SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 4(4); March (2024), pp. 209–216
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

International Journal

Advances in Artificial Life Robotics
ICAROB series
Editor-in-Chief
Masanori Sugisaka
Co-Editor-in-Chief
Takao Ito

ISSN 2435-8061
Publisher Masanori Sugisaka(Global Register of Publishes)
regstrated in Jan, 25, 1996
Handadai Higashi 2-8-4, Oita, 870-1108, Japan

Research Article

Optimizing Antenna Configurations for Improved Drone
Communication in Challenging Environments
Mastaneh Mokayef1, MHD Amen Summakieh1, M.K.A Ahamed Khan1, Sew Sun Tiang1, Wei Hong Lim1, Abdul Qayyum2
Department of Electrical and Electronic Engineering, FETBE, UCSI University, KL, Malaysia
National Heart and Lung Institute, Imperial College London, UK

1
2

ARTICLE INFO
Article History
Received 23 November 2023
Accepted 29 August 2024
Keywords
SNR
UAV
E/N
MATLAB
IoT
SISO
SIMO
Disaster

ABSTRACTIn recent years, the use of drones, also known as Unmanned Aerial Vehicles (UAVs), hasexperienced rapid growth across multiple industries, including agriculture, logistics, disastermanagement, and surveillance. The effectiveness of these UAVs is heavily dependent on reliablecommunication systems, particularly in challenging environments where signal strength andreliability are critical. This article explores the optimization of antenna configurations using
MATLAB simulation tools to enhance the Signal-to-Noise Ratio (SNR), with a focus on the
broader context of Internet of Things (IoT) integration. Considering the use of Energy per Bit to
Noise Power Spectral Density Ratio (E/N) as a measurement technique commonly used inwireless communication systems to assess the quality of the received signal relative to thebackground noise, this paper evaluates and compares the quality of the received signals in SISO
and SIMO systems.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

signal to the undesired background noise. Optimal SNR
level is a key factor for ensuring seamless and dependable
communication between drones and ground control
stations, facilitating precise data transmission
A strong SNR is essential for enabling effective drone
operations, because it guarantees the caliber andreliability of the communication channels. This isparticularly important in scenarios where drones are
required to transmit critical information or receive
commands from ground control in a reliable and timely
manner. The optimization of the SNR is, therefore, a key
focus area for researchers and developers working on
improving the overall performance and capabilities ofdrone-based communication networks. By enhancing the
SNR, drone operators can ensure more robust andefficient data transfer, ultimately unlocking the fullpotential of these versatile aerial platforms across various
industries and applications. [1], [2]. Nevertheless,attaining a robust signal-to-noise ratio within drone
networks poses multiple challenges [3]. Utilizing
numerous drones nearby may lead to signal interference
and degradation, impacting communication quality.

1. Introduction
Drones are poised to act a pivotal role in Emerging
wireless connectivity, furnishing a positive approach tofulfill the growing requests of users. Their inherent
Versatility, Resilience, enhanced direct visibility featuresor enhanced LOS, and potential to access the challenging
regions, led to well-suited technologies to serve as aerialbase stations. In this regard, several research has been
conducted to measure the efficiency of appraisal and theallocation of resources in wireless networks that are
powered by UAV. This survey article aims tocomprehensively review the various algorithms and use
cases driving drones’ deployment. Lately, drones or
UAVs usage has seen a remarkable increase throughout
an extensive range of industries and uses, from capturing
aerial photography to delivering packages. The Signal-toNoise Ratio (SNR) is a crucial factor that affects drone
networks' efficiency and output. In a communication
system, the SNR shows the proportion of the desired

Corresponding author’s E-mail: mastaneh@ucsiuniversity.edu.my, URL: https://www.ucsiuniversity.edu.my/209

Additionally, the drones' generated noise, known as egonoise, can further influence the SNR. Effective anddependable
drone-to-groundcontrolstationcommunication necessitates thoughtful deliberation andinnovative strategies for enhancing the signal-to-noise
ratio within drone networks. [4]. Recent research has
focused on optimizing antenna configurations to improve
the quality of drone communication in such challenging
environments.

Authors in [12] proposed a novel multi-UAV
communication paradigm with a focus on security.
Multiple drones can communicate efficiently andsecurely using a wireless mesh network andcryptographic techniques in this model. Establishingsecure data communication between drones and servers
is crucial, as emphasized by the study. Within the field ofdrone communication, these academic works together
provide significant contributions regarding the evaluationof coverage probability, propagation mechanisms,
coordination among multiple UAVs, and security
protocols [13], [14]. The data extraction process was
conducted based on a Scopus document search, yielding
a total of 2704 articles. To ensure the reliability andrelevance of the data, conference proceedings were
excluded from the search. The next step involved data
cleaning, where the focus was limited to articles written
in English. Further refinement was carried out by filtering
the articles based on specific journals. The resulting
article counts for each journal was as follows: IEEE
Access (963 articles), Remote Sensing (321 articles),Sensors Switzerland (311 articles), IEEE Internet ofThings Journal (294 articles), Sustainability Switzerland(231 articles), IEEE Transactions on VehicularTechnology (215 articles), and Wireless PersonalCommunications (125 articles). By narrowing down thedataset based on these journals, the research aims togather valuable insights and trends in the respective
fields.in the last stage of data cleaning process, only
related articles that focused on SNR optimization, hasbeen considered. Fig. 1 illustrates the importance of this
topic based on the number of related documents
published since 1996 till today.

The research in [5] proposed an antenna offering 360°
coverage and adaptive beam control for reliable drone-todrone communication. While [6] introduced a structure
for lunching a drone-centric antenna array, minimizing
service time by optimizing drone spacing and positions.
[7] experimentally evaluated antenna polarization andelevation effects, revealing that drone bodies can act as
local
scatterers,
reducing
cross-polarization
discrimination and potentially doubling spectral
efficiency. [8] developed a distributed solution for
optimizing communication performance in UAV fleetsby independently adjusting individual drone orientations
based on local reception power information. Thesestudies collectively demonstrate the importance ofconsidering antenna configurations, polarization,
elevation, and orientation in improving drone
communication systems for various applications andenvironments.
At a specific location, the SNR can gauge the unwanted
signal encountered by each drone. Various elements
including Signal strength, antenna characteristics, drone
location and height, ambient noise power spectral density,and path loss collectively shape the SNR. These factors
hold significant importance in determining interference
levels and are crucial for maintaining communication
quality within drone networks [9]. Drones experiencesignificant changes in signal-to-noise ratio based on their
flying altitude. At higher altitudes, the signal quality
diminishes due to the increased distance along thecommunication path. As altitude increases, signal loss isfurther exacerbated by atmospheric attenuation. The
strength of the signal is influenced by the emission
intensity and antenna characteristics of drones.
Overcoming noise and interference requires optimal
power levels, while signal coverage and directionality are
influenced by antenna properties. The overall SNR isaffected by background noise introduced by various
sources’ noise power spectral density. The maintenanceof dependable communication in drone networks hinges
on the careful consideration of these factors [4], [5], [6],[7], [8], [9]. In [9], [10], The domain of UAV supported
communication in a smart sensor grid is explored by thewriters. Their efforts are concentrated on creating a
blueprint to assess the probability that terrestrial
monitoring unit will be covered. Examining diffraction,
frequency, and atmospheric attenuation, Raja’s 2021
study estimates the distribution of links spreads among
UAVs and terrestrial units in an enclosed area [11].

Fig. 1: Documents published per year
Recent research on optimizing drone communication
focuses on enhancing air-to-ground (A2G) links andimproving energy efficiency. [15] proposed an algorithm
for selecting antenna patterns using beamforming to
reduce sidelobe levels and optimize antenna array
configurations. [16] present a dual optimization approachthat simultaneously addresses UAV mobility andtransmission power, resulting in enhanced energy
efficiency and Air-to-Ground transmission reliability.
[17] suggest using hybrid beamforming techniques tooptimize UAV power consumption in wirelesscommunication for disaster management scenarios. [18]addressed secure UAV communications by proposing a210

multi-objective optimization problem that maximizes
secrecy rates and minimizes sidelobe levels and energy
consumption. They introduced an enhanced multi-criteria
Salp Swarm Algorithm to solve this complex problem.
These studies collectively demonstrate advancements inantenna configurations, power optimization, and securecommunication strategies for improved drone
performance in challenging environments.

comprehend the essential communication needs of UAVs,
facilitating a better understanding of the underlying
requirements and considerations in this domain. This
table serves as a valuable resource for those involved in
the deployment of UAV-based communication systems,
as it provides a concise and easily digestible summary of
the critical communication requirements that must be
addressed to ensure the successful implementation andoperation of these aerial platforms.

To aid in understanding the communication
requirements of UAVs, Table 1 presents the key
communication needs for Unmanned Aerial Vehicle(UAV) systems, categorized by data type and direction(downlink or uplink). The high data rate requirement for
uplink might pose challenges in terms of bandwidthavailability and power consumption. Table 1 succinctly
provides an overview of these specifications. Thistabulated reference efficiently outlines the essential
facets of UAV communication, aiding readers incomprehending the vital information with ease. Table 1
presents a succinct and organized representation of thevarious communication requirements for UAVs, such asrange, data rate, latency, and other relevant parameters.
By consolidating this information into a tabular format,
the text allows readers to quickly reference andTable 1 Key Communication Needs for UAV
Systems
Category

Throughput (Kbps)

System Alignment

Not applicable

Downlink /Uplink Control
Uplink App speed

60 < 𝐷𝐷𝐷𝐷, 𝑈𝑈𝑈𝑈 < 100

Maximum of 50

An Analysis of the unique characteristics of various
drone models is illustrated in [12], [19], [20], [21], [22]and the findings are structured in a table in Table 2.

Table 2 Distinctive Features of Drone Types [20], [21]System Type

WeightPayloadFlight Mechanism

Range

Flight Time (min) Speed (Km/h)

Micro

16g

Not applicable

Poly-spin

50-80 m6-8

Not applicable

Very Small

750g

Not applicablestatic winged

2km

45

80

Small

3.3kg

4.9kg

Poly-spin

Not applicable

18

57.6

Medium90kg

50kg

Poly-spin

Not applicable

180

100

Large

2223kg1700kgPoly-spin

1852km1800

482

frequencies, shedding light on how noise affects signal
quality and SNR performance. Furthermore, they
compare the Energy per Bit to Noise Power Spectral
Density Ratio (E/N) between Single-Input Single-Output(SISO) and Single-Input Multiple-Output (SIMO)
systems, which provides deeper insights into system
reliability and performance metrics. Finally, the analysis
explores how the Line-of-Sight (LOS) channels forMultiple-Input Multiple-Output (MIMO) systems stack
up against SISO and SIMO setups, illuminating the SNRlevels and operational efficiencies in LOS scenarios.
Through these comprehensive evaluations, the team aims
to gain a nuanced understanding of system performancebased on SNR strength variations under diverse
conditions and configurations. have surged in popularity,
thanks to their autonomous capabilities, adaptability, and

2. Methodology
2.1. SNR Analysis for Performance Assessment
The process of evaluating performance through Signalto-Noise Ratio (SNR) strength involves exploring a seriesof key steps. It starts by looking at how SNR changes as
the distance between the drone and the signal source
increases, trying to determine the relationship between
SNR and the drone's displacement. The investigation then
expands to examining SNR variations across different
operating frequencies, assessing how SNR levels
fluctuate with varying frequencies and visualizing thesepatterns through graphs. Additionally, the team dives into
the impact of noise on SNR levels at different operating211

diverse array of uses. This section will conduct athorough evaluation of drone performance. This talk will
primarily center on examining a crucial Signal-to-Noise
Ratio (SNR) metric. This metric will be used to assessand examine drone operations in-depth, providing
insightful information about their overall efficacy. The
comparison of signal power and noise power is reflectedin the SNR, which is commonly expressed in dB. In thecontext of wireless communication, where data istransmitted between two points via a channel called a datalink, this is an important consideration. This wirelesstransfer is known as a radio connection in the unmanned
aviation community. Information exchanged between theaircraft's autopilot and the Ground Control Station (GCS)
is facilitated by radio communication. This
communication consists of two distinct links: thedownlink, which is in charge of sending data from theaircraft to the GCS, and the uplink, which is in charge ofsending data from the GCS to the aircraft. Equation (1) isutilized to compute the signal strength over the noise at
the output of the system, which is a key factor for
efficiency measurement of the communication links indrone-based operations. By analyzing and optimizing thisSNR metric, researchers and practitioners can glean
important understandings of the effectiveness andviability of drone-based communication systems,
ultimately supporting the development and deploymentof robust and efficient unmanned aerial platforms.
𝑆𝑆𝑆𝑆𝑆𝑆 = 𝑃𝑃 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 − 𝑃𝑃𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛

Fig. 2 (a) SNR variation vs Drone’s traveled distance[12], (b) SNR vs frequency of operation

Fig. 3 (a)Effect of frequency of operation on SNR level[12], (b) SNR vs Frequency of Operation in presence of
Noise
Fig.3 illustrates that the frequency of 22MHz is apromising operating frequency that can be resistive to thebackground noise.
2.2. Signal strength Improvement Techniques(1)

The examination outlined in Fig. 3
providesperspectives on the importance of the SNR in influencing
output quality. The determined threshold value for SNR
is −10 𝑑𝑑𝑑𝑑; Nevertheless, it is advisable to maintain a
Signal-to-Noise Ratio exceeding −20 𝑑𝑑𝑑𝑑 to achieve
optimal output quality. The result depicts a gradual
deterioration in signal power as the range extends, withthe peak signal strength noted near the transmitter. Thisimplies that the closeness to the transmitter is vital for
upholding a robust and dependable signal. In light of the
fixed parameters of a 2.4 GHz frequency cap, a 1 W
power restriction, and a predetermined distance, it isevident that a stronger signal at 1 meter distance, 10 MHz
frequency, and 1 W transmission power is required. This
information highlights the importance of carefully
managing the SNR in communication systems,
particularly in scenarios where signal quality andreliability are critical factors, such as in drone-based
operations. By understanding the relationship between
SNR, range, and other relevant parameters, researchersand engineers can optimize the design and deployment ofcommunication systems to ensure robust and consistent

Power levels are commonly denoted in decibels per
dBm. The minimum SNR required for a task determinesa drone's operational radius with respect to a base station.
Defining the maximum reachable distance while
upholding the desired SNR is crucial. As the drone travels
different distances during flight, the SNR varies andadjusts to reach the predetermined SNR. It should behighlighted that all the values in (1) have to be presented
in dB. To illustrate this relationship, Fig. 2 illustrates a
MATLAB-generated
visualization
model
that
demonstrates the impact of drone distance on the𝑆𝑆𝑆𝑆𝑆𝑆𝑑𝑑𝑑𝑑 .

212

performance, even in challenging environments [11]. The
higher signal quality observed near the transmitter is as aresult of existence of several scatterers. These external
factors have a tendency to diminish the signal power,
particularly at longer distances. By increasing thecommunication path, the degradation in signal power canbe observed. This occurs when the signal travels throughthe environment, while the noise from various sources
remains relatively constant or even increases. The
proximity to the transmitter is a critical factor inmaintaining a strong and reliable signal. Closer proximity
to the transmitter helps mitigate the impact ofinterference from external sources, resulting in a higher
SNR and, consequently, better signal quality. This
information highlights the importance of considering the
spatial distribution of communication devices and thepotential sources of interference when designing anddeploying communication systems, particularly inenvironments with a high density of electronic devices orphysical obstacles. By optimizing the placement andconfiguration of transmitters and receivers, the SNR canbe maximized, leading to improved signal quality andoverall communication performance.

Where 𝐺𝐺𝑟𝑟𝑟𝑟 is the gain of the i-th receiving antenna.
The Bit Error Rate (BER) vs E/N ratio has been
displayed in a comparison format as a result of thesecomputations. This method is frequently applied as a
metric to measure the received signal power and the noise
power impacting the transmission in a communication
system.

Fig. 4 Energy-to-Noise Ratios in SISO versus
SIMO Systems
In the scenario under consideration, the transmitter andreceiver are in direct Line-of-Sight (LOS)
communication, allowing for a straightforward
calculation of the system parameters. For the simulations,a four-element Uniform Linear Array (ULA) with halfwavelength spacing is utilized. The provided Fig. 4
illustrates the energy to noise ratio as a performancemeasurement technique for both SISO and SIMO
systems. There is a notable enhancement of 6 dB in the
curve. The primary cause of this gain is the coherent
interaction between the components of the receive arrayand the received signals. Because the receiver is aware of
the incoming signal path, it is possible to strategicallyalign the receive array with respect to the transmitter,
which improves the SNR. The coherence displayed by thereceived signals surrounding the receive arraycomponents can account for the observed gain. The
spatial diversity that the receive array's multiple antennas
provide is what causes this coherence. Several records forthe transmitted signal, characterized by slight alterations
in both amplitude and phase of each antenna.
Concatenating these signals causes constructive
interference, which raises the total power of the received
signal. This occurrence underscores the advantages ofutilizing a multi-antenna receiver array, enabling thesystem to effectively capture and utilize coherent signals,
thereby enhancing signal robustness and subsequently
improving Bit Error Rate (BER) performance. Bit Error

2.3. SNR Boost through Array Gain in LOS
Transmission
One method employed to enhance the dependability
and excellence of wireless communication in situationsof direct visibility propagation involves the utilization ofarray gain. To get the most from the array gain, largeantenna arrays are commonly integrated into modern
wireless communication systems [22], [23], [24]. By
optimizing antenna configurations for improved drone
communication in challenging environments, thereliability and effectiveness of medical drones can beenhanced [25]. A method utilized to enhance thedependability and quality of wireless communication in
scenarios of line-of-sight propagation involves theimplementation of array gain. Large antenna arrays arecommonly
integrated
into
modernwireless
communication systems in order to take advantage ofarray gain. [15], [16]. Equations (2) and (3) are utilized
to compute and compare the SNR for the SISO and SIMO
scenarios, respectively.
𝑆𝑆𝑆𝑆𝑆𝑆𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 =𝑃𝑃𝑡𝑡 𝐺𝐺𝑡𝑡 𝐺𝐺𝑟𝑟 𝜆𝜆2(2)𝑃𝑃𝑡𝑡 𝐺𝐺𝑡𝑡 ∑ 𝐺𝐺𝑟𝑟𝑟𝑟 𝜆𝜆2(3)4𝜋𝜋𝑑𝑑 2 𝑁𝑁0

Where input power, gain at transmitter and receiver side,and input signal frequency are represented by
𝑃𝑃𝑡𝑡 , 𝐺𝐺𝑡𝑡 , 𝐺𝐺𝑟𝑟 , and 𝜆𝜆 respectively. The 𝑑𝑑 and 𝑁𝑁0 present
spatial gap between the transmitter and receiver, and theoutput noise power, respectively.
𝑆𝑆𝑆𝑆𝑆𝑆𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 =

4𝜋𝜋𝑑𝑑 2 𝑁𝑁0213

significantly increased, facilitating communication in avariety of situations, including the distribution of vital
public safety information. In addition, unmanned aerial
vehicles (UAVs) exhibit potential for facilitating safe andeconomical mm-waves communication. Drones that are
outfitted with cellular links necessitate real-timecommunication and ensure the communication integrity
with terrestrial stations. The SIMO receive array's ability
to receive coherent signals offers a way to point the arrayin the direction of the transmitter, which boosts the SNR.
By using the receive array, a gain of 6 dB is obtained, as
shown by the Bit Error Rate (BER) curve analysis,suggesting that the receiver is aware of the incoming
signal path. These results demonstrate the benefits ofusing UAVs in wireless networks by highlighting how
they can improve coverage, power, and communication
capabilities. Improved SNR and overall system
performance are a result of both the coherent nature ofreceived signals and the capability to direct the receiving
emissions into the transmitter. Overall, the results
demonstrate the challenges associated with wirelesscommunication systems, particularly regarding signal
attenuation and noise interference. These factors cansignificantly impact the performance and reliability ofcommunication links, especially over longer distances
and at higher frequencies. The obtained results show thegain of 12dB or the increment of 6dB compared to the
SIMO antenna.

Rate (BER) performance for three distinct antennaconfigurations—Single-Input Single-Output (SISO),
Multiple-Input Multiple-Output (MIMO), and SIMO—iscompared in Fig. 5. Plotting the results against the Eb/No(energy per bit to noise power spectral density ratio) in
decibels (dB), the performance is assessed over a Lineof-Sight (LOS) channel. A 4 × 4array is used in a MIMO
setup; For peak performance, the elements need to beoriented in each other's direction. The BER curve can becomputed as shown in Fig. 5.

Fig. 5 MIMO LOS Channel vs SISO and SIMO LOS
channelAs expected, the BER decreases as Eb/No increases for
all three systems. This indicates that higher signal-tonoise ratios lead to better error performance. The MIMO
system consistently exhibits the lowest BER across theentire Eb/No range. This illustrates the substantial benefitof employing numerous antennas for error rate reduction
at both the transmitter and the receiver. The SIMO
system shows improved performance compared to SISO,
particularly at lower Eb/No values. This highlights thebenefit of using multiple antennas at the receiver tocombat channel impairments. The performance gains of
MIMO over SIMO and SISO become more pronounced
at higher Eb/No values. This suggests that MIMO
systems can effectively exploit higher signal-to-noise
conditions to achieve significantly lower error rates.

Subsequent investigations may concentrate on
streamlining
UAV
deployments,
improving
beamforming methods, and investigating new uses for
UAVs in wireless networks. Improvements in safe,
dependable, and effective wireless communication
systems can be achieved by utilizing UAVs in thesesituations.
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3. Conclusion

3.

The importance of Drones in wireless networks ishighlighted in this research paper, which also highlights
the potential uses of UAVs, aerial base stations, andwireless network user services. By deploying UAV basestations, wireless network coverage and power can be

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Authors Introduction
Dr. Mastaneh Mokayef
She holds a PhD from WCC at UTM,
Malaysia. She is serving as an
Assistant. Prof. at UCSI University.
She is a member of IET She received
the Global Distinguished Young
Scientists Award from IEEE IAS
2023.

Mr. MHD Amen Summakieh
He earned a Communication andElectronics degree from UCSI
University and his master’s degree infrom Multimedia University. He is anIEEE HKN member.

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Dr. M. KA. Ahamed Khan
He completed his B.Eng studies inElectronics and Communication
Engineering and pursued studies inElectronics and Control Engineeringin India. He later received a Diploma
in Drives and Controls from Woo
Sun in Korea in 2014. His academic
journey culminated in a Ph.D.
program in the United States in
Robotics, Power Electronics, and Controls. He is
acknowledged as a Professional Engineer (PEng) in the
USA and holds the designation of a Chartered Engineer(CEng) in the UK. Moreover, he is a Senior member of theIEEE in the USA and a member of MIET in the UK.
Dr. Sew Sun Tiang

Dr. Wei Hong Lim

methodologies.

Currently serving as an AssistantProfessor in the Faculty ofEngineering at UCSI University in
Malaysia, she completed her doctoral
studies in Electrical and Electronic
Engineering at USM in 2014. Her
research focuses on optimization
techniques and antenna design

Currently holding the position ofAssistant Professor in the Faculty ofEngineering at UCSI University in
Malaysia, he earned his doctoral
degreeinComputational
Intelligence from Universiti Sains
Malaysia in 2014. His researchpursuits center around optimization
strategies and artificial intelligence

Dr. Abdul Qayyum

He obtained his Ph.D. in Electricaland Electronics Engineering from
UTP, Malaysia. His doctoral work
focused on developing deeplearning algorithms for estimating
the depth of vegetation and trees
near power lines for TNB and SESB
under KeTTHA Malaysia. Prior tothis, he completed his bachelor's
degree in computer engineering and a Master's inElectronic Engineering in Pakistan. Currently, he serves asa Postdoctoral Researcher at Burgundy University inFrance, where his research involves utilizing deep learningtechniques for analyzing cardiac MRI images.

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