ArticlePDF Available

Packet-Drop Design in URLLC for Real-Time Wireless Control Systems

Authors:

Abstract and Figures

In real-time wireless control systems, ultra-reliable and low-latency communication (URLLC) is critical for the connection between remote controller and its control objective. Since both transmission delay and packet loss can lead to control performance loss, our goal is to optimize control performance by jointly considering control and URLLC constraints in this paper. To achieve this goal, we formulate an optimal problem to minimize control cost by optimizing packet drop and wireless resource allocation. To solve the problem, we analyze the relationship between communication and control. Then, based on the relationship, we decompose the original problem into two subproblems: (1) an optimal packet-drop problem to minimize control cost and (2) an optimal resource allocation problem to minimize communication packet error. Finally, the corresponding solutions for each subproblem can be obtained. Compared with the traditional method only considering communication aspect, the proposed packet-drop and resource allocation method shows remarkable performance gain in terms of control cost.
Content may be subject to copyright.
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.DOI
Packet-Drop Design in URLLC for
Real-Time Wireless Control Systems
BO CHANG1, (Student Member, IEEE), GUODONG ZHAO2, (Senior Member, IEEE), ZHI
CHEN3, (Senior Member, IEEE), LIYING LI4, (Member, IEEE), and MUHAMMAD ALI IMRAN5,
(Senior Member, IEEE),
1National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), Chengdu
611731, China (emails: changb3212@163.com)
2School of Engineering, University of Glasgow, Glasgow, G12 8LT, UK (e-mail: Guodong.Zhao@glasgow.ac.uk)
3National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), Chengdu
611731, China (emails: chenzhi@uestc.edu.cn)
4School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK (e-mail: Liying.Li@glasgow.ac.uk)
5School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK (e-mail: muhammad.imran@glasgow.ac.uk)
Corresponding author: Zhi Chen (e-mail: chenzhi@uestc.edu.cn).
This work was supported in part by the National Natural Science Foundation of China under Grant 61631004, by the National Key R&D
Program of China under Grant 2018YFC0807101, by EPSRC Global Challenges Research Fund - the DARE project - EP/P028764/1, and
by the scholarship from China Scholarship Council (CSC) under the Grant CSC 201806070120.
ABSTRACT In real-time wireless control systems, ultra-reliable and low-latency communication (URLL-
C) is critical for the connection between remote controller and its control objective. Since both transmission
delay and packet loss can lead to control performance loss, our goal is to optimize control performance
by jointly considering control and URLLC constraints in this paper. To achieve this goal, we formulate
an optimal problem to minimize control cost by optimizing packet drop and wireless resource allocation.
To solve the problem, we analyze the relationship between communication and control. Then, based on the
relationship, we decompose the original problem into two subproblems: (1) an optimal packet-drop problem
to minimize control cost and (2) an optimal resource allocation problem to minimize communication
packet error. Finally, the corresponding solutions for each subproblem can be obtained. Compared with
the traditional method only considering communication aspect, the proposed packet-drop and resource
allocation method shows remarkable performance gain in terms of control cost.
INDEX TERMS URLLC, real-time wireless control, packet drop, wireless resource allocation.
I. INTRODUCTION
In real-time wireless control systems, ultra-reliable and low-
latency communication (URLLC) is critical for the connec-
tion between remote controller embedded in the base station
(BS) and its control objective (i.e., the plant) [1]. In such
a system, the inevitable transmission delay and packet loss
in URLLC lead to control performance loss. Furthermore,
when the controller handles plenty of plants, guaranteeing
both ultra-reliable and low latency is extremely challenging,
which may lead to significant control performance loss. It
is expected that both communication and control aspects
can be jointly considered to maintain good overall system
performance.
Recently, some works have been done on the impact of
communication on control performance [2]–[10]. For exam-
ple, by modeling transmission time delay and packet loss into
control systems, the authors in [3] analyzed the consequence
of the imperfect transmission on the control performance,
where imperfect transmission coefficients are introduced by
communication protocols, e.g., transmission control protocol
(TCP) or user datagram protocol (UDP). In [4]–[9], the au-
thors further investigated the control performance of different
control categories with imperfect communications. In [10],
the authors provided a tutorial and reviewed the existing
advances of wireless network design and optimization for
wireless networked control systems. However, the aforemen-
tioned works are based on the existing wireless networks and
cannot be used in control scenarios with ultra-reliable and
low-latency requirements.
To deal with the issue, URLLC is proposed in the coming
fifth generation (5G) cellular networks to support real-time
wireless control systems [11]. Some research have been done
to maintain the extremely high QoS requirements in URLLC
[12]–[19]. For instance, the authors in [12] discussed re-
VOLUME 4, 2016 1
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
source reservation strategy to maintain extreme high QoS. In
[13], the authors investigated resource allocation schedules in
physical layer for URLLC downlink. In [14], the authors ana-
lyzed resource allocation method when URLLC coexists with
another important scenario, i.e., enhance mobile broadband
(eMBB). In [15], the authors derived the closed-form solution
for resource allocation based on the concept of effective
capacity by considering the queueing delay violation prob-
ability. In [16], the authors considered the joint blocklength
and location optimization to minimize the decoding error
probability while adopting the channel capacity expression
for finite blocklength. In addition, the authors in [18] devel-
oped a protocol of device-to-device (D2D) communications
in URLLC. However, the packet drop introduced by limited
computing capacity is not discussed in the aforementioned
works, which may lead to significant queueing packet drop.
To deal with this problem, the authors in [19] proposed a
queueing policy and a random packet drop policy to maintain
the QoS requirement, where only communication aspect is
taken into account. However, the interaction between control
and URLLC is not obtained, which is very important since
the communication design in URLLC is actually determined
by the control requirement.
In this paper, we consider the design of the uplinks from
sensors to the BS, where we formulate an optimal problem
to minimize control cost by optimizing packet drop and
resource allocation method. To solve the problem, the key is
to decouple the binary packet drop and continuous resource
allocation variables. The main contributions of this paper are
summarized as follows.
We formulate an optimal problem to minimize the con-
trol cost, where the limited wireless resource and ex-
tremely high QoS requirements are taken into account.
The formulated problem allows us to use optimal packet
drop and wireless resource allocation method to support
real-time wireless control with minimum control cost.
We analyze the relationship between optimal control
law and communication parameter design, and then
we find that the binary packet drop and continuous
bandwidth allocation can be decoupled based on their
contributions to the control and communication.
We decompose the formulated original problem into
two subproblems based on the variable decouple: (1)
an optimal packet drop problem to minimize control
cost and (2) an optimal resource allocation problem to
minimize communication packet error. By solving the
subproblems, we obtain the packet drop and resource
allocation method.
The rest of this paper is organized as follows. In Section
II, the system model is presented. In Section III, the optimal
problem is formulated. In Section IV, we obtain the resource
allocation method and packet drop method for the formulated
problem. In Section V, simulation results are provided to
show the performance of our method. Finally, Section VI
concludes the paper.
!"#$%#&"&'()%*"%+$,(&$%
-()&+(..$+%$,/$00$01
2$)#(+%3
4.")&%3
2$)#(+%5
4.")&%5
2$)#(+%6
4.")&%6
FIGURE 1: Communication model.
II. SYSTEM MODEL
As shown in Fig. 1, we consider a centralized wireless control
system, where a base station (BS) embedded Mremote
controllers conducts the control process for Mplants. In the
control process, each plant has one sensor sampling the state
of the plant. Once the BS receives the sampling signal from
each sensor, the corresponding remote controller embedded
in the BS calculates the control command. Then, the BS
transmits the command to the corresponding the plant to
update its current state. With the control process performing,
the plant state turns to the target state. In this section, the
system model considering both communication latency and
reliability is presented for the performance evaluation in real-
time wireless control systems.
A. COMMUNICATION
In this subsection, we focus on the uplink from the sensors
to the BS, where we assume that only the uplink experiences
time delay and packet loss. We consider orthogonal frequen-
cy division multiple access (OFDMA), where we assume
that each sensor is allocated with independent continuous
bandwidth Bm. In addition, we consider flat fading channel,
where the channel gains for each sensor are approximately
identical and perfectly known for the sensor. Furthermore,
we assume that transmission duration for the m-th sensor is
Tm.
1) Channel Model
We consider that the channel model consists of the small-
scale fading and large-scale attenuation coefficients between
transceiver, which are represented as hmand gmfor the
uplink from the m-th sensor to the BS, respectively. Ac-
cording to [20], the large-scale attenuation coefficient can be
expressed as
gm[dB]=128.137.6 lg(lm),(1)
2VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
where lm0.035 km is the distance between transceiver
[20].
The small-scale fading hmfollows Rayleigh distribution
with mean zero and variance σ2
0= 1 [21]. However, since
the end-to-end (E2E) latency is no more than 1ms in URLLC
[19], the transmission time delay from the sensors to the BS
or that from the BS to the plants is less than the channel
coherence time, which means that the small-scale fading hm
is constant during the transmission period of the uplink and
the downlink [22].
2) Channel Capacity
According to [19] and [23], we can obtain the uplink channel
capacity expression for the m-th sensor in URLLC as
Rm=CmrVm
TmBm
f1
Q(εe
m) + log(TmBm)
2TmBm
,(2)
where the first term on the right hand of (2) is the achievable
Shannon capacity without transmission error, the second term
is the minus error bits introduced by channel dispersion
Vm, the third term is the approximation of the reminder
terms of order log(TmBm)/(TmBm),f1
Q(·)is inverse of
Qfunction, and εe
mis the transmission error probability.
Furthermore, we assume that the single-sided noise spectral
density is represented by N0, then according to [19], we have
Shannon capacity Cmand channel dispersion Vmas follows,
respectively,
Cm= log (1 + γm),(3)
and
Vm= (log e)211
(1 + γ2
m),(4)
where γmis the received signal-to-noise-ratio (SNR) at the
BS and can be expressed as
γm=|hm|2Bmgmpm
N0Bm
=|hm|2gmpm
N0
,(5)
where Pmis the transmission power spectral density of the
m-th sensor.
B. CONTROL
In this subsection, we provide the control model for each
plant mwith communication time delay and reliability. As
shown in Fig. 2, the control process is conducted as follow-
ing. First, a sensor takes the sample of the corresponding
plant’s current state and transmits it to the BS. Then, the
controller in the BS estimates the state by Kalman Filter
[3], calculates the control command, and sends it to the
plant. Finally, the plant state updates by the received control
command. Based on the above control process, the linear
differential equation of the m-th plant can be expressed as
[3]
dxm(t) = Axm(t)dt +Bum(t)dt +dnm(t),(6)
where xm(t)is the plant state, um(t)is the control input, and
nm(t)is the disturbance caused by additive white gaussian
!"#$%&'($(#&
)##*+$,- .$/0$"&1!/(#%
2#03(#&,3"(%3//#%&$(&(4#&56
7/$"(&0 6#"'3%&0
839"/!"- :;/!"-
FIGURE 2: Control model.
noise (AWGN) with zero mean and variance R. In addition,
we assume that each plant mhas the same Aand B, which
represent the physical system parameter matrices (more de-
tails can be obtained in [24]).
We assume that sm,n represents the sample period at time
index n, which consists of the wireless transmission time
delay Tm,n and an idle period ¯sm,n. Their relationship can
be expressed as
sm,n = ¯sm,n +Tm,n,(7)
where n= 1,2,··· , N represents the sampling time index
in the control process. Then, the discrete time control model
with time delay dm,n can be obtained as [3]
xm,n+1=m,nxm,n+Φm,n
0un+Φm,n
1um,n1+nm,n,(8)
where m,n =eAsm,n ,Φm,n
0=R¯sm,n
0eAtdt·B, and
Φm,n
1=Rsm,n
¯sm,n eAtdt·B.
Assuming ξm,n = (xT
m,n uT
m,n1)Tis the generalized
state, then the control function in (8) can be rewritten as
ξm,n+1 =m,dξm,n +Φm,d um,n +¯nm,n,(9)
where ¯nm,n = (nT
m,n 0)Tand Φm,d = Φm,n
0
I!.
We assume m,n =m. Then, we have m,d =
mΦm,n
1
0 0 !.
Considering the packet loss, we have the close-loop system
in (9) can be rewritten as (10) on the top of next page.
In the above discussion, we have obtained the wireless
control model 1where both communication time delay and
packet loss have been taken into account. In the following
of this paper, we will formulate the optimal problem and
1According to [27], to maintain the stability of the wireless control
system, the following assumption should be satisfied: The packet loss
probability in URLLC and the control system parameters satisfy ρ(1
εth)(m,d +Φm,d L)(m,d +Φm,dL)+εthm,d m,d , where
ρ(·)is the spectral radius, Lis the control command feedback and will be
discussed in Appendix A, is the Kronecker product, and εth is the upper
bound of the packet loss probability.
VOLUME 4, 2016 3
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
ξm,n+1 =m,dξm,n +Φm,d um,n +¯nm,n,if βm,n = 1,and αm,n = 1,
m,dξm,n +¯nm,n,if βm,n = 0,or αm,n = 0,(10)
where we use αm,n to indicate if a packet should be discarded or not (αm,n = 0 is to discard the packet while αm,n = 1 is
to keep the packer for transmission) and use βm,n to indicate if a packet is successfully transmitted or not (βm,n = 0 is failed
transmission while αm,n = 1 is successful transmission).
propose corresponding method to obtain packet drop and
resource allocation.
III. PROBLEM FORMULATION
Our goal is to find the optimal packet drop method and
wireless resource allocation method by minimizing control
cost. To achieve this goal, we first provide the objective func-
tion, i.e., the control cost. Then, we obtain the packet drop
constraint in wireless communications. Finally, we formulate
the optimal problem.
A. OBJECTIVE
The quadratic control cost is one the most important criteri-
ons to evaluate the control performance [25][26], which are
composed by the sum of the deviations of the plant state from
its desired setpoint and the magnitude of the control input.
Then, the quadratic control cost can be expressed as [10]
Jm,N =E[ξT
m,N Wξm,N
+
N1
X
n=0
(ξT
m,nWξm,n +uT
m,nUum,n )],(11)
where Wand Uare the weight of the state and that of
the control input, respectively, and they can be adjusted
according to the emphasis of the control system. The control
variables in (11) can be obtained by Appendix A.
From (10), we can obtain that the generalized plant state
ξm,n is a function of transmission time delay and packet
loss, i.e., ξm,n(Tm,n , αm,n). In addition, um,n is also a
function of Tm,n and αm,n. Furthermore, the total number
of packet loss is determined by constraints on the packet loss
probability. Thereby, the control cost Jm,N is a function of
communication parameters.
B. CONSTRAINT
In Section II.A, we have introduced transmission error prob-
ability εe
m. However, when we consider the packets from M
sensors, the queueing delay violation probability cannot be
ignored [18], which results in some part of the packet loss at
the BS to maintain the extreme high QoS in URLLC.
We assume that εq
mrepresents the queueing delay violation
probability at the BS. Considering both queueing delay vio-
lation probability and transmission error probability, we have
the total packet loss probability of the m-th sensor as
εm=εe
m+εq
mεth,(12)
!""#$%&'(&()#& *
*#+%,$&-
*#+%,$&-./
000
000
1m n
i
!m n
i
m n
Q
!"m n
Q
m n
j
!"m n
j
!"#$%&
!"#$%&'(
)))
)))
))) )))
FIGURE 3: Queueing at the BS.
where εth is the upper bound of the total packet loss proba-
bility. From (2), we have
εe
m=fQTmBmCmλ+ log(TmBm)/2
(log e)TmBm,(13)
where λ=TmBmRmis payload information for each
sensor, and fQ(·)is the Q function. Next, we discuss the the
queueing delay violation probability εq
min details.
As shown in Fig. 3, each E2E communication pair, i.e.,
sensor-BS-plant, has the corresponding buffer at the BS,
where imrepresents the packets uploaded to the BS from the
m-th sensor and Qmrepresents the queue length for the m-th
plant. Furthermore, we assume that jmrepresents the packets
departed from the m-th queue. Then, according to [19], we
have the following Lemma 1.
Lemma 1: The queueing delay violation probability εq
m
can be expressed as
εq
m= exp{−φmEB
m(φm)Dq
max},(14)
where φmis the QoS exponent for the m-th plant, Dq
max
is the queueing delay bound, and EB
m(φm)is the effective
bandwidth and can be expressed as
EB
m(φm) = lim
N→∞
1
NTu,m φm
ln
(E
"exp
φm
N
X
n=1
im,n
!#).
(15)
Proof 1: The details of the proof for Lemma 1 can be
obtained in [19].
4VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
C. OPTIMAL PROBLEM
In this subsection, we formulate the optimal problem, which
is described in Problem 0, i.e., P0, and can be expressed as
P0: min
αm,n, Bm,n Tm,n
Jsum =
M
X
m=1
Jm,N (16a)
s.t.
αm,n ∈ {0,1},(16b)
εe
m+εq
mεth,(16c)
Tm,n Tth,(16d)
Bm,n Bth,(16e)
where the objective of this optimal problem is to minimize
the total control cost Jsum constrained by communication
parameters, Bth is the upper bound of the allocated band-
width for each sensor at any time index, Tth is the upper
bound of the communication time delay. In addition, εth is
the upper bound of the packet loss probability in URLLC,
which is in the region that guarantees the convergence of the
control systems [1]. Furthermore, αm,n is used to indicate
if a packet should be discarded or not. Then, we obtain
that the successful packet transmission probability can be
expressed as Pr{βm,n = 1|αm,n = 1}= 1 εe
mand the
failed packet transmission probability can be expressed as
Pr{βm,n = 0|αm,n = 1}=εe
m.
In (16), the constraint in (16e) maintains the URLLC
QoS requirements to guarantee successful transmission when
wireless transmission is triggered by control process. Con-
straints in (16b) and (16c) are related to both communication
and control. On the one hand, (16c) maintains the total packet
loss probability to guarantee the URLLC QoS requirements
for successful transmission, which determines packet drop
strategy αm. On the other hand, the total control cost of M
plants is determined by how to arrange αmin (16b), where
αmis related to communication packet loss probability. Fur-
thermore, the constraint on transmission time delay in (16d)
is also related to communication performance and control
performance. Thus, P0in (16) is extremely challenging to
be solved under the constraints in (16b), (16c), and (16d). In
the next section, we will discuss the solution for the problem
P0in details.
IV. PACKET DROP AND WIRELESS RESOURCE
ALLOCATION METHOD
In this section, we first analyze the relationship between
communication and control. Based on this, we decompose
(16) into two subproblems: (1) an optimal wireless resource
allocation problem to minimize transmission error proba-
bility and (2) an optimal packet drop problem to minimize
control cost. Then, the solution for the two subproblems can
be obtained.
A. RELATIONSHIP BETWEEN CONTROL AND
COMMUNICATION
As shown in Fig. 2, the linear feedback control law is used in
this paper (more details about this law can be obtained in [2]).
Then, the optimal expression for the control cost in (11) can
be rewritten as (17) on the top of next page [2]. Then, we can
obtain the relationship between control and communication
by the following Theorem 1.
Theorem 1: Once the communication time delay and
packet loss probability is determined, the optimal control cost
in (17) is related with αm,n from communication aspect.
Thus, the minimization of the objective function in (16a)
is relatively independent with communication constraints on
communication time delay and packet loss probability in
(16).
Based on Theorem 1,αm,n is the connection between
communication and control. Then, P0can be divided into
two subproblems. The first subproblem is to optimize the
communication time delay and packet loss probability by
wireless resource allocation. Once they are obtained, the
second subproblem is to minimize control cost by packet
drop design.
B. PACKET DROP AND RESOURCE ALLOCATION
Compared with transmission time delay in URLLC, packet
loss introduces more control performance loss, i.e., larger
control cost. This is because packet loss can be treated as
larger time delay than the required time delay in URLLC.
Then, minimizing packet loss probability is critical for better
control performance. Thus, our goal is to minimize packet
loss probability by optimizing transmission time delay and
bandwidth allocation in the first subproblem. By the ob-
tained transmission time delay and packet loss probability,
the second subproblem is to design the packet drop αm,n to
minimize control cost.
1) Resource Allocation
Since the queueing delay violation probability is independent
with wireless communications, the first sub-problem is to
minimize transmission error probability by optimal wireless
resource allocation, which can be expressed as P1, i.e.,
P1: min
Tm,n,Bm,n
εe
m(18a)
s.t.
εe
m+εq
mεth,(18b)
Tm,n Tth,(18c)
Bm,n Bth,(18d)
By minimizing error probability, the transmission require-
ment for the control process can be guaranteed. To obtain
the optimal resource allocation for P1, we assume that the
resource block consists of time resource and bandwidth re-
source, i.e., Tm,n ×Bm,n. Then, to solve the problem (18),
we need the following property about εe
m,n.
VOLUME 4, 2016 5
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
J
m,N=ξT
m,0Sm,0ξm,0+Tr(Sm,0Pm,0)+
N1
X
n=0
(Tr((T
m,dSm,n+1 m,d +WSm,n)Eαm,n [Pm,n|n])+Tr(Sm,n+1Rm,n )),
(17)
where the parameters can be obtained by Appendix A. Observing the expression in (17), the optimal control cost J
m,N is
related with αm,n for given transmission time delay.
Property 1: The function εe
m,n(Tm,n ×Bm,n )is convex
in Tm,n ×Bm,n.
Proof: See Appendix B.
By Property 1, we can apply the exact linear search
method to find the optimal (Bm,nTm,n )to minimize εe
m,n
[28].
From the above discussion, we can obtain the optimal
resource block allocation to minimize transmission error
probability. To reduce control cost, the transmission time
delay is desired to be short enough. Then, the time delay
Tm,n in resource block can be calculated by
Tm,n =(Bm,nTm,n )
Bth
.(19)
By the obtained minimum transmission error probability
εe
mand time delay Tm,n in (19), we discuss the packet drop
design in the following.
2) Packet Drop
The second subproblem minimizing the overall control cost
can be expressed as P2, i.e.,
P2: min
αm,n
J
sum =
M
X
m=1
J
m,N (20a)
s.t.
αm,n ∈ {0,1},(20b)
1PN1
n=0 αm,n
Nεe
m+εq
m,(20c)
where (20b) is the overall communication packet loss prob-
ability for each E2E (i.e., sensor-BS-plant pair). To deal
with P2in (20), we assume that Mplants have the same
control parameters. In addition, we assume that the weight
on the plant state Wis much larger than that on the control
input U. This assumption holds in this paper, since the plant
state is more important than the control input in mission-
critical real-time wireless control systems [1]. In addition,
we have proved that J
m,N increases strictly with the overall
communication packet loss probability in [1].
Since control process is a sequential process, packet drop
strategy leads to different control cost. In addition, it is
extremely difficult to predict the plant state since the state
update in (6) has disturbance term. Then, it is challenging to
obtain global optimal packet drop method to minimize total
control cost. Instead, we propose a suboptimal packet drop
method, where we obtain the point-wise minimum control
cost by the suboptimal packet drop.
In the proposed method, we assume that Em,n =
xT
m,nWxm,n represents the instantaneous control cost of the
m-th plant at time index n. When packet drop occurs at time
index n, the BS will drop the packet that contributes to mini-
mum plant state, which leads to point-wise minimum control
cost. The detailed method is summarized in Algorithm 1.
Algorithm 1 The proposed suboptimal packet drop method.
Input: εe
m,εq
m,W,A,B,C,xm,0, and Tm,n.
1: Set 4m= 1, where m= 1,2,··· , M
2: Set αm,n = 1, where m= 1,2,··· , M , and n=
0,1,2,··· , N 1
3: while nNdo
4: Calculate Em,n =ξT
m,nWξm,n , where m=
1,2,··· , M
5: [Emin, mmin , nmin] = min{Em,n },
6: while PM
m=1 EB
m(φm)PM
m=1 Rmdo
7: if 4mmin > εe
mmin +εq
mmin then
8: m0=mmin,
9: n0=nmin,
10: αm0,n0= 0,
11: {Em,n}={Em,n } \ Emin,
12: {m}={m} \ mmin,
13: {n}={n} \ nmin,
14: else
15: m0=mmin,
16: n0=nmin,
17: αm0,n0= 1,
18: {Em,n}={Em,n } \ Emin,
19: {m}={m} \ mmin,
20: {n}={n} \ nmin,
21: end if
22: end while
23: n=n+ 1
24: end while
Output: Packet drop method αm,n.
V. SIMULATION RESULTS
In this section, we provide simulation results to demonstrate
the performance of our analysis in this paper. In communica-
tion sub-systems, we assume that the payload information is
100 bits. The maximum time delay of URLLC is 1ms and
the maximum packet loss εis 105. The control parameters
are as follows: A= 2 14
0 1 !,B= 0
1!,C= 1 0
0 1!,
6VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
1 2 3 4 5 6 7 8 9 10
Total available bandwidth (Hz) 106
4.5
5
5.5
6
6.5
7
7.5
8
Total control cost
107
Proposed method
Random method
Channel gain based method
Exhaustive search method
FIGURE 4: Total control cost with different available band-
width Bth.
150 200 250 300 350 400 450 500
Total number of plants, M
0
1
2
3
4
5
6
7
8
Total control cost
107
Proposed method
Random method
Channel gain based method
Exhaustive search method
FIGURE 5: Total control cost with different number of
sensor-BS-plant pairs M.
P0= 0.01I,W=I,U=I,Rn=I, and Rn0= 0.01I.
Furthermore, we assume that the initial state is (100,100).
Each curve is obtained by 10000 Monte Carlo trails if there
is no extra declaration. Moreover, the random packet drop
method and channel gain based packet drop method are
considered as comparison. In addition, the exhaustive search
method solving the formulated problem is considered to
justify the benefits of the proposed algorithm.
Fig. 4 demonstrates the total control cost when the avail-
able bandwidth Bth is different, where the queueing delay
bound is 0.1ms. From the figure, all the curves decrease
monotonously with Bth. This is reasonable since larger Bth
can guarantee less packet loss, which maintains the timely
control input for control systems to reduce the control cost.
0 100 200 300 400 500 600 700 800
Arriving rate (packet/s)
1
2
3
4
5
6
7
8
Total control cost
107
Proposed method
Random method
Channel gain based method
Exhaustive search method
FIGURE 6: Total control cost with different arriving rate of
sampling packet.
1 2 3 4 5 6 7 8 9 10
Queuing delay constraint (s) 10-7
1
2
3
4
5
6
7
8
Total control cost
108
Proposed method
Random method
Channel gain based method
Exhaustive search method
FIGURE 7: Total control cost with different constraints on
queueing delay.
In addition, the decreasing rate of all the curves is smooth and
low when Bth is more than 2×106Hz, which is because that
Bth is saturated. Furthermore, the total control cost is similar
for both random method and channel gain based method,
since they have equal contribution to the control cost. On
the one hand, all the three curves are similar when Bth is
small. This is reasonable since small Bth leads to large packet
error probability, which results in that the control system is
not very sensitive to the packet drop method. From the fig-
ure, compared with random method and channel gain based
method, the proposed method decreases the total control cost
by almost 40% when Bth 2×106Hz, which indicates
that the proposed method in this paper has large advantage
compared with only considering the communication aspect.
In addition, compared with exhaustive search method, the
VOLUME 4, 2016 7
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
control cost of the proposed method is raised by at most
6.5%, which indicates that the solution of the proposed
method is close to the global optimal solution.
Fig. 5 shows the total control cost when the number of
sensor-plant pairs Mis different, where the total available
bandwidth is Bth = 2 ×106Hz, the queueing delay bound is
0.1ms. From the figure, all the curves increase monotonously
with the number of sensor-plant pairs M. This is reasonable
since the supported number of sensor-plant pairs is fixed with
given Bth, which further leads to large control cost when
Mincreases. In addition, the curves of the random method
and the channel gain based method are similar, which is
because that both of them have the same effect on the control
performance. Furthermore, the advantage of the proposed
method is approximative when the number Mis very large,
i.e., M300. This is reasonable since the dropped packets
to minimize control cost has minor effect on the total cost
when Mis too large compared with the traditional methods.
In addition, the control cost of the proposed method is is
similar to the global optimal solution obtained by exhaustive
search method.
Fig. 6 demonstrates the total control cost when the arriving
rate of the sampling packets is different, where the total
available bandwidth is Bth = 2 ×106Hz, the total number
of sensor-plant pairs is M= 250, and the queueing delay
bound is 0.1ms. From the figure, all the curves increase
monotonously with the arriving rate until 500 packets/s. In
addition, after 500 packets/sof the arriving rate, the curves
of the total control cost are approximative horizontal. This
can be explained by the following two aspects. On the one
hand, when the arriving rate is less than 500 packets/s, larger
arriving rate means smaller sampling period hk, which leads
to smaller dk/hk. Then, the control cost increases as the ar-
riving rate increasing before 500 packets/s[1]. On the other
hand, when the arriving rate is larger than 500 packets/s, the
number of arriving packets tends to saturated, which leads to
a balance state and the curves of the control cost have little
changes. Furthermore, from the figure, we can obtain that
the proposed method decreases the control cost by at most
60% compared with only considering the communication
aspect. In addition, the control cost of the proposed method
is approximated to the global optimal solution obtained by
exhaustive search method.
Fig. 7 demonstrates the total control cost when the queue-
ing time delay constraint is different, where the total available
bandwidth is Bth = 2 ×106Hz. From the figure, all the
curves decrease monotonously with the queueing time delay
constraint. This is reasonable since larger queueing time
delay constraint allows more packets in the queue, which
leads to less packet drop probability and larger transmission
successful probability. Then, the control cost can be reduced.
However, the control cost changes smoothly when the queue-
ing time delay constraint is larger than 5×107s. This
is because the allowed number of arriving packets tends to
saturated, and a balance state is maintained. Then, the curves
of the control cost have little changes. Furthermore, from
the figure, we can obtain that the proposed decreases the
control cost by at most 62% compared with only considering
the communication aspect. In addition, the performance gap
between the proposed method and the exhaustive search
method is minor.
VI. CONCLUSIONS
In this paper, we proposed a packet drop and wireless re-
source allocation method in URLLC for real-time wireless
control systems. To obtain good control performance, we
formulated an optimal problem to minimize the control cost
with communication constraints. To solve the problem, we
discussed the relationship between control and communica-
tion. Based on that, we decomposed the original problem
into two relatively independent sub-problems. By solving
the two subproblems, we obtained the transmission time
allocation, bandwidth allocation and packet drop method.
The proposed approach established a theoretic foundation
for the URLLC enabled real-time wireless control system
performance analysis and algorithm design.
.
APPENDIX A
This appendix provides the detailed calculation of the param-
eters in (17).
According to [2], Skis calculated by
Sk=T
dSk+1d+WT
dSk+1Φd(ΦT
dSk+1Φd
+U)1ΦT
dSk+1d.(21)
The generalized state can be estimated by a modified Kalman
filter, which can be obtained as follows.
Step 1: prior generalized state estimation. The prior
estimation for the generalized state can be expressed as
ˆ
ξm,n+1|n=m,d ˆ
ξm,n|n+Φm,dum,n ,(22)
where ˆ
ξm,n|nis the generalized state estimation based
on the current generalized state, and ˆ
ξm,n+1|nis the
generalized state estimation at time n+ 1 based on the
last generalized state at n.
Step 2: prior error variance estimation. The prior esti-
mation for the error variance can be expressed as
Pm,n+1|n=m,dPm,n|nT
m,d +Rn,(23)
where Pm,n|n=E[(ξm,n ˆ
ξm,n)(ξm,n ˆ
ξm,n)T]is
the estimation error variance, and Pm,n+1|nis the prior
estimation error variance at time k+ 1.
Step 3: optimal generalized state estimation. The opti-
mal generalized state estimation is the generalized state
estimation based on ˆ
ξm,n+1|n, and can be expressed as
ˆ
ξm,n+1|n+1 =ˆ
ξm,n+1|n+αm,nKm,n+1 (ym,n+1
Cm,d ˆ
ξm,n+1|n),
(24)
8VOLUME 4, 2016
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
where Km,n+1 will be discussed in the following Step
4.
Step 4: optimal control gain estimation. The optimal
control gain estimation Km,n+1 can be expressed as
Km,n+1 =Pm,n+1|nCT
m,d
(Cm,dPm,n+1|nCT
m,d +Rn0)1.(25)
Step 5: optimal error variance estimation. The optimal
error variance estimation is the error variance estimation
based on Pm,n+1|n, which can be calculated by
Pm,n+1|n+1 =Pm,n+1|n
αm,nKm,n+1 Cm,dPm,n+1|n.(26)
Finally, substituting the above parameters into (11), we can
obtain (17). Furthermore, to minimize the control cost in
(11), the control input needs to satisfy the following expres-
sion
um,n =ΦT
m,dSm,n+1 Φm,d +U1ΦT
m,dSm,n+1
m,d ˆ
ξm,n|n=Lm,n ˆ
ξm,n|n.
(27)
APPENDIX B
This appendix provides the detailed proof for Property 1.
We assume x=Bm,n Tm,n,G1= log(1 + |hm|2gmpm
N0),
and G2= (log e), then εe
m,n can be rewritten as
εe
m,n =fQ(xG1λ+ log(x)/2
G2x).(28)
Let
f1(x) = xG1λ+ log(x)/2
G2x.(29)
Taking derivative with respect to x, we can obtain
∂f1(x)
∂x =G2
2x1
2·G1x+ (1 + λ)log(x)
2(30)
Then, the second derivative with respect to xcan be ex-
pressed as
2f1(x)
∂x2=G2[G1(ln 4)x(1 + λ)(ln 4) + ln x2]
8x3
2(ln 2) .
(31)
Since Bm,n is with MHz order of magnitude and Tm,n is
with ms order of magnitude, we can obtain [G1(ln 4)x(1+
λ)(ln 4)] >0and (ln x2) >0. Then, we have that 2f1(x)
∂x2
is more than zero, i.e.,
2f1(x)
∂x2>0.(32)
Thus, f1(x)is convex in x. Furthermore, since fQ(·)is
convex. Thus, εe
m,n(x)is convex, i.e., εe
m,n is convex in
Bm,n ×Tm,n.
REFERENCES
[1] B. Chang, G. Zhao, M. Imran, L. Li, and Z. Chen, “Dynamic wireless QoS
analysis for real-time control in URLLC," IEEE Globecom Workshops
(GC Wkshps), Dec. 2018, pp. 1-4.
[2] L. Schenato, B. Sinopoli, M. Franceschetti, K. Poola, and S. Sastry,
“Foundations of control and estimation over lossy networks," IEEE Proc.,
vol. 95, no. 1, pp. 163-187, Jan. 2007.
[3] P. Park, J. Ara´ujo, and K. H. Johansson, “Wireless networked control
system co-design," IEEE Inter. Conf. Networking, Sensing and Control
(ICNSC), 2011, pp. 486-491.
[4] B. Carabelli, R. Blind, F. D¨urr, and K. Rothermel, “State-dependent
priority scheduling for networked control systems," American Control
Conference (ACC), pp. 1003-1010, May 2017.
[5] J. Zhou, G. Gu, and X. Chen, “Distributed kalman filtering over wireless
sensor networks in the presence of data packet drops," American Control
Conference (ACC), pp. 2556-2561, May 2017.
[6] X. Cao, P. Cheng, J. Chen, and Y. Sun, “An Online Optimization Approach
for Control and Communication Codesign in Networked Cyber-Physical
Systems," IEEE Trans. Industrial Informatics, vol. 9, no. 1, pp. 439-450,
Feb. 2013.
[7] A. Mohammadi and K. Plataniotis, “Event-based estimation with
information-based triggering and adaptive update," IEEE Trans. Signal
Processing, vol. 65, no. 18, pp. 4924-4939, Sep. 2017.
[8] V. Gupta and F. Luo, “On a control algorithm for time-varying processor
availability," IEEE Trans. Automatic Control, vol. 58, no. 3, pp. 743-748,
2013.
[9] S. Prakash, E. Horssen, D. Antunes, and W. Heemels, “Self-triggered and
event-driven control for linear systems with stochastic delays," American
Control Conference (ACC), pp. 3023-3028, 2017.
[10] P. Park, S. Ergen, C. Fischione, C. Lu, and K. Johansson, “Wireless
network design for control systems: a survey," IEEE Commun. Surveys
&Tutorials, pp. 978-1013, Dec. 2017.
[11] C. She, Ch. Yang, and T. Q. S. Quek, “Uplink transmission design
with massive machine type devices in tactile internet," IEEE Globecom
Workshops (GC Wkshps), Dec. 2016, pp. 1-6.
[12] Y. Chen, L. Cheng, and L. Wang, “Prioritized resource reservation for
reducing random access delay in 5G URLLC," IEEE International Sympo-
sium on Personal, Indoor, and Mobile Radio Communications (PIMRC),
Otc. 2017, pp. 1-5.
[13] H. Ji, S. Park, J. Yeo, Y. Kim, J. Lee, and B. Shim, “Ultra-reliable and low-
latency communications in 5G downlink: physical layer aspects," IEEE
Wireless Commun., vol. 25, no.3, pp. 124-130, Jul. 2018.
[14] A. Esswie and K. Pedersen, “Opportunistic spatial preemptive scheduling
for URLLC and eMBB coexistence in multi-eser 5G networks," IEEE
Access, vol. 6, pp. 38451-38463, Jul. 2018.
[15] H. Ren, N. Liu, C. Pan, M. Elkashlan, A. Nallanathan, X. You, and
L. Hanzo, “Power- and rate-adaptation improves the effective capacity
of C-RAN for nakagami-mfading channels," IEEE Trans. Vehicular
Technology, vol. 67, no. 11, pp. 10841-10855, Nov. 2018.
[16] C. Pan, H. Ren, Y. Deng, M. Elkashlan, A. Nallanathan, “Joint blocklength
and location optimization for URLLC-enabled UAV relay systems," IEEE
Commun. Letters, vol. 23, no. 3, pp. 498-501, Mar. 2019.
[17] R. Kotaba, C. Manch´on, T. Balercia, and P. Popovski, “Uplink transmis-
sions in URLLC systems with shared diversity resources," IEEE Wireless
Commun. Lett., vol. 6, pp. 38451-38463, Jul. 2018.
[18] L. Liu and W. Yu, “A D2D-based protocol for ultra-reliable wireless com-
munications for industrial automation," IEEE Trans. Wireless Commun.,
vol. 17, no. 8, pp. 5045-5058, Aug. 2018.
[19] C. She, C. Yang, and T. Quek, “Cross-layer optimization for ultra-reliable
and low-latency radio access networks," IEEE Trans. Wireless Commun.,
vol. 17, no. 1, pp. 127-141, Jan. 2018.
[20] 3GPP, Study on Scenarios and Requirements for Next Generation Ac-
cess Technologies. Technical Specification Group Radio Access Network,
Technical Report 38.913, Release 14, Oct. 2016.
[21] B. Chang, Z. Guo, G. Zhao, and Z. Chen, “Positioning receiver using full-
duplex amplify-and-forward relay," in Proc. IEEE Global Commun. Conf.
(GLOBECOM), Dec. 2015, pp. 1-6.
[22] W. Yang, G. Durisi, T. Koch, and Y. Polyanskiy, “Quasi-static multiplean-
tenna fading channels at finite blocklength," IEEE Trans. Inf. Theory, vol.
60, no. 7, pp. 4232-4264, Jul. 2014.
[23] G. Durisi, T. Koch, and P. Popovski, “Toward massive, ultrareliable, and
low-latency wireless communication with short packets," IEEE Proc.,
vol.104, no. 9, pp. 1711-1726, Aug. 2016.
VOLUME 4, 2016 9
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2019.2928681, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
[24] S. Cai and V. Lau, “Zero MAC latency sensor networking for cyber-
physical systems," IEEE Trans. Signal Processing, vol. 66, no. 14, pp.
3814-3823, Jul. 2018.
[25] J. Sarangapani and H. Xu, Optimal networked control systems with
MATLAB (automation and control engineering), CRC Press, 2015.
[26] Y. Zhao, G. Liu, Y. Kang, and L. liu, Packet-based control for networked
control systems, Springer Press, Nov. 2017.
[27] K. Gatsis, A. Ribeiro, and G. Pappas, “State-Based Communication De-
sign for Wireless Control Systems," IEEE 55th Conference on Decision
and Control (CDC), Dec. 2016, pp. 129-134.
[28] S. Boyd and L. Vandanberghe, Convex Optimization. Cambridge Univ.
Press, 2004.
BO CHANG received his Bachelor and Master
degrees from University of Electronic Science and
Technology of China (UESTC), Chengdu, China,
where he is currently pursuing a Ph.D. degree at
the National Key Laboratory of Science and Tech-
nology on Communications. From May 2019 to
May 2020, he is a visiting student at University of
Glasgow. His research interests include cognitive
radio, wireless localization, ultra-reliable and low-
latency communications, and communication and
control co-design for Industrial Internet-of-Things (IIoT).
GUODONG (PHILIP) ZHAO (SM’16) received
his Ph.D. Degree from Beihang University, Bei-
jing, China, in 2011 and his B.E. degree from
Xidian University, Xi’an, China, in 2005. In 2011-
2018, he worked as an associate professor at U-
niversity of Electronic Science and Technology of
China (UESTC) in China. In 2012-2013, he visited
the Hong Kong University of Science and Tech-
nology, Hong Kong. In 2016, he visited Lehigh
University, USA. In 2018, he joined University
of Glasgow in UK as a lecturer (assistant professor). His current research
interests are within the areas of wireless communications and control. He has
authored 50+ papers in IEEE journals and conferences. He received the Best
Paper Award from IEEE Global Telecommunication Conference (GLOBE-
COM) in 2012 and the Best Ph.D. Thesis Award from Beihang University
in 2012. He has served as a TPC for many international conferences, e.g.,
ICC and VTC. He also served a reviewer for many IEEE Transactions, e.g.,
the IEEE Transactions on Signal Processing and IEEE Journal on Selected
Areas in Communications.
ZHI CHEN (SM’16) received the B. Eng, M.
Eng., and Ph.D. degrees in electrical engineering
from the University of Electronic Science and
Technology of China (UESTC), in 1997, 2000,
and 2006, respectively. In 2006, he joined the Na-
tional Key Laboratory of Science and Technology
on Communications, UESTC, where he has been
a Professor since 2013. He was a Visiting Scholar
with the University of California at Riverside,
Riverside, from 2010 to 2011. His current research
interests include 5G mobile communications, tactile internet, and Terahertz
communication. He has served as a reviewer for various international jour-
nals and conferences, including IEEE Transactions on Vehicular Technology
and IEEE Transactions on Signal Processing.
LIYING LI received her B.E. and Ph.D. Degrees
from University of Electronic Science and Tech-
nology of China (UESTC) in 2005 and 2011, both
in Electrical Engineering. She visited Georgia In-
stitute of Technology, GA, USA, in 2008-2010
and Lehigh University, PA, USA, in 2016. Since
2011, she has been with the School of Automa-
tion Engineering, University of Electronic Science
and Technology of China (UESTC), where she
is currently an Associate Professor. Her research
interests are within the areas of wireless communications, smart grid, and
data science.
MUHAMMAD ALI IMRAN (M’03, SM’12) Fel-
low IET, Senior Member IEEE, Senior Fellow
HEA is a Professor of Wireless Communication
Systems with research interests in self organised
networks, wireless networked control systems and
the wireless sensor systems. He heads the Com-
munications, Sensing and Imaging CSI research
group at University of Glasgow. He is an Affiliate
Professor at the University of Oklahoma, USA
and a visiting Professor at 5G Innovation Centre,
University of Surrey, UK. He has over 20 years of combined academic
and industry experience with several leading roles in multi-million pounds
funded projects. He has filed 15 patents; has authored/co-authored over 400
journal and conference publications; was editor of 3 books and author of
more than 20 book chapters; has successfully supervised over 40 postgrad-
uate students at Doctoral level. He has been a consultant to international
projects and local companies in the area of self-organised networks.
10 VOLUME 4, 2016
... In general, a testbed consists of USRP/GNU radio units (i.e., BSs and nodes) that exchange control messages (e.g., channel sensing outcomes and available routes) with a single processing unit such as a PC (i.e., CC) via a Gigabit switch [15]. Control messages are transmitted in the wired medium; while data packets are transmitted in the wireless medium [36]. Hence, the effects and the incurred delay of control message exchange to network performance (e.g., end-to-end delay), which may affect the fulfilment of the network requirements (e.g., the delay constraint imposed by IEEE [37]), are not considered in the investigations. ...
Full-text available
Article
This paper demonstrates a route selection mechanism on a testbed with heterogeneous device-to-device (D2D) wireless communication for a 5G network scenario. The source node receives information about the primary users’ (PUs’) (or licensed users’) activities and available routes from the macrocell base station (or a central controller) and makes a decision to select a multihop route to the destination node. The source node from small cells can either choose: (a) a route with direct communication with the macrocell base station to improve the route performance; or (b) a route with D2D communication among nodes in the small cells to offload traffic from the macrocell to improve spectrum efficiency. The selected D2D route has the least PUs’ activities. The route selection mechanism is investigated on our testbed that helps to improve the accuracy of network performance measurement. In traditional testbeds, each node (e.g., Universal Software Radio Peripheral (USRP) that serves as the front-end communication block) is connected to a single processing unit (e.g., a personal computer) via a switch using cables. In our testbed, each USRP node is connected to a separate processing unit, i.e., raspberry Pi3 B+ (or RP3), which offers three main advantages: (a) control messages and data packets are exchanged via the wireless medium; (b) separate processing units make decisions in a distributed and heterogeneous manner; and (c) the nodes are placed further apart from one another. Therefore, in the investigation of our route selection scheme, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are implied in our end-to-end delay and packet delivery ratio measurement. Our results show an increase of end-to-end delay and a decrease of packet delivery ratio due to the transmission of control messages and data packets in the wireless medium in the presence of the dynamic PUs’ activities. Furthermore, D2D communication can offload 25% to 75% traffic from macrocell base station to small cells.
... The real-time wireless feedback control is a primary feature in many emerging application areas such as cyber-physical systems (CPS), industrial Internet of Things (IIoT), and Tactile Internet [1,2] where ultrareliable and low-latency communication (URLLC) [3][4][5][6][7] is critical to ensure real-time control performance, e.g., control cost and stability. As shown in Figure 1, a typical real-time wireless feedback control system consists of two phases [8][9][10][11][12][13], i.e., Phase 1 from sensor to controller and Phase 2 from controller to actuator. To ensure system performance and stability, one of main requirements is the timely exchange of information between the two phases. ...
Article
Ultra-reliable and low-latency communication (URLLC) is one of the most important scenarios in forthcoming fifth generation (5G) cellular networks to ensure timely exchange of information and realize real-time wireless control. In URLLC, timely information update needs to be guaranteed since control performance, e.g., control cost and stability, is directly determined by timely control information update. In this paper, we introduce an effective age of information (EAoI) to evaluate the timeliness of information update in control process. We consider the control process with two phases: sensor to controller phase and controller to actuator phase. We adopt first-generate-first-serve (FGFS) M/M/1/1 → M/M/1/2 and FGFS M/M/1/1* → M/M/1/2* tandem queuing models to represent control process and we use finite-state Markov chains to describe control information updates. By studying state transitions, we calculate the average EAoI for both tandem queuing models. More importantly, we analyze throughput of wireless control systems and its relationship with average EAoI, which provides a guideline for URLLC system design in real-time feedback control systems. Simulation results show the advantage of using EAoI.
... In URLLC, a single OFDM symbol [19] is used to detect a packet, to minimize latency via differential detection and transmission prediction and to achieve higher reliability up to 10 −6 . A real-time packet drop design in URLLC for wireless control systems is analyzed in [20] and in this design, the control cost is minimized by optimizing packet drop or by optimal wireless resource allocation in a multi-remote-controller-connected system. The dual existence of URLLC and eMBB services in a cloud-based radio access network is discussed in [21] and in addition, NOMA, OMA, URLLC and eMBB are investigated for reliability, latency, and inter-cell power gain in terms of different network traffic loads by employing puncturing and successive interference cancellation. ...
Full-text available
Article
Ultra-Reliable Low-Latency Communication (URLLC) is challenging due to its extremely higher reliability requirement with stringent short latency packet transmission. In order to overcome this reliability and latency bound, a communication access scheme needs to assure almost error-free and high speed packet transmission. In this paper, a new multiple-access scheme—Orthogonal Frequency-Subcarrier-based Multiple Access (OFSMA)—is proposed with URLLC’s high requirement adaptation. In this scheme, the packet diversity concept is incorporated to achieve the expected packet transmission reliability and a diverse number of the duplicated packet are processed with a set of operations and transmitted over randomly selected orthogonal subcarrier frequency channels. Performances of the OFSMA system are measured in terms of applying several numbers of frequency bands, a massive number of subcarrier channels, a different number of packet duplications, and a diverse rate of traffic arrival conditions. We determined the minimum number of subcarrier channels requirement to satisfy the reliability of 99.999% for different packet duplication in presence of different frequency bands. The reliability response for a fixed number of subcarrier channels is evaluated for different frequency band conditions. Finally, the air interface latency of the OFSMA system is measured for single packet uplink transmission and compared with that of a traditional OFDMA system. The performance results in terms of reliability and latency express that the OFSMA scheme can assure the expected reliability and latency defined by URLLC.
Article
Wireless and mobile computing enables the spontaneous networking of a system with or without previous set-up. Mobile Ad Hoc Network (MANET) is a Wi-Fi grid that has already been developed and does not require an existing infrastructure for a specific extemporaneous operation. Any node can then connect or exit the network, which will permit the attacker to access the whole system. These networks are liable to various attacks. This paper directs detection of the clone attack from MANETs in which the attacker node steals the id of the closing node, twin it and attracts all the data towards it. ACO noticed the clone attack and measured performance based on the packet drops, packet delivery ratio and network throughput.
Article
In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.
Chapter
As one of the three main scenarios in fifth generation (5G) cellular networks, ultra‐reliable and low‐latency communication (URLLC) can serve as an enabler for real‐time wireless control systems. In such a system, communication resource consumption in URLLC and control subsystem performance are mutually dependent. To optimize the overall system performance, it is critical to integrate URLLC and control subsystems together by formulating a co‐design problem. Based on uplink transmission, this chapter studies the problem of resource allocation for URLLC in real‐time wireless control systems. The problem is conducted by optimizing bandwidth and transmission power allocation in URLLC and control convergence rate subject to the constraints on communication and control. To formulate and solve the problem, this chapter first converts the control convergence rate requirement into a communication reliability constraint. Then, the co‐design problem can be replaced by a regular wireless resource allocation problem. By proving that the converted problem is concave, an iteration algorithm is proposed to find the optimal communication resource allocation. Based on that, the optimal control convergence rate can be obtained to optimize overall system performance. Simulation results show remarkable performance gain in terms of spectral efficiency and control cost. Compared with the scheme of satisfying fixed quality‐of‐service in traditional URLLC design, the proposed method in this chapter can adjust optimal spectrum allocation to maximize the communication spectral efficiency and maintain the actual control requirement.
Article
In this paper we investigate an innovative solution, to implement high sampling frequency industrial control by means of networked embedded systems connected via WiFi. The basic idea relies on a co-design approach for the control application, which is then able to adapt its sampling period, as well as to tune the Wi-Fi parameters, according to the feedback coming from the network. To this end, we implemented a cross-layer architecture acting at both application and data-link layers, that features a robust frame--delay state estimator, a time-efficient communication policy, and a specific tuning of the critical protocol parameters. Suitable hardware-in--the--loop experiments have been carried out exploiting two different embedded systems available off--the--shelf. The preliminary results, obtained from an extensive experimental campaign, are encouraging since they show that the proposed architecture enables industrial control applications requiring a sampling rate up to 1000~Hz, even in presence of communication impairments.
Full-text available
Article
Due to the ever-expanding applications of the Internet-of-Things (IoT), designing energy-and spectrally-efficient transmission schemes to support massive connections and devices is inevitable and still challenging. Thus, energy-harvesting (EH) and cognitive-radio (CR) systems are becoming more inseparable for future IoT networks. This paper analyzes the performance of EH-CR-IoT networks, where closed-form expressions for network metrics, such as GoodPut, collision probability and average packet delay are derived. In addition to the interference caused by spectrum sensing errors, our analysis also incorporates the primary user (PU) return interference into the different network metrics. Furthermore, the effect of primary network traffic behavior and IoT network parameters are investigated. To account for delay-sensitive packets, the average end-to-end delay of packets as well as delay violation probability in the IoT network are mathematically formulated and analyzed as quality-of-service (QoS) measures for network stability. Moreover, the derived metrics can be utilized to optimize the Goodput, subject to various practical constraints. Simulations are also performed to verify the theoretical results. Above all, the effect of energy-harvesting rate on GoodPut and IoT network stability is explored, which provides insights into determining the physical structure of the energy-harvesting system.
Full-text available
Article
We propose a power-and rate-adaptation scheme for cloud radio access networks (C-RANs), where each radio remote head (RRH) is connected to the baseband unit (BBU) pool through optical links. The RRHs jointly support the users by efficiently exploiting the enhanced spatial degrees of freedom. Our proposed scheme aims for maximizing the effective capacity (EC) of the user subject to both per-RRH average-and peak-power constraints, where the EC is defined as the maximum arrival rate that can be supported by the C-RAN under the statistical delay requirement. We first transform the EC maximization problem into an equivalent convex optimization problem. By using the Lagrange dual decomposition method and solving the Karush-Kuhn-Tucker (KKT) equations, the optimal transmission power of each RRH can be obtained in closed-form. Furthermore, an online tracking method is provided for approximating the average power of each RRH for the sake of updating the Lagrange dual variables. For the special case of two RRHs, the expression of the average power of each RRH can be calculated in explicit form. Hence, the Lagrange dual variables can be computed in advance in this special case. Furthermore, we derive the power allocation for two important extreme cases: 1) no delay constraint; 2) extremely stringent delay-requirements. Our simulation results show that the proposed scheme significantly outperforms the conventional algorithm without considering the delay requirements. Furthermore, when appropriately tuning the value of the delay exponent, our proposed algorithm is capable of guaranteeing a delay outage probability below $10^{-9}$ when the maximum tolerable delay is 1 ms. This is suitable for the future ultra-reliable low latency communications (URLLC).
Full-text available
Article
As one indispensable use case for the 5G wireless systems on the roadmap, ultra-reliable and low latency communications (URLLC) is a crucial requirement for the coming era of wireless industrial automation. The key performance indicators for URLLC are in sharp contrast to the current broadband communications, since latency and reliability are paramount but high data rates are often not required. This paper aims to develop communication techniques for making such a paradigm shift from the conventional human-type broadband communications to the emerging machine-type URLLC. One fundamental task for URLLC is to deliver a short command from the controller to each actuator within the stringent delay requirement and also with high-reliability in the downlink. Motivated by the geographic feature in industrial automation that in the factories many tasks are assigned to different groups of devices who work in close proximity to each other and thus can form clusters of reliable device-to-device (D2D) networks, this paper proposes a novel two-phase transmission protocol for achieving the above goal. Specifically, in the first phase within the latency requirement, the multi-antenna base station (BS) combines the messages of each group together and multicasts them to the corresponding groups; while in the second phase, the devices that have decoded the messages successfully, who are defined as the leaders, help relay the messages to the other devices in their groups. Under this protocol, we further design an innovative leader selection based beamforming strategy at the BS by utilizing the sparse optimization technique, which leads to the desired sparsity pattern in user activity, i.e., at least one leader exists in each group, in the first phase, thus making full utilization of the reliable D2D networks in the second phase. Simulation results are provided to show that the proposed two-phase transmission protocol considerably improves the reliability of the whole system within the stringent latency requirement as compared to other existing schemes for URLLC such as Occupy CoW.
Full-text available
Article
As one indispensable use case for the 5G wireless systems on the roadmap, ultra-reliable and low latency communications (URLLC) is a crucial requirement for the coming era of wireless industrial automation. This paper aims to develop communication techniques for making such a paradigm shift from the conventional human-type broadband communications to the emerging machine-type URLLC. One fundamental task for URLLC is to deliver a short command from the controller to each actuator within the stringent delay requirement and also with high-reliability in the downlink. Motivated by the geographic feature in industrial automation that in the factories many tasks are assigned to different groups of devices who work in close proximity to each other and thus can form clusters of reliable device-to-device (D2D) networks, this paper proposes a novel two-phase transmission protocol for achieving the above goal. Specifically, in the first phase within the latency requirement, the multi-antenna base station (BS) combines the messages of each group together and multicasts them to the corresponding groups; while in the second phase, the devices that have decoded the messages successfully, who are defined as the leaders, help relay the messages to the other devices in their groups. Under this protocol, we further design an innovative leader selection based beamforming strategy at the BS by utilizing the sparse optimization technique, which leads to the desired sparsity pattern in user activity, i.e., at least one leader exists in each group, in the first phase, thus making full utilization of the reliable D2D networks in the second phase. Simulation results are provided to show that the proposed two-phase transmission protocol considerably improves the reliability of the whole system within the stringent latency requirement as compared to other existing schemes for URLLC such as Occupy CoW.
Article
This letter considers the unmanned aerial vehicle (UAV)-enabled relay system to deliver command information under ultra-reliable and low-latency communication (URLLC) requirements. We aim to jointly optimize the blocklength allocation and the UAV’s location to minimize the decoding error probability subject to the latency requirement. The achievable data rate under finite blocklength regime is adopted. A novel perturbation-based iterative algorithm is proposed to solve this problem. Simulation results show that the proposed algorithm can achieve the same performance as the exhaustive search method, and significantly outperforms the existing algorithms.
Article
The fifth generation (5G) of the mobile networks is envisioned to feature two major service classes: ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). URLLC applications require a stringent one-way radio latency of 1 ms with 99:999% success probability while eMBB services demand extreme data rates. The coexistence of the URLLC and eMBB quality of service (QoS) on the same radio spectrum leads to a challenging scheduling optimization problem, that is vastly different from that of the current cellular technology. This calls for novel scheduling solutions which cross-optimize the system performance on a user-centric, instead of network-centric basis. In this paper, a null-space-based spatial preemptive scheduler for joint URLLC and eMBB traffic is proposed for densely populated 5G networks. Proposed scheduler framework seeks for cross-objective optimization, where critical URLLC QoS is guaranteed while extracting the maximum possible eMBB ergodic capacity. It utilizes the system spatial degrees of freedom in order to instantly offer an interference-free subspace for critical URLLC traffic. Thus, a sufficient URLLC decoding ability is always preserved, and with the minimal impact on the eMBB performance. Analytical analysis and extensive system level simulations are conducted to evaluate the performance of the proposed scheduler against the state-of-the-art scheduler proposals from industry and academia. Simulation results show that proposed scheduler offers extremely robust URLLC latency performance with a significantly improved ergodic capacity.
Article
URLLC is a new service category in 5G to accommodate emerging services and applications having stringent latency and reliability requirements. In order to support URLLC, there should be both evolutionary and revolutionary changes in the air interface named 5G NR. In this article, we provide an up-to-date overview of URLLC with an emphasis on the physical layer challenges and solutions in 5G NR downlink. We highlight key requirements of URLLC and then elaborate the physical layer issues and enabling technologies including packet and frame structure, scheduling schemes, and reliability improvement techniques, which have been discussed in the 3GPP Release 15 standardization.
Article
In this paper, we consider a cyber-physical system, where an unstable dynamic plant is monitored by multiple distributed sensors over an wireless network with shared common spectrum. We propose a novel a zero-latency MAC protocol for cyber-physical systems. The proposed scheme exploits interferences and collisions among active sensors in the wireless channels to enhance the performance of remote state estimation. Using the Lyapunov drift analysis approach, we further establish closed-form necessary and sufficient requirements on the communication resources needed to achieve stabilization of the cyber-physical system. Large system analysis demonstrates that the proposed scheme has superior scalability with respect to large number of sensors. The proposed scheme is also compared with various state-of-the-art baselines and we show that significant performance gains can be achieved.
Article
5G features flagship use cases with Ultra Reliable Low Latency Communication (URLLC), supported through high diversity. When multiple URLLC connections are only intermittently active, dedicating many diversity resources to a single connection leads to inefficient operation. We address this problem through shared diversity resources and compare it to per-link dedicated diversity. Two receiver types are considered, MMSE (minimum mean squared error) and MMSE-SIC (successive interference cancellation). Outage probability is evaluated by assuming channel estimation errors. The results show that it is possible to remain close to the reliability of reference system with a relatively low amount of pre-allocated resources.