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URLLC Based Cooperative Industrial IoT Networks
with Non-Linear Energy Harvesting
Sravani Kurma, Prabhat Kumar Sharma, Keshav Singh, Shahid Mumtaz, and Chih-Peng Li
Abstract—The efficient and effective framework for next-
generation (5G and beyond 5G) wireless networks should include
mission-critical aspects such as ultra-low latency (≤1ms), ultra-
high reliability (99.999%), and enhanced data rate. Billions of
ubiquitously connected devices are expected to serve various
industrial applications in upcoming industry standards such
as Industry 5.0. These industrial applications include mission-
critical tasks such as smart grids, remote surgery and intelli-
gent transportation systems. This paper considers an industrial
internet of things (IIoT) environment in mission-critical ultra-
reliable low latency communication (URLLC) application where
the main industrial unit or industrial control node (CN) sends
messages to the target device (TD) with the aid of a cooperative
device (CD). We investigate a novel transmission protocol and
analyze the network’s performance. Considering the non-linear
energy harvesting (EH) mechanism at power-constrained nodes
and direct and cooperative phase transmissions, the outage
probability (OP) and block error rate (BLER) performances are
evaluated for Rayleigh distributed fading channels. The analytical
results are validated through Monte-Carlo simulations.
Index Terms—Industrial internet of things (IIoT), energy
harvesting (EH), non-linear (NL), outage probability (OP), device
selection, ultra-reliable low latency communication (URLLC).
I. INTRODUCTION
THe mission-critical applications with rock-solid security,
unfailing reliability, robust performance to withstand
remote environments, precision, consistency, accuracy-based
working conditions, and low-latency to enable real-time com-
munication are expected to rule the control and data signalling
for upcoming industrial internet of things (IIoT) systems such
as Industry 5.0and beyond. In this context, the mission-
critical IoT is more than just critical applications in healthcare,
industrial, and power systems industries. Due to the prospec-
tive fifth-generation (5G) and beyond 5G (B5G) technologies
which focus on achieving URLLC services, a new breed of
applications are evolving to be included in mission-critical
IoT that must perform as expected, without fail, every time,
in industries such as wearables, smart cities, smart homes,
healthcare (remote surgery, patient health monitoring etc.),
This work was supported by the Ministry of Science and Technology of
Taiwan under Grants MOST 110-2221-E-110-020 & MOST 109-2221-E-110-
050-MY3.
Sravani Kurma was with the Department of Electronics and Communi-
cation Engineering, Visvesvaraya National Institute of Technology, Nagpur,
India. She is currently with the Institute of Communications Engineering,
National Sun Yat-sen University, Kaohsiung 80424, Taiwan, R.O.C. (e-mail:
d103070001@nsysu.edu.tw).
Prabhat Kumar Sharma is with the Department of Electronics and Commu-
nication Engineering, Visvesvaraya National Institute of Technology, Nagpur,
India. (e-mail: prabhatsharma@ece.vnit.ac.in).
Keshav Singh and Chih-Peng Li are with the Institute of Communications
Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan,
R.O.C. (e-mail: keshav.singh@mail.nsysu.edu.tw, cpli@faculty.nsysu.edu.tw).
Shahid Mumtaz is with the Instituto de Telecomunicac¸ ˜
oes, P-3810-193
Aveiro, Portugal (e-mail: smumtaz@av.it.pt).
industry 5.0application (enable remote machinery), smart
grids (automate energy distribution), and intelligent transporta-
tion systems (automatic emergency detection and autonomous
vehicle accident prevention), etc [1].
Despite their promising applications and benefits, the most
of the IoT devices have power constraints that limits their
lifespan. Hence, the limited energy resources become a critical
aspect of IoT networks that must be addressed in the 5G
and B5G wireless communications. Reduction in the trans-
mission power can help the IoT nodes to save some energy
and is one of the two potential solutions for the energy
constraint. The cooperative communication can reduce the
transmit power through transmission in hops and also provide
the diversity gains [2]. The lifespan of IoT nodes can also
be improved through harvesting the energy from incoming
radio-frequency (RF) signal [3]. Thus, the combined strategy
of node cooperation [4] and EH can be applied to the IIoT
networks. In EH, the time-switching and/or power-splitting
schemes are adopted in the literature for information decoding
and harvesting the energy from the received signals. The
practically suitable non-linear EH model was studied in [5]
considering the non-linearity of the EH circuitry for massive
multi-input multi-output (MIMO) systems with simultaneous
wireless information and power transfer (SWIPT) in the beam-
domain (BD), and the proposed BD SWIPT protocol shows the
superiority on spectral efficiency (SE) and energy efficiency
(EE) compared with the traditional approaches.
URLLC is the critical aspect of any IIoT system. Low
latency can be achieved through transmission of shorter length
data packets. Such communication is known as short packet
or finite blocklength (FB) communication. The cooperative
IoT with URLLC serves mission-critical applications [6] like
smart-grid automation, tactile Internet, haptic communica-
tions, traffic safety and control, remote health care, train-
ing surgery, remote manufacturing, industrial application, and
control where ultra-reliability and low latency are of critical
importance.
A. Prior Works and Motivations
There are several works on cooperative device selection
(CDS) and non-linear EH model with infinite blocklength,
however, the CDS has been explored with FB transmission
in limited works. To enhance the transmission efficiency
and receiver reliability, the cooperative communication is
adopted in [4], [7]. The paper [4] considered cooperative
device selection-based model for IoT networks and concluded
that cooperative relaying provides higher throughput than the
automatic repeat request (ARQ) method. However, the analysis
in [4] did not consider the battery-limited nodes, and thus
EH was not considered. The time-splitting (TS) and power-
2
splitting (PS) mechanisms for EH were discussed in [8]. The
outage probability for SWIPT based multiple devices coop-
erative communication network was analyzed in [8]. Further,
for Nakagami-m fading channels, the outage probability of
the amplify and forward (AF) and decode and forward (DF)
cooperative networks with EH was derived in [9]. All these
works are however considered the traditional linear EH model.
The more practical non-linear EH model was proposed in [10].
Further, a hybrid power-time-splitting based (PTS) protocol
was explored in [11] to handle the trade-off between the
harvested energy and reliability.
On the other hand, the cooperation among the devices in
highly dense networks is crucial for fetching the cooperative
diversity gains through proper selection of CDs for the source
(S) to destination communication [7], [9], [12]. The best relay
device was selected by the partial relay selection (PRS) method
in [12], assuming the availability of channel state information
(CSI) of first-hop. Assumption of having the availability of
perfect CSI is not always feasible, thus performance of the
PRS scheme was derived in [7] with channel estimation errors
and feedback delay.
An IIoT has recently attracted increasing attention from both
industrial communities and academics. In [13], a framework
for the deployment of an IoT-based system in industrial envi-
ronments has been developed. In [14], the end-to-end latency
reduction in an IIoT based system by choosing the most-
reliable path with minimal probability of route-flapping has
been described. The authors in [15] introduced the cooperative
and distributed framework to ensure secure energy-trading
and evaluated the performance in terms of reliability and
responsiveness in a vehicular network energy-trade scenario.
The URLLC systems are recently studied in [16]–[20]. In
[16], the authors studied the hybrid time-switching & power-
splitting (TSPS) SWIPT protocol design in a full-duplex (FD)
massive MIMO system. Moreover, the work in [17] investi-
gated the trade-offs among network availability, reliability and
stability in ultra-reliable EH cognitive radio IoT networks.
In [18], the performance of ultra-reliable short message DF
relaying protocol was analyzed. [19] derived the closed-form
expression of a critical BLER, which can be used to efficiently
determine the optimal duplex mode for URLLC scenarios. The
work [20] suggested that the latency is directly proportional
to the length of transmission block and can be effectively
achieved by considering FB transmissions.
To the best of the authors’ knowledge, cooperative device
selection and non-linear energy harvesting with URLLC based
IoT networks have not yet studied in the open literature. More-
over, none of the above-mentioned CDS schemes considered
the unavailability of perfect CSI. Thus, we, in this work,
focus on URLLC based IoT networks with non-linear EH,
and opt for the absolute SNR based scheduling scheme and
compare it with chase combining considering hybrid CSI and
imperfect CSI scenarios for a network consisting of a single
source device, multiple cooperative devices, and a target device
(destination).
Active user
Active user
Active
user
Active user
Target device (TD)
Inactive device
Active device Active device
Active device
Active device
Inactive device
Active device
Active device
Active device as CD
Active device with incorrect decode
Active device with correct decode
CN broadcast signal in CND phase
Forward signal from selected CD in CR phase
Control Node (CN)
Fig. 1: System model.
B. Contributions
Given the surge of IoT applications and progress of newer
generation industrial standards, energy efficient wireless com-
munication solutions for the battery operated devices and
mission-critical applications have become significantly im-
portant. Specifically, in the IoT applications, the EH and
cooperation-based energy-efficient transmission schemes can
be exploited to elongate the battery life of the power-
constrained battery operated nodes. Motivated with this, in the
present work, a cooperative device selection protocol is inves-
tigated for a wireless IoT network in which energy efficiency
is a critical requirement. The considered system is analyzed
for conventional infinite blocklength wireless transmissions
and FB URLLC networks. Specifically, following are our key
contributions through this paper:
We consider an IIoT network where there are Nactive
devices that can assist the downlink communication
from CN to the target device. One of the active devices
which has maximum absolute SNR of downlink channel
from CN is selected as CD.
For the realistic non-linear EH model, the time-
switching and TSPS based non-linear EH protocols are
considered. Moreover, the impact of absolute SNR-based
cooperative device selection is evaluated for communi-
cation and EH performances of the considered system.
Moreover, we compare the performance of the consid-
ered absolute SNR based CDS with the chase combining
scheme.
The closed-form expression for the OP is derived in case
of FB and infinite blocklength transmissions. Further, the
effect of imperfect CSI on the OP is also evaluated. In
addition to this, we derive the closed-form expression of
BLER under URLLC constraints. The performances of
the OP for DF protocol (without a direct link), selection
combining (SC) protocol (with direct link) and compress
and forward (CF) protocol are also compared.
Finally, all the analytical results are validated through
Monte-Carlo simulations.
II. SY ST EM MO DE L
An IIoT environment is considered as shown in Fig. 1,
where the downlink transmission is occurring from CN to
TD. There are Nnumber of active devices that can cooperate
with the CN and forward the message to TD. The CD is
3
selected from the active device using maximum absolute SNR.
The instantaneous CSI is assumed to be available at the
receiver1only. Furthermore, we assume the availability of the
statistical CSI at both the transmitter and receiver ends [4].
As CSI is available at both ends, the absolute SNR based
CD selection scheme is one of the best choices [23]. For
cooperative transmission, the DF relaying is considered. All
the nodes are assumed to operate in half-duplex (HD) mode.
Further, both, the infinite and FB transmissions are considered.
A. Transmission Protocol
The transmission takes place in two successive phases, first
the cooperative node delivery (CND) phase and second the
cooperative relaying (CR) phase. In the CND phase, the signal
transmitted from the CN is received by all the active devices
and the TD. All the active devices decode the signal with 1-ρ
and perform EH with ρfraction of power. The parameter α
denotes the time-switching ratio, ρdenotes the power-splitting
ratio and Tdenotes the time block. Note that, the range of α
and ρare given as (0 < α < 1) and (0 <ρ<1).
αT(1-α)T
CND phase CR phase
TD performs energy harvesting using power received
from the CN TD decodes the signal from the received CD power
Fig. 2: Operation for the case if SNRT D <threshold SNR.
CND phase CR phase
αT (1-α)T
CD performs energy harvesting with PS ratio (ρ)
CD performs information decoding with PS ratio (1-ρ)
Signal transmission from CD to TD
Fig. 3: Operation for the case if SNRT D <threshold SNR.
If the received SNR is above the threshold SNR in the CND
phase, the TD sends an acknowledgment (ACK) message to
the CN and uses all the received power to decode the signal
and doesn’t undergo EH mode.
If the received SNR is less than the target or threshold SNR,
TD sends the negative–acknowledgement (NACK) message
through a dedicated common control channel and uses the total
received signal power in the CND phase to harvest the energy
as shown in Fig. 2. Moreover, in order to ascertain the low
latency communication, it is assumed that the time required
for NACK signal, say τd, is smaller compared to the CND
phase duration αT , i.e., τd<< αT . Hence, for simplicity, we
ignore the τdi.e., τd= 0. All the active devices that have
correctly decoded signals in the CND phase are considered as
cooperative devices (CDs).
In the CR phase, the request-to-send (RTS) packet with the
pilot for the channel gain estimation is sent by all the potential
CDs to the TD after receiving the NACK message from it.
Among all the CDs, the best CD is selected on the basis of
the highest received SNR. The selected CD then forwards the
signal to the TD in the CND phase by using the harvested
1In order to analyse the stochastic behaviour of the wireless channel, the
receiver is trained with known pilot signals [21]. From [22] it is observed
that reference and control signals are loaded at the beginning of the frame
structure in order to reduce the latency.
energy as shown in Fig. 3. Finally, the TD decodes the signal
based on CR phase reception. Without loss of generality, it is
assumed that both phases have equal time duration i.e., α=
0.5and the transmission rates in the two phases are identical.
Further, a time-slotted system is used to avoid the collision
among the active devices while sending RTS to TD, where
each active device sends RTS in allocated time slot only. We
assumed that at least one potential CD exists in the CR phase.
Control signals like NACK (TD to CDs) during the CND phase
and RTS (CDs to TD) during the CR phase are transmitted
through a dedicated common control channel to avoid collision
with the CN message signal.
B. Channel and signal models:
The channel coefficients between CN to jth active device
(or potential CD), CN to TD, and jth active devices to TD are
represented with hCN ,CDj ,hC N,T and hC Dj,T , respectively.
We consider all the non-frequency selective channels to be
independently faded with complex Gaussian fading coefficient
which means the fading magnitude has the Rayleigh density
with parameters 1/(λnl) = E|hnl |2, where E(.)is the
expectation operator. Two nodes are considered at distance
dnl where, nl ∈ {{CN , CD},{C D, T },{C N, T }}. For the
channel coefficient |hnl|2, the probability density function
(PDF) and cumulative distribution function (CDF) can be
given as
f|hnl|2(x) = 1
λe−x/λnl ,(1)
F|hnl|2(x) =1 −e−x/λnl ,(2)
respectively. Here, x∈ {0,∞},λnl is mean value of |hnl|2.
The parameters of the channels between the CN to the
selected CD and selected CD to TD are taken as λ1and λ2,
respectively. In the CND phase, the signals received by TD
and the selected CD are given by
yT1=sPCN
dm
CN,T |hC N,T |xCN +nT,(3)
yCD =sPCN
dm
CN,C D |hCN,C D |xCN +nCD ,(4)
where PCN is the transmitted power from the CN, m is the path
loss exponent and xCN is the transmitted signal with E|xB|2=
1. Here, nT∼ CN(0, σ2
T)and nCD ∼ CN(0, σ2
CD )are the
additive white Gaussian noise at TD and CD respectively.
The selected CD after decoding the signal in the CND
phase, forwards the decoded signal to TD in the CR phase.
The signal received by TD from the selected CD is given as
yT2=sPCD
dm
CD,T |hC D,T |ˆxCN +nT,(5)
where PCD denotes the transmission power of the selected CD
and bxCN is the estimate of xCN which is decoded signal at
the selected CD.
4
III. NON -LI NE AR EH MO DE L
The earlier section describes that when the TD cannot
decode the signal properly, it harvests energy in the CND
phase. We assume that both the CND phase and the CR
phase have the same time duration, i.e., α= 0.5. Moreover,
to capture the joint effects of various non-linear phenomena
caused by hardware limitations, we consider a non-linear EH
model, which is described as
EH =
Emax
1 + e−a(Prf −b)−EmaxΩ
1−Ω,(6)
where Ω = 1
1 + eab , EH indicates the total harvested energy
at the device. Furthermore, Prf and Emax in (6) denote
the received RF power and the maximum harvested power
threshold. Finally, the parameters aand bare determined by
the resistor, capacitor, etc. It is easy to find the parameters a
and bof the proposed model (6) by using a standard curve
fitting tools as the EH hardware circuit of each ER is fixed.
When TD fails to decode the signal in the CND phase, TS EH
protocol is activated at the TD. In the CND phase, the TSPS
based non-linear EH protocol is activated at the CDs under all
the above-discussed scenarios.
In a practical non-linear EH model, the EH depends on
the average power of the harvested signal (Pin). Assuming
the harvested energy from the noise to be negligibly small,
the average power of the harvested signal at the best-selected
CD is given by Pin =PCN h2
CN,C D
dm
CN,C D
. The circuit works only
when Pin reaches the minimum required sensitivity value
Pmin. When Pin reaches the maximum allowable saturation
value Pmax, the output power of the best-selected CD PCD is
constant [24, Fig. 2]. Therefore, PCD with non-linear EH for
CD to TD transmission can be expressed as
PCD =
0,if Pin<Pmin ,
ηραPCN |hC N,CD |2
(1 −α)dm
CN,C D
,if Pmin < Pin < Pmax ,
ηραPmax
(1 −α)dm
CN,C D
,otherwise .
(7)
where ηis the energy conversion efficiency (η∈(0,1)), and
mdenotes path loss exponent.
IV. PERFORMANCE ANALYSIS
In CND phase, using (3) and (4), the SNR at the TD and
CD is given by
γT1=PCN |hCN ,T |2
σ2
T1dm
CN,T
,(8)
γCD =(1 −ρ)(1 −α)T PCN |hC N,CD |2
σ2
CD dm
CN,C D
,(9)
respectively.
The best-selected CD works as a relay which forwards the
signal to TD in the CR phase. Therefore, the SNR at the TD
is given by [11]
γT2=
0,if Pin<Pmin ,
ηραPCN |hC N,CD |2|hC D,T |2
σ2
T2(1 −α)dm
CN,C D dm
CD,T
,if Pmin<Pin < Pmax ,
ηραPmax |hCD,T |2
σ2
T2(1 −α)dm
CD,T
,otherwise .
(10)
Since we adopt an absolute SNR-based scheduling
scheme [25], the CD with maximum instantaneous SNR is
selected. Hence, the instantaneous SNR at the selected CD
can be computed as
γCN,C D = max
iγCN,C Di , i = 1, . . . , N , (11)
where γCN,C Di is defined as
γCN,C Di =(1 −α)(1 −ρ)T PCN |hCN ,CDi |2
σ2
CDi dm
CN,C Di
.(12)
By utilizing Nth order statistics [26], we derive the PDF of
γCN,CD as
fγCN,C D (x) = NfγCN,C Di (x)[FγCN,CDi (x)]N−1.(13)
Here, fγCN,C Di (x)in (13) can be computed with known PDF
of |hCN,C Di |2as
fγCN,C Di (x) = 1
wf|hCN,C Di |2x
w,(14)
where w=(1 −α)(1 −ρ)T PCN
σ2
CDi dm
CN,CDi
.
Thus, the PDF and CDF of γCN,C Di are, respectively, given
by
fγCN,C Di (x) = σ2
CDi dm
CN,C Di
(1 −α)(1 −ρ)T PCN λCD i
×exp −xσ2
CDi dm
CN,C Di
(1 −α)(1 −ρ)T PCN λCD i !,(15)
FγCN,C Di (x) =1 −exp −xσ2
CDi dm
CN,C Di
(1 −α)(1 −ρ)T PCN λCD i !.
(16)
Using (13)-(15), we derive the PDF and CDF of instantaneous
SNR at the best-selected CD and can be, respectively, given
by
fγCN,C D (x) = Nσ2
CDi dm
CN,C Di
(1 −α)(1 −ρ)T PCN λCD i
×exp −xσ2
CDi dm
CN,C Di
(1 −α)(1 −ρ)T PCN λCD i !
×"1−exp −xσ2
CDi dm
CN,C Di
(1 −α)(1 −ρ)T PCN λCD i !#N−1
,(17)
FγCN,C D (x) = "1−exp −xσ2
CDi dm
CN,C Di
(1 −α)(1 −ρ)T PCN λCD i !#N
.
(18)
Note that γCN,C D,T for DF protocol is computed as
γCN,C D,T = min (γCN,CD , γC D,T ).
5
In 5G, the most important use case is URLLC, where
the reliability and low latency is the ultimate aspect that
needs to be considered in the first place. Low latency implies
the use of short data packets. So, the proposed model is
improvized to enable the features of URLLC and to observe
the enhanced performance by the OP analysis. Let CN,C D
and CD,T represent the BLER of CN-CD and CD-TD link,
respectively. In DF protocol, the direct link is always assumed
to be in the outage, and thus, CD collaborates with the CN.
Whereas, in the SC protocol, CD collaborates with CN only if
the TD confirms that the CN transmission was unsuccessful,
and so, the TD requests for retransmission from CD to receive
the frame correctly. The overall BLER using DF protocol [18]
can be expressed as
DF =CN,C D + (1 −CN,C D )CD,T (19)
The overall BLER using SC protocol can be expressed
as [18]
SC =CN ,CD CN,T + (1 −C N,CD )CD,T C N,T (20)
It is observed that pq can be tightly approximated as [19]
pq(rpq )≈E(Q C(γpq )−rpq
pV(γpq)/mpq !),(21)
where C(γpq) = log2(1 + γpq)is the Shannon capacity and
V(γpq) = 1−(1 + γpq)−2log2(e)is the channel disper-
sion that measures the stochastic variability of the channel
relative to a deterministic channel with the same capacity. It
is mathematically intractable to evaluate (i.e., pq(rpq )) in a
closed-form and hence, we use the linear approximation of
Q C(γpq)−rpq
pV(γpq)/mpq !≈K(γpq )given by [27]
K(γpq) =
1,if γpq≤ςpq ,
1
2−ϑpq√mpq (γpq −θpq ),if ςpq < γpq < ξpq ,
0,if γpq > ξpq .
(22)
where ϑpq =1
2Π√22γpq −1,θpq = 2γpq −1,ςpq =θpq −
1
2ϑpq√mpq
and ξpq =θpq +1
2ϑpq√mpq
. The approximation
under consideration is a close-approximation obtained based
on the linearization technique [28]. The closeness of the
approximation can be observed as in Fig. 4, where the linear
approximation and exact expression, making the approxima-
tion suitable for consideration. With the earlier mentioned
approximation of Q-function, BLER of link pq is evaluated by
averaging K(γpq)with respect to probability density function
(PDF) of γpq as pq(rpq )≈R∞
0K(x)fγpq dx. Thus,
pq(rpq )≈K(x)Fγpq ∞
0−Z∞
0
Fγpq (x)dK(x),(23)
where F{·} is the cumulative distribution function (CDF).
Differentiating K(x) and substituting it in (23), pq becomes
pq(rpq )≈ϑpq √mpq Rξpq
ςpq Fγpq (x)dx. Finally, applying the
Gaussian-Chebyshev quadrature method, the pq are obtained
as
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
Fig. 4: Comparison of the linear approximation and exact standard
Q−function.
pq(rpq ) = ξpq −ςpq
2Z
X
z=1
π
Zp1−∅2
zFγpq (w) + RZ,
(24)
where Zis the complexity-accuracy trade-off parameter. The
symbols ∅zand ware the error terms and defined as
∅z=cos (2z−1)π
2Z,(25)
w=∅zξpq −ςpq
2+ξpq +ςpq
2.(26)
Note that RZis the error term, and it becomes negligible for
higher values of Z[11].
The expressions for CN,CD , C D,T and CN ,T are obtained
using (24) and substituted in (19) and (20) to get overall
BLER expression using DF and SC protocols respectively. The
OP analysis with URLLC is observed under the FB regime,
where k represents number of information bits transmitted over
the m0channel uses in each packet transmission. Considering
HD system, the blocklength of each information packet and
the coding rate are same for all links i.e., by mpq =m0/2
channel uses and rpq = 2k/m0bits per channel uses where
pq ∈ {{CN , CD},{C D, T },{C N, T }}, we get simplified
expressions for overall BLER expressions for DF and SC as
DF =CN,C D (rpq) + 1−C N,CD (rpq )CD,T (rpq ),(27)
=ψhFγCN,C D (w)
+1−ψFγCN ,CD (w)FγCD,T (w)i,(28)
SC =CN ,CD (rpq)CN,T (rpq )
+1−CN,C D (rpq)C D,T (rpq)C N,T (rpq ),(29)
=ψ2hFγCN,C D (w)FγCN,T (w)(30)
+1−ψFγCN ,CD (w)FγCD,T (w)FγC N,T (w)i,
where ψ=ξpq −ςpq
2PZ
z=1
π
Zp1−∅2
zand wis defined
in (26). For a pre-defined SNR threshold γth = 2rpq −1, the
6
0 0.2 0.4 0.6 0.8 1
Power-splitting ratio ( )
10-3
10-2
10-1
100
Outage probability (OP)
N = 1
N = 2
SNR = 15 dB
SNR = 20 dB
SNR = 25 dB
Fig. 5: OP of a cooperative IIoT network without
URLLC case against ρ
0 5 10 15 20 25 30
Transmit power of CN (dBm)
0.5
1
1.5
2
2.5
3
3.5
4
Harvested energy (mW) at best selected CD
N = 1
N = 2
N = 5
Fig. 6: Effect of Non the energy harvested (with-
out URLLC case).
0 10 20 30 40 50 60
Transmit power of CN (dBm)
0
0.5
1
1.5
2
2.5
3
3.5
4
Harvested energy (mW) at TD
= 0.15
= 0.45
= 0.85
Fig. 7: Impact of ηon the energy harvested (with-
out URLLC case).
OP of the pq link is evaluated as Ppq =pq(γth ). Thus, the
OP of the proposed system for DF and SC can be illustrated
as
PDF =CN,C D (γth ) + 1−CN,C D (γth)C D,T (γth ),
=PCN,C D + (1 −PCN,C D )PCD,T ,(31)
PSC =CN ,CD (γth )CN,T (γth )
+1−CN,C D (γth)C D,T (γth )CN ,T (γth ),
=PCN,C D PCN,T +1−PCN ,CD PCD,T PC N,T .(32)
V. SIMULATION RESULTS
The derived analytical expressions are validated through
Monte-Carlo simulations for α= 0.5,r0= 2 bits/sec/Hz,
T= 1 ms, ρ= 0.5,Pmin = 0.1PC N ,Pmax = 0.9PCN , [11],
and η= 0.85.
The OP is plotted against PS ratio (ρ)for varying average
SNR number of CDs (N) in Fig. 5. It can be seen that
the OP decreases initially when ρincreases from 0 to an
optimal value, but as ρcontinues to increase beyond the
critical value, although the energy harvested increases, power
is increasingly becoming inadequate for information decoding
at CD. However, for the absolute SNR based scheduling, as the
CD with maximum received SNR is selected, the OP is less
but, it can be observed that the rise in decoding probabilities
by TD in the CR phase overrides the reduction of the same
in the CND phase, and thus it results in the initial fall in the
OP.
The total harvested energy is plotted in Fig. 6 for varying
transmit power of CN and the number of CDs (N) . For
the non-linear EH model the parameters are taken as per the
logistic function as Emax = 3.9mW, a= 1500,b= 0.0022
[29]. It can be seen that the harvested power at selected
CD shows a non-linear behavior and saturates after a certain
optimal value. This is due to the dominance of non-linearity
of components in practical EH circuit. Moreover, sensitivity
and threshold are responsible for maximum limit of energy
that can be harvested and minimum input power required to
harvest the energy. Further, due to the best CD selection, the
total amount of harvested power swiftly increases with N.
Fig. 7 illustrates the impact of ηon the harvested energy
(without URLLC case) for different values of the transmit
power at the CN. We can observe the non-linear behavior
of the harvested power at the TD due to the non-linearity
of components in a practical EH circuit. Furthermore, it can
also be noticed that when the energy conversion efficiency
increases, the harvested power remarkably increases.
Fig. 8 depicts the comparison of different EH protocols,
namely TS, PS, and hybrid PTS at TD for different ηvalues.
We considered the following parameters: (i) TS protocol with
time-splitting ratio (ν) = 0.2, ρ= 0. (ii) PS protocol with
ρ= 0.5 and ν= 0. (iii) Hybrid PTS protocol with ρ= 0.5
and ν= 0.2. In this paper, we adopted the PS protocol. We
observed that the PS protocol provides better EH performance
than TS protocol. However, the hybrid PTS protocol performs
the best and gives the highest EH compared to the PS and
TS protocols. Further, it is noticed that the total amount of
harvested energy significantly increases as ηincreases for all
three EH protocols.
In Fig. 9, we compare the OP vs SNR of a URLLC based
IoT network case for different number of CDs (N) . We
considered λ−1= 3.8dB, σ2
T1=σ2
T2=σ2
CD =−40 dB ,
η= 0.85,ρ= 0.5,m= 128,k= 64,Z= 40,PCN = 30dB.
For the FB regime, we normalize the direct link distance as
dCN,T = 1m. We considered the dCN,CD =dC D,T =dCN,T /2.
It is intuitive that more the active cooperative devices in the
network, the better OP becomes. In contrast, the OP falls when
SNR climbs. In principle, when SNR rises, both TD and CD
harvest more energy, so there is more energy for information
transfer in the next hop. It is also observed that the outage
performance is improved for a URLLC based IoT networks
for increased N.
Fig. 10 shows the comparison of OP of the URLLC based
network considering perfect CSI with an imperfect CSI for
different values of N. The channel estimation error (CEE) for
CN-U channel is defined as CEE =E[||hu−ˆ
hu||2],where
ˆ
hudenotes the estimated channel response corresponding to
the exact channel response huand u∈ {CD , T D}. By
considering different percentage values of CEE, we compared
the outage performance of the system under an imperfect CSI
scenario with a perfect CSI case (i.e., 0 % CEE) for DF
and SC protocols. It is observed that when DF compared to
SC, the OP degrades by 40%,20%,12.5% for perfect CSI,
imperfect CSI oF 20% CEE, and 30% CEE respectively. The
outage performance is slightly appreciable in SC compared to
7
0 10 20 30 40 50
Transmit power of CN (dBm)
0
0.5
1
1.5
2
2.5
3
3.5
4
Harvested energy (mW) at TD
PTS, = 0.15
PTS, = 0.35
PTS, = 0.85
TS, = 0.15
TS, = 0.35
TS, = 0.85
Fig. 8: Comparison of different EH protocols of
the network without URLLC.
0 5 10 15 20 25 30
SNR (dB)
10-6
10-4
10-2
100
Outage probability (OP)
Analytical with N = 3
Simulation with N = 3
Analytical with N = 5
Simulation with N = 5
Analytical with N = 9
Simulation with N = 9
Fig. 9: OP vs SNR of a URLLC based IoT network
case for different number of CDs (N) .
1 2 3 4 5 6 7 8 9 10
Number of CUs (N)
10-8
10-6
10-4
10-2
100
Outage probability (OP)
Imperfect CSI, CEE = 20 %, DF
Imperfect CSI, CEE = 20 %, SC
Imperfect CSI, CEE = 30 %, DF
Imperfect CSI, CEE = 30 %, SC
Perfect CSI, CEE = 0 %, DF
Perfect CSI, CEE = 0 %, SC
Fig. 10: Comparison of perfect CSI with imperfect
CSI.
0 5 10 15 20 25 30
SNR (dB)
10-10
10-8
10-6
10-4
10-2
100
Outage probability
Analytical, N = 3
Analytical, N = 5
Analytical, N = 9
Absolute scheduling
Chase combining,
Trounds = 2
Chase combining,
Trounds = 6
Marker- Simulation
Fig. 11: Comparison of several scheduling schemes
16 18 20 22 24 26 28
SNR(dB)
10-6
10-4
10-2
100
Outage probability
Simulation, DF, N = 3
Simulation, SC, N = 9
Analytical, DF
Analytical, SC
Simulation, DF, N = 9
Simulation, SC, N = 3
CF, N = 3
CF, N = 9
= 0.85
= 0.45
Fig. 12: OP vs SNR of a URLLC based IoT
network case for DF, SC and CF relaying protocols.
0 500 1000 1500 2000
Blocklength (mpq)
10-9
10-8
10-7
10-6
10-5
10-4
Outage probability (OP)
N = 2, DF
N = 2, SC
N = 3, DF
N = 3, SC
N = 5, DF
N = 5, SC
Fig. 13: OP vs blocklength of a URLLC based IoT
network case for DF and SC.
DF. It can be observed that as Nincreases, the performance
degradation of the system decreases. The impact of CEE is
almost negligible for lower values of Nand noticeable for
larger values of N.
Fig. 11 compares the system outage performance of the
absolute scheduling scheme of the URLLC based network
with the chase combining scheme [30] for different number of
CDs (N) and transmission rounds (the number of transmission
attempts from CN to TD) say Trounds. We could observe that
chase combining produces better outage performance than the
absolute scheduling scheme. However, it is worth noting that
the chase combining outage performance varies with respect
to the maximum number of Trounds considered. The reason
for this can be explained as follows: In chase combining,
if the TD decodes the signal correctly, it responds with an
ACK; otherwise, it stores the received signal and responds
with a NACK. Upon receiving a NACK, the CN resends the
same message, which is combined at the TD using maximum
ratio combining (MRC). Then, the TD tries to decode the
combined message, and this process is repeated until success
or maximum Trounds attempts have been made, after which
an error is declared.
Moreover, to reduce the communication latency, we assume
that the NACK is sent if the accumulated SNR is below a
threshold before decoding the entire message. Thus, it only has
to be decoded once when the accumulated SNR is above the
threshold. To conclude, chase combining holds an advantage
of using previously received signal to decode the signal by
combining it with the present signal, which denotes that
the probability of successful decoding of the signal tends to
increase. Further, it is observed that the more the Trounds,
the better the outage performance would be. Nevertheless,
considering the practical application of the system and latency
constraint into account, we restrict the Trounds value not
to exceed the maximum Trounds. This can be illustrated by
observing the figure where the improvement in system outage
performance is observed as Trounds increases from 2 to 5.
For different Trounds, we observed that after a particular
range of SNR, the chase combining gives the same response
irrespective of the number of CDs (N) used. In order words,
the difference in OP for different number of CDs (N) is
negligible for large SNR values, such that it becomes a limit
factor for achieving greater outage performance. Moreover, it
is also intuitive that both the scheduling schemes have their
outage performance improvement as Nincreases from 3 to
9. The OP versus SNR of a URLLC based IoT network case
with three different DF, SC and CF protocols is illustrated
in Fig. 12. We consider the same parameter set as given in
Fig. 9 description. It is observed that the outage performance
of the system with CF is better than that of the DF and SC.
With increased N and η, outage performance improves for all
the three relaying protocols. It is also important to notice that
there is no significant difference observed in CF protocol when
compared with the DF and SC protocols as N increases. When
we compare SC and DF, for a particular number of CDs (N),
the gap between the corresponding curves becomes noticeable
as SNR climb. Selection combining technique improves the
diversity gain and performance of the massive IoT network
8
2 4 6 8 10
Number of CDs (N)
10-10
10-5
100
Outage probability (OP)
mpq= 128, DF
mpq= 128, SC
mpq= 256, DF
mpq = 256, SC
PB = 30 dB
PB = 40 dB
Fig. 14: OP vs Nof a URLLC based IoT network
case for DF and SC.
0 0.2 0.4 0.6 0.8 1
Time-switching ratio ( )
10-3
10-2
10-1
100
Outage probability (OP)
Analytical
Simulation, SC, N = 2
Simulation, DF, N = 2
Analytical
Simulation, SC, N = 1
Simulation, DF, N = 1
SNR = 20 dB
SNR= 25 dB
Fig. 15: OP vs αfor a URLLC based IoT network
case for DF and SC.
1 1.5 2 2.5 3
dB,CU
10-4
10-3
10-2
10-1
100
Outage probability (OP)
Analytical, PB= 30 dB
Simulation, SC, N = 2
Simulation, DF, N = 2
Analytical, PB= 30 dB
Simulation, SC, N = 1
Simulation, SC, N = 1
Analytical, PB = 25 dB
Analytical, PB = 25 dB
Fig. 16: OP vs dCN,C D for a URLLC based IoT
network case for DF and SC.
with cooperative devices.
Fig. 13 depicts the OP against blocklength of a URLLC
based IoT network case for DF and SC. We considered PCN =
40 dB and rest all other parameters same as given in Fig. 9 .
The results illustrate that SC is superior to DF under the FB
regime. It is observed that a cooperative scheme fetches the
advantage of diversity gain and decreases the OP remarkably.
It is obvious that the OP decreases in blocklength. Short packet
transmission has to be encouraged to reduce the latency in the
proposed system. Hence, we adopt the SC protocol, which can
support URLLC under the FB regime with very short packet
lengths.
Fig. 14 illustrates the comparative analysis of OP versus
number of CDs (N) considering different blocklength sizes,
transmit power of CN, and relaying schemes protocols (SC
& DF). It is observed that outage performance improves
significantly as Nincreases. The performance of SC out-
weighs the DF for all the cases. OP decreases as blocklength
increases. Moreover, for increased transmission power of CN,
OP reduces significantly. Overall, OP drops for the increased
N.
Fig. 15 presents the OP as a function of the time-switching
ratio for DF and SC considering different number of CDs (N)
and different SNR values taken at the best-selected CD node.
We consider dCN,C D = 1 m, dCD,T = 3 m, dC N,T = 2 m,
λ−1= 2 dB. Firstly, it can be seen that OP drops as αin-
creases. The reason for the OP degradation is given as follows:
as αincreases, a slight degradation in the outage performance
of the first-hop link ( i.e., CN-CD link) is observed because
the SNR threshold increases. However, the performance of the
second-hop link (i.e., CD-TD link) is significantly improved
due to higher harvested energy and shorter transmission time.
Hence, there exists large transmit power from best-selected
CD to TD. So, the TD receives the signal with higher SNR.
Secondly, it is observed that OP decreases for increased SNR
value from 20 dB to 25 dB. It can also be observed that the OP
of the system with SC performs better than that with DF, and
the system with two CDs provides better outage performance
than the system with one CD. Increasing Nin the system
improves the selection of the best CD, sending the signal to
TD with high probability. Overall, there is an increase in the
probability of correct signal reception by the TD; thus, OP
decreases.
Fig. 16 depicts the OP as a function of the distance between
CN and best-selected CD with CN transmit power as 25 dB
and 30 dB. It can be observed from Fig. 15 that the OP
of the proposed model increases as dCN,C D increases. By
increasing dCN,C D , both energy harvested and the received
signal strength at the best-selected CD node decreases due to
significant path loss. As a result, we observe that there is a
decrease in OP as dCN,C D increases. There is an improvement
in outage performance in the system for increased transmit
power as the strength of the received signal at best-selected
CD is improved.
VI. CONCLUSION
In this paper, a CDS with non-linear EH was analyzed in an
IIoT environment for infinite and FB regimes. It is observed
that when DF compared to SC, the OP degrades by 40%,20%,
12.5% for perfect CSI, imperfect CSI oF 20% CEE, and 30%
CEE respectively. An absolute SNR-based scheme is used for
best CD selection, and the closed-form expression was derived
for OP. The BLER performance of URLLC based IoT network
is analyzed by deriving closed-form expressions for BLER and
OP for FB transmission. Furthermore, the comparative analysis
of OP is performed by considering DF, SC and CF protocols.
Numerical results show that the outage performance of the
URLLC based IoT network system outweighs the performance
of IoT networks without URLLC with the increased number
of CDs (N). Using short packet communication in URLLC
based IoT environment, the latency is reduced and observed
that the considered SC protocol can support URLLC under the
FB regime and outperforms the DF protocol.
In future work, investigating multiple antenna scenario at
CN and CDs to improve the diversity gain might prove
important. New insights will be gained by considering the
potential effects of mobility of the TD and CDs. Furthermore,
determining the optimal αand ρ, and considering the multi-
ple active devices selection as CDs simultaneously could be
another interesting research direction.
APPENDIX
The probability of SNR of CN-TD link is less than the target
SNR, O1is given by
O1=P rγCN ,T < γth =P rPCN |hC N,T |2
σ2
T1dm
CN,T
< γth ,
=P r|hCN ,T |2<γth σ2
T1dm
CN,T
PCN .(33)
9
As given in (2), we can express the O1in CDF form as
O1= 1 −e
γth σ2
T1dm
CN,T
PCN λCN ,T .(34)
The probability of relaying path SNR to be less than the target
SNR for absolute scheduling O2, is given by
O2=P rmin(γCN ,CD , γCD,T )< γth
=1 −P1−P2,(35)
where
P1=P rPmin ≤Pin ≤Pmax ∩γCN ,CD > γth
∩γCD,T > γth ,(36)
P2=P rPin > Pmax ∩γCN ,CD > γth ∩γCD,T > γth .
(37)
Solving for P1, we get (38), shown on the top of the next
page. Thus, P1is given by
P1=(ZB
A
[1 −F|hCD,T |2(C
|hCN,C D |2)]f|hCN,CD |2(x)dx)
×Z∞
γth
f|γCN,C D |2(y)dy . (39)
where A,B,Cand Dare defined as
A=Pmindm
CN,C D
PCN
, B =Pmaxdm
CN,C D
PCN
,
C=γth (1 −α)dm
CN,C D σ2
T1
PCN α η , D =γth (1 −α)dm
CN,C D dm
CD,T σ2
T1
Pmax α η .
Now, solving for P2, we have
P2=P rPin > Pmax ∩γCN ,CD > γth ∩γCD,T > γth ,
=P r PC N |hCN,C D |2
dm
CN,C D
> Pmax ∩γCN,C D > γth
∩ηαPmax |hCD,T |2
σ2
T2(1 −α)dm
CD,T
γth !,
=P r|hCN ,CD |2> B ∩γCN,C D > γth ∩ |hCD,T |2> D,
=h1−F|hCN,C D |2(B)ih1−F|hCD ,T |2(D)i
×Z∞
γth
fγCN,C D (y)dy .
Thus, P2is expressed as
P2=e−B/λ2e−D/λ2Z∞
γth
fγCN,C D (y)dy . (40)
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Sravani Kurma received the B.Tech. degree in
Electronics and Communication Engineering from
the JNTUH college of Engineering, Jagtial, India,
in 2017, and Master’s degree in Communication
System Engineering from Visvesvaraya National In-
stitute of Technology, Nagpur, India, in 2019. She
is currently pursuing Ph.D in Institute of Communi-
cations Engineering (ICE) in National Sun Yat-sen
University, Taiwan. Her current research interests
include 5G, 6G, Industrial internet of things (IIoT),
wireless energy harvesting (EH), simultaneous wire-
less information and power transfer (SWIPT), cooperative communications,
Reconfigurable intelligent surfaces (RIS), Full-duplex communication, cell-
free MIMO, and ultra-reliable and low latency communication (URLLC).
Prabhat Kumar Sharma (Senior Member, IEEE)
received the B.Tech. and M.Tech. degrees in elec-
tronics and communication engineering and VLSI
design from Uttar Pradesh Technical University,
Lucknow, and the Malaviya National Institute of
Technology, Jaipur, respectively, and the Ph.D. de-
gree in wireless communications from the University
of Delhi in 2015. He is an Assistant Professor
with the Department of Electronics and Communica-
tion Engineering, Visvesvaraya National Institute of
Technology, Nagpur, India. Dr. Sharma is a recipient
of the Visvesvaraya Young Faculty Research Fellowship from the Ministry of
Electronics and Information Technology, Government of India. In 2019, He
was awarded the URSI/InRaSS Young Indian Radio Scientist Award by the
International Radio Science Union. Dr. Sharma has authored over 80 journal
and conference papers. His current research interests include physical layer
aspects of wireless, molecular and biological, and quantum communications.
Keshav Singh (Member, IEEE) received the
M.Sc. degree in Information and Telecommunica-
tions Technologies from Athens Information Tech-
nology, Greece, in 2009, and the Ph.D. degree in
Communication Engineering from National Central
University, Taiwan, in 2015. He currently works
at the Institute of Communications Engineering,
National Sun Yat-sen University (NSYSU), Taiwan
as an Assistant Professor. Prior to this, he held the
position of Research Associate from 2016 to 2019 at
the Institute of Digital Communications, University
of Edinburgh, U.K. From 2019 to 2020, he was associated with the University
College Dublin, Ireland as a Research Fellow. He leads research in the areas
of green communications, resource allocation, full-duplex radio, ultra-reliable
low-latency communication, machine learning for communications, and large
intelligent surface assisted communications.
Shahid Mumtaz (Senior Member, IEEE) is an
IET Fellow, IEEE ComSoc and ACM Distin-
guished speaker, recipient of IEEE ComSoC Young
Researcher Award (2020), founder and EiC of
IET “Journal of Quantum communication,” Vice-
Chair: Europe/Africa Region- IEEE ComSoc: Green
Communications & Computing society and Vice-
chair for IEEE standard on P1932.1: Standard for
Licensed/Unlicensed Spectrum Interoperability in
Wireless Mobile Networks. He is the author of 4
technical books, 12 book chapters, 300+ technical
papers (200+ IEEE Journals/transactions, 100+ conference, 2 IEEE best paper
award- in the area of mobile communications. Most of his publication is in
the field of Wireless Communication. He is serving as Scientific Expert and
Evaluator for various Research Funding Agencies. He was awarded an “Alain
Bensoussan fellowship” in 2012. He is the recipient of the NSFC Researcher
Fund for Young Scientist in 2017 from China.
Chih-Peng Li (Fellow, IEEE) received the B.S.
degree in Physics from National Tsing Hua Uni-
versity, Hsin Chu, Taiwan, in June 1989 and the
Ph.D. degree in Electrical Engineering from Cornell
University, Ithaca, NY, USA, in December 1997.
From 1998 to 2000, Dr. Li was a Member of
Technical Staff with the Lucent Technologies. From
2001 to 2002, he was a Manager of the Acer Mobile
Networks. In 2002, he joined the faculty of the
Institute of Communications Engineering, National
Sun Yat-sen University (NSYSU), Taiwan, as an
assistant professor. He has been promoted to Full Professor in 2010. Dr. Li
served as the Chairman of the Department of Electrical Engineering with
NSYSU from 2012 to 2015. He was the Director of the Joint Research
and Development Center of NSYSU and Brogent Technologies from 2015
to 2016. Dr. Li served as the Vice President of General Affairs with NSYSU
from 2016 to 2017. He is currently the Dean of Engineering College with
NSYSU. His research interests include wireless communications, baseband
signal processing, and data networks.
Dr. Li is currently the Chair of the IEEE Broadcasting Technology Society
Tainan Section. Dr. Li also serves as the Editor of the IEEE Transactions on
Wireless Communications, the Associate Editor of the IEEE Transactions on
Broadcasting, the General Co-Chair of IEEE Information Theory Workshop
2017, and the Member of Board of Governors with IEEE Tainan Section. Dr.
Li was the lead guest editor of the Special Issue of International Journal of
Antennas and Propagation. He was also the recipient of the 2014 Outstanding
Electrical Engineering Professor Award of the Chinese Institute of Electri-
cal Engineering Kaohsiung Section and the 2015 Outstanding Engineering
Professor Award of the Chinese Institute of Engineers Kaohsiung Section.
