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Engineering, Technology & Applied Science Research Vol. 13, No. 4, 2023, 11318-11325 11318
www.etasr.com Alotaibi: Interference Mitigation Strategy for D2D Communication in 5G Networks
Interference Mitigation Strategy for D2D
Communication in 5G Networks
Sultan Alotaibi
College of Computer and Information Systems, Umm Al-Qura University, Saudi Arabia
srotaibi@uqu.edu.sa (corresponding author)
Received: 2 May 2023 | Revised: 26 May 2023, 4 June 2023, and 7 June 2023 | Accepted: 10 June 2023
Licensed under a CC-BY 4.0 license | Copyright (c) by the authors | DOI: https://doi.org/10.48084/etasr.6008
ABSTRACT
Device-to-Device (D2D) communication is one of the most promising developments in 5G networks. D2D
communication can reduce communication latency and increase spectrum efficiency and system capacity.
However, implementing D2D communication in reuse mode with a traditional cellular network can result
in severe interference that can significantly reduce network performance, as both networks share the same
resources. It is essential to mitigate interference to improve network capacity when D2D communication
coexists with traditional cellular networks. An effective power control strategy can help mitigate the
potential detrimental impact of interference and maximize the potential benefits of D2D communication.
In this study, a dynamic transmission power control strategy was proposed for D2D transmitters to allow
them to dynamically adjust transmission power, aiming to minimize interference and maximize network
capacity. The distance between and the number of D2D pairs that experience interference were the
primary parameters considered for the proposed strategy, while its complexity was in polynomial time. A
simulation was carried out to evaluate the proposed strategy and compare it with fixed and ranged-based
approaches, and the results validated its effectiveness.
Keywords-5G; D2D; interference; power control
I. INTRODUCTION
All wireless communication systems have shown
impressive improvements during the past few decades.
However, the increasing demand for communication services is
not being met by the current state of wireless network
improvement. The demand for ubiquitous high-speed Internet
access and the increase in the average number of connections
per user have led to meteoric development in all these metrics
for today's wireless communication systems. Additionally,
current wireless systems are not equipped to handle the
increasing demand for hyperrealistic multimedia services, such
as those used in vehicle-to-vehicle, wireless health
applications, virtual reality, and Internet of Things (IoT)
applications, and the use of wireless networks as essential
broadband access service [1]. Mobile traffic is expected to
expand at an annual rate of about 55% in 2024-2030 [2], which
is a huge demand in terms of network resources and link
capacity. Moreover, effective management of limited resources
is required to keep up with this growing demand. Existing
wireless network systems might not be able to handle the
explosive growth in demand that future wireless network
applications will require.
According to recent developments, a new 5G network
design is required to address spectrum challenges and satisfy
customers' demands for high-speed connectivity. Increased
mobile data volumes, lower latency, huge network
connectivity, and stable experience quality are all necessary for
the 5G wireless network to provide seamless user experiences.
Several essential technologies have been proposed to enable the
5G network to achieve these goals. Technologies include
massive Multiple-Input Multiple-Output (MIMO), Device-to-
Device (D2D), millimeter wave (mm-wave), and Ultra-Dense
Network (UDN). D2D communication is considered a
promising technology for 5G networks, as it allows nearby
devices to be directly connected without going through the
main base station. In addition, D2D communications can reuse
cellular resources [3-6]. D2D communication can be used as an
underlying layer in cellular networks to support communication
between nodes that are adjacent to each other with minimal
latency and energy consumption. In addition, D2D
communications would be implemented to offload traffic from
Macrocell BS, which is the fundamental driver to implement
them. D2D technology has great potential to facilitate
proximity-based applications, as proximity between connected
devices improves spectrum utilization and supports the power
efficiency of cellular networks [7-9].
The D2D communication procedure consists of the
discovery and communication stages. In the D2D discovery
stage, D2D helps User Equipment (UE) to find nearby devices
that might be able to communicate directly with it. In the
communication stage, newly discovered D2D users set up
communication channels to share information. Procedures for
establishing a connection between two or more D2D users in
proximity are what this stage is all about. Managing the
interference introduced by the D2D transmitter and maximizing
the efficiency of the UE power consumption have become
essential requirements for the implementation of D2D
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communication [10]. This is the case despite the many
advantages that D2D communications offer, which function in
the underlay mode. For example, cellular links have to deal
with cross-tier interference from D2D transmissions. D2D links
should deal with interference between D2D transmissions, but
also with interference between cellular links and D2D
transmissions. The interference problem could be solved by
multiple approaches. The Power Control approach is an
essential method that can be used by cellular networks to
regulate interference, protect Cellular User Equipment (CUE),
and provide communications that minimize power usage [11-
13]. Cellular networks that support D2D communications are
susceptible to interference on both types: co-tier and cross-tier.
Co-tier refers to the interference between D2D pairs that share
the same resources, while cross-tier refers to the interference
between D2D and cellular users in the same channel. Cross-tier
interference occurs when the resource that is allocated to a user
of the cellular network is reused by one or more users of the
D2D network. Two types of interference occur when D2D
communication coexists with conventional cellular networks:
D2D-to-cellular interference: Depending on the direction of
transmission, interference from D2D communication can
occur at either the base station or the CUEs. In the uplink
direction, the interference is received by the base station
while it receives data from its CUE. In the downlink
direction, CUE experiences interference from the D2D
transmitter while receiving data from the Macrocell BS.
Cellular-to-D2D interference: The direction of
communication can also play a role in interference between
cellular and D2D networks. The CUE that transmits to the
base station is the source of interference to the D2D
receiver in terms of the uplink transmission mode. In the
downlink direction, the Macrocell BS would be the source
of interference for the D2D receiver. Furthermore,
undesirable mutual interference would be experienced
when multiple D2D pairs compete for the same resource.
D2D users in different pairings will always experience
interference from each other, regardless of the direction of
the transmission mode.
Sharing the same spectrum introduces the problem of
interference. There are two main strategies for allocating the
spectrum for D2D communication: in-band and out-band [14].
In the in-band strategy, the same licensed spectrum is shared
between D2D and cellular communication. In the out-band
strategy, D2D communication uses an unlicensed spectrum.
The coexistence of cellular networks and D2D communication
imposes the networks' elements to use the same frequency
band. As a result, the problem of interference arises. Thus, a
power control strategy is needed to solve this problem [15].
Moreover, the power control strategy could improve the use of
the UE battery, as this issue has become an active research
topic in the industry and academic community [16-17].
This study focused on the power control issue of D2D
communication, to autonomously control transmission power.
The D2D transmitter should be able to dynamically adjust its
power to mitigate the undesirable impact of interference and
improve network capacity.
II. RELATED WORKS
Power control is recognized as a promising strategy that can
be used to mitigate interference. The geometric water-filing
method was used in [18] to control the transmission power of
the D2D transmitter. The proposed mechanism also included a
resource allocation algorithm to distribute subcarriers among
UEs, assuming that the transmission power is infinite, where in
reality the batteries of UEs need to be recharged, so the
transmission power is finite. In [19], two stochastic geometry-
based power control algorithms were developed for
interference coordination in underlaid D2D cellular networks,
aiming to provide effective power control and showing that it
was possible to increase the total capacity of the network.
However, interference could not be canceled but can have an
acceptable impact while still accounting for uplink
transmission channels in a hybrid random network. In [20], an
innovative power control strategy was presented for dense
cellular networks operating in a frequency reuse strategy. This
technique relied on setting the transmission power of D2D
devices to reduce the interference they could cause. This study
was conducted using a single scenario, and the findings
suggested that introducing D2D communication into cellular
networks could improve system capacity. In [21], a resource-
sharing mechanism was proposed for channel assignment and
power distribution in a downlink scenario for D2D
communication, without affecting the quality of service. Each
pair of D2D reused multiple channels from a variety of CUEs,
and multiple pairs of D2D could share the same channels that
were used by a single CUE. The Lagrangian dual optimization
method was used to assign channels with appropriate power
transmission for each assigned channel to increase the overall
data rate of D2D pairs to its maximum potential.
The effectiveness of D2D communication for an uplink
transmission mode in an underlay mm-wave-based technology
was discussed in [22]. The mm-wave spectrum was responsible
for a significant amount of interference and led to an
attenuation of the user's signal due to path loss. Maintaining
transmission power between predetermined higher and lower
limits, the suggested power control method guaranteed the
highest possible throughput, and the reduced outage probability
of the proposed approach ensured greater performance, as it
enabled D2D communication in the 5G mm-wave network. In
[23], a dynamic power control strategy was proposed to reduce
interference and improve network performance, improving
overall system throughput with consistent and dynamic power
regulations that considered channel conditions. In [24], a power
control mechanism for smart grids was presented, which could
incorporate contemporary communication technologies with
issues related to power supply management. The results of the
suggested scheme demonstrated superior performance
compared to similar current schemes. In [25], graph theory was
used to optimize resource allocation, aiming to increase ergodic
sum rates. The bipartite graph was constructed according to the
required outage probability constraints, and the Hungarian
algorithm was used to identify the best solution. The simulation
results showed an improved constrained spectrum efficiency of
D2D pairs. However, the complexity of the proposed algorithm
was high.
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An overlapping coalition game scheme was proposed in
[26], where each DUE could reuse several Resource Blocks
(RBs), and numerous DUEs could share a single spectrum. In
addition, the proposed method ensured the security of the entire
system and increased performance by increasing the system
sum rate to its maximum. In [27], power optimization systems
were proposed to coordinate interference between
communication channels, prioritize cellular communication,
and set a maximum data rate for each link. Performance
evaluations showed a considerable increase in data rates after
taking into account all factors. In [28], a technique was
presented to optimize power management in 5G IoT networks.
The experimental results showed that the proposed strategy
performed well in terms of accurately predicting battery life
and maintaining a specific degree of network connectivity. In
addition, an effective channel selection and power allocation
strategy was proposed in [29] to reduce D2D-to-cellular
interference. Furthermore, it increased the spectral efficiency in
scenarios where each D2D pair could reuse numerous cellular
subcarriers. The optimal power of the D2D user was calculated
using the Lagrangian approach to optimize the D2D data rates,
as well as to maintain the quality of service for the cellular
user. Furthermore, this study explored how the location and
distance between D2D and cellular users could affect the
throughput performance of D2D pairs.
In [30], a resource allocation strategy based on Soft
Frequency Reuse (SFR) was proposed, taking into account both
licensed and unlicensed bands for D2D communications and
using power control to prevent interference. The simulation
results showed that the suggested strategy outperformed the
traditional allocation scheme, which used only the licensed
band and did not support D2D in terms of system capacity and
blockage rate. In [31], a strategy was proposed to control
transmission power, considering only a single CUE that was
sharing resources with multiple D2D pairs. The assumptions of
this study relied on predetermined values such as the constant
distance between the D2D transmitter and receiver, constant
transmission power, and constant SINR thresholds. The
derivation of the analytical model was simplified by these
deterministic assumptions, which may often be unrealistic.
This study aimed to:
Propose a dynamic transmission power strategy for D2D
transmitters. The proposed strategy aimed to control the
transmission power for D2D transmitters under interference
and capacity improvement constraints, enable D2D
communication to mitigate interference, and run in
polynomial time.
Evaluate the proposed transmission power control strategy
based on a 5G environment model, simulating a real-time
environment for 5G networks.
Carry out a MATLAB simulation to evaluate the
performance of the proposed transmission power control
strategy, use different performance metrics to investigate its
feasibility, consider the average and the gained average
throughput of the system, and measure the range of the
transmission power value of the D2D transmitters.
III. SYSTEM MODEL
This study considered a single Macrocell scenario, where
the Macrocell BS was located in the cell's center. Set D
represents all D2D pairs in the system where D = [ 1, 2, 3,...,
n]. The D2D pairs were randomly distributed within Macrocell
coverage. In addition, the in-band spectrum strategy was
assumed, considering an underlay mode, where D2D
communication and CUE used the same licensed spectrum.
Accordingly, D2D communication would reuse the resources
of CUE. Thus, the problem of interference would increase.
This study considered cross-tier interference. There are two
scenarios of interference in this context. Figure 1 shows the
scenario of the downlink transmission mode, where the CUE
experiences interference from the D2D transmitters. Figure 2
shows the scenario of the uplink transmission mode, where
D2D transmitters produce interference on the Macrocell BS.
Fig. 1. Downlink scenario.
Fig. 2. Uplink scenario.
First, the downlink transmission mode is discussed.
Assuming PTx is the transmitted power of the Macrocell BS, the
received power PRx by CUE in terms of the downlink is
modeled as follows:
��� + ��� . � + ∑ �� . ��
�
(1)
where GM denotes the channel gain between the Macrocell BS
and its attached CUE, given as follows:
� = �� . � (2)
GD denotes the channel gain between CUE and its interfered
D2D transmitter, given as follows:
� = ��� . �� (3)
where PLΜ represents the path loss between the Macrocell BS
and its CUE. PLD represents the path loss between the D2D
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transmitter and CUE, λM represents the small-scale fading of
the Macrocell BS channel and its CUE, and λD represents the
small-scale fading of the D2D pair channel to the cellular UE.
The calculation of the distance between the transmitter and
the receiver nodes influences the calculation of the path loss.
The distance between the transmitter and receiver nodes causes
a propagation loss. Thus, the distance between the transmitter
and receiver is given according to the following formula [32]:
�� = ��
� + ��
� − 2 � �� ����� (4)
where r represents the radius, and θi is randomly distributed in
[0, 2π]. In the downlink transmission mode, the received signal
by the CUE is given as follows [33]:
� = � �� �
�� ℎ + �� ��� ��
�� ℎ� (5)
where hM represents the signal transmitted from the Macrocell
BS, hD represents the signal transmitted from the D2D
transmitter, � �
�� denotes the received power from the
Macrocell BS, �� ��
�� denotes the received interfering signal
from the D2D transmitter, and a is the path loss coefficient.
Path loss is given as follows [34]:
�� ! = 128.1 + 37.6 '�() *Km- (6)
where d is the distance in Km. Equation 6 is used when CUE is
linked to the Macrocell BS, CUE is linked to the D2D, and
D2D is linked to the Macrocell BS. However, the path loss for
D2D communication is given as follows:
��� ! = 148 + 40 '�() *01- (7)
In terms of downlink, the SINR at CUE can be modeled
according to the following:
2�3 =
45 . 65,89:
∑ ;<= 4> . 6>,89:?@A
(8)
where ε has a value of 1 when the D2D pair and the CUE use
the same subcarrier and 0 when this condition is not met.
System noise is represented by N0. In terms of uplink
transmission mode, the SINR at Macrocell BS can be
formulated as follows:
2B3 =
489: . 689:,5
∑ ;<= 4> . 6>,5?@A
(9)
where PCUE represents the transmit power of CUE, PD
represents the interfering signal transmitted by the D2D
transmitter, GCUE,M represents the channel gain between the
CUE and Macrocell BS, and GD,M represents the channel gain
between the D2D and Macrocell BS.
IV. THE PROPOSED POWER CONTROL STRATEGY
An adequate transmission power control strategy could play
an essential role in mitigating the impact of interference
between network elements and improving the performance of
the network. In this study, a transmission power control
strategy was developed to alleviate interference between the
Macrocell BS and D2D pairs and increase the capacity of the
whole network. The main idea behind the proposed
transmission power control strategy was to assist the D2D
transmitter in adjusting its transmission power autonomously.
The proposed transmission power control strategy allows D2D
pairs to communicate with each other, addressing the
interference challenge. The D2D transmitter reduces its
transmission power when it interferes with adjacent CUEs and
D2D pairs.
The Macrocell BS transmission power is assumed to be an
essential parameter for the proposed transmission power
control strategy. In addition, the coverage of the Macrocell
where the D2D pairs are located is considered by the proposed
transmission power control strategy. Consequently, the
proportion between the Macrocell BS transmission power and
its coverage is identified to derive a suitable transmission
power level for the D2D transmitter. Thus, a proportional
degree between the Macrocell BS transmission power and its
radius should be recognized. The following formula was used
to configure the proportional degree between Macrocell BS
transmission power and its radius:
� =
4CD
EF
(10)
where PTx is the Macrocell BS transmission power, and χm is
calculated as follows:
GH = '�(
IJKHL (11)
where Rm is the Macrocell radius. Accordingly, the D2D
transmitter established its transmission power based on the
following formula:
��M = 1NOP1Q1J� . G� , ��5ST L (12)
��5ST was set to 20 dBm and χD was calculated as follows:
G� = '�(
IJK� L (13)
where RD represents the distance between the D2D transmitter
and the D2D receiver.
The D2D transmitter should determine the number of
victim elements that interfered with its transmitted signals. In
this case, the coverage of the D2D transmitter is considered to
have a number of victims that are affected. The coverage
depends on the distance between the D2D transmitter and the
D2D receiver. The following formula was used to specify the
area where other network elements could be affected and
interfered with:
U = V J2 . K� L (14)
where A represents the threshold of the possible distance in
which network elements could interfere with and be affected by
the D2D transmitter signal. After the number of victims is
recognized, the D2D transmitter can derive a cost function to
reduce its transmission power based on the impact of the
interference on adjacent victims. The cost function was
calculated for each active D2D pair according to:
2� =
WX
Y
(15)
where τ represents the total active elements of the system such
as the D2D transmitter and CUE, and Ωi represents the number
of elements that interfered with a D2D transmitter. Table I is
used to construct Ωi for every active element in the system.
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TABLE I. CONSTRUCTED COST FUNCTION
E1 E2 E3 … En
E1 0 1 0 … 1
E2 1 0 1 … 1
E3 0 1 0 … 0
E4 1 1 0 …
… … … … … …
En 1 1 0 … 0
Ωi Ω1=3 Ω2=4 Ω3=1 … Ωn=2
Accordingly, the proper level of transmission power for
every D2D transmitter can be derived based on the following:
��X JMZ=L = ��X M − [��X M . 2� \ (16)
The pseudo-code of the proposed transmission power
control strategy is presented below.
Inputs ← ���, KH , ^
Get:GH , �
while ^ ≤ 1 do
For P = 1 to ^ do
Compute: ��X M = � . G� �
End For
For P = 1 to ^ do
Compute U�
Compute: 2� =
WX
Y
Compute: ��X JMZ=L = ��X M − [��X M . 2� \
End For
End While
END
V. RESULTS
A simulation was carried out to evaluate the proposed
transmission power control strategy in a single Macrocell
scenario, with the Macrocell BS located in the cell's center.
D2D pairs and CUEs were randomly located within the
Macrocell extent. As D2D pairs would increase network
capacity and interference, the distance between the D2D
transmitter and the receiver should not be long. In this context,
the maximum distance between the D2D transmitter and
receiver was set at 40 m. D2D communication was used to
offload traffic from the Macrocell BS, using the model
mentioned above. Channel conditions such as noise and path
loss were simulated, and Table II summarizes the assumptions
and simulation parameters. The antenna type was
omnidirectional, the white noise spectral density was -174
dBm/Hz, and the channel bandwidth was 15 MHz. Interference
occurs when the D2D pairs communicate because the spectrum
resources are reused. Therefore, a power control/management
strategy was used to alleviate the impact of undesirable
interference. According to 3GPP standards, UEs are classified
into four classes based on their frequency band, and there is a
minimum and maximum level of transmit power for each class.
This study considered class 3 for UEs, which represent D2D
transmitters. The minimum transmission power was set at 23
dBm for class 3 UEs for all frequency bands, and this class is
an obvious example of handheld UEs [35]. The maximum
distance between the D2D elements was set to 40 m, to ensure
that the D2D transmitters are connected to the closest receivers
to mitigate interference and avoid excessive power.
TABLE II. SIMULATION PARAMETERS
Parameters Value
Maximum distance between D2D elements 40 m
Maximum D2D Tx power - ��5ST 23 dBm
Fixed D2D Tx power - �� 23 dBm
Macrocell Tx power - ��� 46 dBm
Frequency band 1900 MHz
Channel bandwidth 15 MHz
Antenna mode Omni-Directional
Maximum number of D2D pairs 25 Pairs
N0 -174 dBm/Hz
Three power control methods were used in the simulation;
the proposed transmission power control strategy, the fixed-
based approach with fixed transmission power at 20 dBm
without changes, and the range-based approach using (12). The
third approach adjusts the transmission power of the D2D
transmitter based on its distance from the receiver. The fixed-
based approach was considered because it is used in real
implementations where the D2D transmitter uses the maximum
predefined transmission power. The range-based approach
adjusts the transmission power according to the distance
acknowledged between the transmitter and the receiver to
alleviate interference by decreasing the transmission power
when the distance is short.
Figure 3 shows the network average throughput, presenting
the total capacity of the network, including Macrocell and the
active D2D pairs. The number of D2D pairs increased in each
run to analyze the simulated power control strategies. The
network capacity increased with the number of D2D pairs
because traffic was offloaded to D2D pairs. The fixed
transmission power strategy provided the best average
throughput when the number of D2D pairs was low. In this
case, the capacity was high because the D2D transmitter
operates with the highest possible transmission power.
Transmission power affects the quality of the transmitted
signal, which in turn affects the data rate. However, the
performance of the fixed transmission strategy decreased when
the number of D2D pairs increased to more than 10. The
average throughput of the fixed transmission power approach
with 5 D2D pairs was better than with 10 or 15 D2D pairs since
the interference in the first case was less. The figure shows that
the average throughput of the fixed transmission power
approach constantly increased when the number of D2D pairs
became more than 15. The range-based strategy provided the
worst average throughput, as its behavior did not change with
the number of D2D pairs. The range-based power control
strategy tended to control the transmission power of the D2D
transmitter according to the calculated distance from the
receiver. As a result, the short distance between them leads to
low transmission power and low data rates. On the other hand,
the proposed transmission power control strategy could control
transmission power considering interference and network
capacity, providing acceptable average throughput for less than
10 D2D pairs. In this case, the proposed strategy delivered
lower average throughput than the fixed transmission power
approach, but higher than the range-based approach. However,
for more than 10 D2D pairs, the proposed transmission power
control strategy provided the best average throughput, as it
increased by nearly 11% and 35% compared to the fixed and
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range-based approaches, respectively. The proposed
transmission power control strategy constantly increased the
average network throughput four times when the number of
D2D pairs increased from 1 to 25.
Fig. 3. The network average throughput.
Figure 5 shows the average throughput achieved by
implementing D2D communication. Average throughput
decreased as the number of D2D pairs increased due to the
undesirable interference caused by them. In this case, a power
control strategy is needed to mitigate interference and preserve
the average gained throughput after implementing D2D
communication. The range-based strategy delivered the worst
average throughput, as its average throughput constantly
decreased when the number of D2D pairs increased. According
to the figure, the average throughput delivered by the range-
based strategy decreased by nearly 73% when the number of
D2D pairs increased from 1 to 25. The fixed transmission
power approach delivered the best average throughput when
the number of D2D pairs was less than 10. However, when the
number of D2D pairs increased to more than 10, its average
throughput decreased. The fixed transmission power approach
performed well for less than 10 D2D pairs, but this does not
support a real environment where the number of D2D pairs
would exceed this number. The fixed transmission power
approach performs better with fewer D2D pairs because the
distance between different distributed D2D pairs is long. As a
result, the impact of interference on distributed D2D pairs is
not robust. However, when the number of D2D pairs increases,
the impact of interference becomes robust. The average
throughput of the fixed transmission power approach decreased
by nearly 65% when the number of D2D pairs increased from 1
to 25. On the other hand, the proposed transmission power
control strategy delivered the best average throughput for more
than 10 D2D pairs. The proposed transmission power control
strategy performed the best, preserving the gained capacity, as
it managed the transmission power considering both the
interference and throughput aspects. Mitigating interference
leads to increased capacity. Thus, the average throughput
degradation of the proposed strategy is the least compared to
the others. The average throughput of the proposed
transmission power control strategy decreased by nearly 58%
when the number of D2D pairs increased from 1 to 25.
According to Figure 4, the proposed transmission power
control strategy performed better when the number of D2D
pairs increased, which simulates a real environment, while the
fixed transmission power approach performed better for less
than 10 D2D pairs.
Fig. 4. D2D pairs average throughput.
Figure 6 shows the average transmission power for the D2D
transmitters. As the transmission power of the fixed
transmission power approach was set to 20 dBm, there were no
changes when the number of D2D pairs changed. However, the
proposed transmission power control strategy enables D2D
transmitters to change their transmission power to mitigate
interference, especially when the number of D2D pairs
increases. Consequently, the average transmission power
ranged between 12 and 16 dBm. In addition, the average
transmission power level constantly decreased as the number of
D2D pairs increased. The average transmission power of the
proposed strategy did not reach maximum, while it provided
the best throughput compared to the other strategies. The
average transmission power level of the range-based strategy
was the lowest, as it ranged between 3.5 and 7 dBm, but
delivered the worst performance in terms of average
throughput compared to the other approaches. The proposed
transmission power control strategy had 15% and 30% better
network capacity compared to the fixed-based and range-based
approaches, respectively. The average throughput of the D2D
pairs for the proposed transmission power control strategy was
16.6% and 35% better than the fixed-based and range-based
approaches, respectively.
Fig. 5. Average transmission power for D2D transmitters.
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When the number of D2D pairs increases beyond 25, two
scenarios will happen according to the simulation results. At
first, the throughput of the entire network would increase as
traffic is offloaded from the main cell to D2D pairs. However,
the level of additional capacity gained for the entire network
would remain stable as D2D pairs are added due to
interference. In the second scenario, the throughput of the D2D
pairs communication would decrease when the number of D2D
pairs increases due to interference and its undesirable impact
on throughput.
VI. CONCLUSION
D2D communication has been recognized as one of the
most promising developments in 5G networks, as it allows
nearby devices to be directly connected without going through
the main base station, improving network capacity. However,
managing the interference introduced by the D2D transmitter
and maximizing the efficiency of the UE power consumption
have become essential requirements for implementing D2D
communications. This study proposed a dynamic transmission
power control strategy for D2D transmitters to allow them to
dynamically adjust their transmission power, alleviate
interference, and improve network capacity. The main factors
considered in the proposed strategy were the number and
distance of the D2D pairs that are affected by interference. A
simulation was carried out to validate the proposed
transmission power control strategy, and the results
demonstrated its effectiveness compared to the fixed and range-
based approaches.
REFERENCES
[1] "Wireless Technology Evolution towards 5G: 3GPP Release 13 to
Release 15 and Beyond," 5G America, Bellevue, WA, USA, Feb. 2017.
[2] A. A. Dejen, Y. Wondie, and A. Förster, "Survey on D2D Resource
Scheduling and Power Control Techniques: State-of-art and Challenges,"
EAI Endorsed Transactions on Mobile Communications and
Applications, vol. 7, no. 21, May 2022.
[3] F. O. Ombongi, H. O. Absaloms, and P. L. Kibet, "Energy Efficient
Resource Allocation in Millimeter-Wave D2D Enabled 5G Cellular
Networks," Engineering, Technology & Applied Science Research, vol.
10, no. 4, pp. 6152–6160, Aug. 2020, https://doi.org/10.48084/
etasr.3727.
[4] Z. Hussain, A. ur R. Khan, H. Mehdi, and S. M. A. Saleem, "Analysis of
Device-to-Device Communication over Double-Generalized Gamma
Channels," Engineering, Technology & Applied Science Research, vol.
8, no. 4, pp. 3265–3269, Aug. 2018, https://doi.org/10.48084/etasr.2230.
[5] Z. Hussain, A. ur R. Khan, H. Mehdi, and S. M. A. Saleem, "Analysis of
D2D Communication System Over κ-μ Shadowed Fading Channel,"
Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp.
3405–3410, Oct. 2018, https://doi.org/10.48084/etasr.2291.
[6] D. Wang, H. Qin, B. Song, X. Du, and M. Guizani, "Resource
Allocation in Information-Centric Wireless Networking With D2D-
Enabled MEC: A Deep Reinforcement Learning Approach," IEEE
Access, vol. 7, pp. 114935–114944, 2019, https://doi.org/10.1109/
ACCESS.2019.2935545.
[7] G. Fodor et al., "Design aspects of network assisted device-to-device
communications," IEEE Communications Magazine, vol. 50, no. 3, pp.
170–177, Mar. 2012, https://doi.org/10.1109/MCOM.2012.6163598.
[8] "Feasibility study for Proximity Services (ProSe)," 3GPP, Sophia
Antipolis, France, 22.803, 2013.
[9] X. Lin, J. G. Andrews, A. Ghosh, and R. Ratasuk, "An overview of
3GPP device-to-device proximity services," IEEE Communications
Magazine, vol. 52, no. 4, pp. 40–48, Apr. 2014, https://doi.org/
10.1109/MCOM.2014.6807945.
[10] P. Mach, Z. Becvar, and T. Vanek, "In-Band Device-to-Device
Communication in OFDMA Cellular Networks: A Survey and
Challenges," IEEE Communications Surveys & Tutorials, vol. 17, no. 4,
pp. 1885–1922, 2015, https://doi.org/10.1109/COMST.2015.2447036.
[11] Y. Yuan, T. Yang, H. Feng, and B. Hu, "An Iterative Matching-
Stackelberg Game Model for Channel-Power Allocation in D2D
Underlaid Cellular Networks," IEEE Transactions on Wireless
Communications, vol. 17, no. 11, pp. 7456–7471, Aug. 2018,
https://doi.org/10.1109/TWC.2018.2867474.
[12] H. Takshi, G. Doǧan, and H. Arslan, "Joint Optimization of Device to
Device Resource and Power Allocation Based on Genetic Algorithm,"
IEEE Access, vol. 6, pp. 21173–21183, 2018, https://doi.org/10.1109/
ACCESS.2018.2826048.
[13] C.-S. Hsu and W.-C. Chen, "Joint Power Control and Channel
Assignment for Green Device-to-Device Communication," in 2018 IEEE
16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th
Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big
Data Intelligence and Computing and Cyber Science and Technology
Congress(DASC/PiCom/DataCom/CyberSciTech), Athens, Greece, Dec.
2018, pp. 881–884, https://doi.org/10.1109/DASC/PiCom/
DataCom/CyberSciTec.2018.00-14.
[14] J. Liu, N. Kato, J. Ma, and N. Kadowaki, "Device-to-Device
Communication in LTE-Advanced Networks: A Survey," IEEE
Communications Surveys & Tutorials, vol. 17, no. 4, pp. 1923–1940,
2015, https://doi.org/10.1109/COMST.2014.2375934.
[15] L. Han, Y. Zhang, X. Zhang, and J. Mu, "Power Control for Full-Duplex
D2D Communications Underlaying Cellular Networks," IEEE Access,
vol. 7, pp. 111858–111865, 2019, https://doi.org/10.1109/ACCESS.
2019.2934479.
[16] A. Bulashenko, S. Piltyay, and I. Demchenko, "Energy Efficiency of the
D2D Direct Connection System in 5G Networks," in 2020 IEEE
International Conference on Problems of Infocommunications. Science
and Technology (PIC S&T), Kharkiv, Ukraine, Jul. 2020, pp. 537–542,
https://doi.org/10.1109/PICST51311.2020.9468035.
[17] K. K. Nguyen, T. Q. Duong, N. A. Vien, N.-A. Le-Khac, and M.-N.
Nguyen, "Non-Cooperative Energy Efficient Power Allocation Game in
D2D Communication: A Multi-Agent Deep Reinforcement Learning
Approach," IEEE Access, vol. 7, pp. 100480–100490, 2019,
https://doi.org/10.1109/ACCESS.2019.2930115.
[18] L. Ferdouse, W. Ejaz, K. Raahemifar, A. Anpalagan, and M.
Markandaier, "Interference and throughput aware resource allocation for
multi-class D2D in 5G networks," IET Communications, vol. 11, no. 8,
pp. 1241–1250, 2017, https://doi.org/10.1049/iet-com.2016.1166.
[19] N. Lee, X. Lin, J. G. Andrews, and R. W. Heath, "Power Control for
D2D Underlaid Cellular Networks: Modeling, Algorithms, and
Analysis," IEEE Journal on Selected Areas in Communications, vol. 33,
no. 1, pp. 1–13, Jan. 2015, https://doi.org/10.1109/JSAC.2014.2369612.
[20] K. Doppler, M. Rinne, C. Wijting, C. B. Ribeiro, and K. Hugl, "Device-
to-device communication as an underlay to LTE-advanced networks,"
IEEE Communications Magazine, vol. 47, no. 12, pp. 42–49, Sep. 2009,
https://doi.org/10.1109/MCOM.2009.5350367.
[21] P. Khuntia and R. Hazra, "An Efficient Channel and Power Allocation
Scheme for D2D Enabled Cellular Communication System: An IoT
Application," IEEE Sensors Journal, vol. 21, no. 22, pp. 25340–25351,
Aug. 2021, https://doi.org/10.1109/JSEN.2021.3060616.
[22] S. S. Sarma, P. Khuntia, and R. Hazra, "Power control scheme for
device-to-device communication using uplink channel in 5G mm-Wave
network," Transactions on Emerging Telecommunications Technologies,
vol. 33, no. 6, 2022, Art. no. e4267, https://doi.org/10.1002/ett.4267.
[23] J. Gu, S. J. Bae, B.-G. Choi, and M. Y. Chung, "Dynamic power control
mechanism for interference coordination of device-to-device
communication in cellular networks," in 2011 Third International
Conference on Ubiquitous and Future Networks (ICUFN), Dalian,
China, Jun. 2011, pp. 71–75, https://doi.org/10.1109/ICUFN
.2011.5949138.
[24] M. Alazab, S. Khan, S. S. R. Krishnan, Q.-V. Pham, M. P. K. Reddy,
and T. R. Gadekallu, "A Multidirectional LSTM Model for Predicting
the Stability of a Smart Grid," IEEE Access, vol. 8, pp. 85454–85463,
2020, https://doi.org/10.1109/ACCESS.2020.2991067.
Engineering, Technology & Applied Science Research Vol. 13, No. 4, 2023, 11318-11325 11325
www.etasr.com Alotaibi: Interference Mitigation Strategy for D2D Communication in 5G Networks
[25] L. Wang, H. Tang, H. Wu, and G. L. Stüber, "Resource Allocation for
D2D Communications Underlay in Rayleigh Fading Channels," IEEE
Transactions on Vehicular Technology, vol. 66, no. 2, pp. 1159–1170,
Oct. 2017, https://doi.org/10.1109/TVT.2016.2553124.
[26] M. Ahmed, H. Shi, X. Chen, Y. Li, M. Waqas, and D. Jin, "Socially
Aware Secrecy-Ensured Resource Allocation in D2D Underlay
Communication: An Overlapping Coalitional Game Scheme," IEEE
Transactions on Wireless Communications, vol. 17, no. 6, pp. 4118–
4133, Jun. 2018, https://doi.org/10.1109/TWC.2018.2820693.
[27] C. H. Yu, O. Tirkkonen, K. Doppler, and C. Ribeiro, "Power
Optimization of Device-to-Device Communication Underlaying Cellular
Communication," in 2009 IEEE International Conference on
Communications, Dresden, Germany, Jun. 2009, pp. 1–5,
https://doi.org/10.1109/ICC.2009.5199353.
[28] P. K. Reddy Maddikunta, G. Srivastava, T. Reddy Gadekallu, N. Deepa,
and P. Boopathy, "Predictive model for battery life in IoT networks,"
IET Intelligent Transport Systems, vol. 14, no. 11, pp. 1388–1395, 2020,
https://doi.org/10.1049/iet-its.2020.0009.
[29] P. Khuntia and R. Hazra, "QOS aware channel and power allocation
scheme for D2D enabled cellular networks," Telecommunication
Systems, vol. 72, no. 4, pp. 543–554, Dec. 2019, https://doi.org/
10.1007/s11235-019-00582-8.
[30] M. Li, "Soft Frequency Reuse-Based Resource Allocation for D2D
Communications Using Both Licensed and Unlicensed Bands," in 2019
Eleventh International Conference on Ubiquitous and Future Networks
(ICUFN), Zagreb, Croatia, Jul. 2019, pp. 384–386,
https://doi.org/10.1109/ICUFN.2019.8806044.
[31] A. Memmi, Z. Rezki, and M.-S. Alouini, "Power Control for D2D
Underlay Cellular Networks With Channel Uncertainty," IEEE
Transactions on Wireless Communications, vol. 16, no. 2, pp. 1330–
1343, Oct. 2017, https://doi.org/10.1109/TWC.2016.2645210.
[32] Q. Duong and O.-S. Shin, "Distance-based interference coordination for
device-to-device communications in cellular networks," in 2013 Fifth
International Conference on Ubiquitous and Future Networks (ICUFN),
Da Nang, Vietnam, Jul. 2013, pp. 776–779, https://doi.org/10.1109/
ICUFN.2013.6614925.
[33] Y. He, X. Luan, J. Wang, M. Feng, and J. Wu, "Power allocation for
D2D communications in heterogeneous networks," in 16th International
Conference on Advanced Communication Technology, Pyeongchang,
Korea (South), Oct. 2014, pp. 1041–1044, https://doi.org/10.1109/
ICACT.2014.6779117.
[34] S. Y. Shin and T. A. Nugraha, "Cooperative water filling (CoopWF)
algorithm for small cell networks," in 2013 International Conference on
ICT Convergence (ICTC), Jeju, Korea (South), Jul. 2013, pp. 959–961,
https://doi.org/10.1109/ICTC.2013.6675527.
[35] "5G NR UE Power Classes," Techplayon, Jan. 05, 2021.
http://www.techplayon.com/5g-nr-ue-power-classes/.