost the same behavior of the graph is observed, because lines
overlap almost perfectly. The slight difference is explained by the fact that with a low intensity
of eMBB arrival, there will be fewer requests in the system. All the graphs show a strong
dependence on the increase in the intensity of incoming URLLC sessions. First, the average
number of sessions decreases at maximum speed, while at others, it increases.
The average number of URLLC sessions in the system is shown in Figures 5.a and 5.b. Based
on the presented graphs, we can conclude that this value depends only on the intensity of
URLLC. This behavior is due to the priority of the URLLC traffic.
4.3. Blocking Probability
The following is the blocking probability of eMBB sessions. Obviously, the higher the intensity
of incoming eMBB sessions, the greater the blocking probability of them. Figure 6.a clearly
shows that with a very high rate of arrival of URLLC sessions, the rate of arrival of eMBB
practically does not affect the blocking probability of eMBB sessions, which is very large and
tends to be 1, since all resources are occupied by URLLC traffic. At the same time, when the
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(a) Scenario 1 (b) Scenario 2
Figure 5: Average number of URLLC sessions.
(a) Scenario 1 (b) Scenario 2
Figure 6: Blocking probability of eMBB sessions.
intensity of URLLC sessions is low, the blocking probability of eMBB will be small since the
system will cope with the load from the eMBB stream. The graph in Figure 6.b confirms the
assumptions made. It can also be concluded that eMBB blocking is more influenced by the
intensity of incoming URLLC sessions since the schedule behaves almost the same for the
selected constant intensities of incoming eMBB traffic.
The blocking probability of URLLC sessions is affected by the rate of only this type of traffic
since the system prioritizes it.
5. Conclusion
This paper is focused on a mathematical model for eMBB and URLLC coexistence in the form
of a queuing system with priority service for URLLC traffic – reducing and interrupting the
transmission rate of eMBB. In particular, we formulated indicators of priority access efficiency
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such as the probability of reducing the transmission rate, the probability of service interruption,
the average transmission rate of eMBB traffic. Moreover, the numerical results show that URLLC
has a significant impact on eMBB. In the future, we will consider the model that considers the
spatial location of devices generating URLLC and eMBB traffic.
Acknowledgments
This paper has been supported by the RUDN University Strategic Academic Leadership Program
(recipients Alexander Chursin, Petr Kharin, and Anna Kushchazli). The work was supported by
the RFBR, project 20-37-70079 (recipients Irina Kochetkova and Petr Kharin).
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