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PUBLISHED DATE: - 28-07-2024
DOI: -
https://doi.org/10.37547/tajiir/Volume06Issue07-05
PAGE NO.: - 35-43
IMPROVING THE PERFORMANCE OF WIRELESS
NETWORKS USING NEW TECHNOLOGIES
Vinas Khalid Kadhim
University of Karbala, College of Administration and Economics, Iraq
INTRODUCTION
1. Introduction
Many modern technologies depend on signals
transmitted through wireless networks of different
types. Therefore, making wireless networks faster
and more reliable could have a huge impact on
technology in several domains. However, there are
many problems in the field of wireless
communication that need to be addressed to
achieve these improvements. In this essay, I will
look at the architecture, problems, and potential
solutions to wireless networking. This essay will
cover several proposed innovations which may
make wireless networks more stable and
dependable, thereby enhancing the performance of
a number of modern technologies.
The proposed improvements include using
sophisticated antennas which emit conventional
radio signals more effectively with less crossover,
changing the frequencies used for transmission
and data analysis to dramatically increase speed
and decrease issues - throughput and range, and
reducing
interference
from
surrounding
competing wireless networks to prevent
performance drops. Building networks in
unconventional new topologies is also proposed. I
will engage directly with the source to understand
their proposals as we discuss each of them. I will
examine methods of broadening the range of
signals emitted by radio transmitters and the
complications they introduce. I will consider the
possible applications for this new technology, as
proposed, evaluate the merits of the arguments,
and conceive of new applications for this
technology except those already proposed, means
of transmission, particularly since these
improvements become less effective at longer
ranges.
1.1. Overview of Wireless Networks
RESEARCH ARTICLE
Open Access
Abstract
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Wireless networks have become an essential part
of our lives. Their characteristics, such as mobility,
free space utilization, and easy installations, make
them superior to wired counterparts. In addition to
these advantages, wireless technologies offer some
additional facilities. Some of the abilities of
wireless networks are:
- Cable-less operations, that offer freedom of
installations - On-demand network access facilities
anywhere, anytime - Network deployment in
unique/remote applications such as location-
tracking and monitoring applications
In technical terms, a wireless network leverages
radio waves and wireless communication
standards to provide network connectivity over
common standards such as WiFi, cellular, and
Bluetooth. Broadly, these networks are classified
based on the standards, architecture, and the
services they offer. For example, WiFi standards
(predominantly, IEEE 802.11x series) afford a
wireless connectivity range of about 30 meters and
are commonly used in home internet connectivity.
Similarly, wireless connections such as LoRA
standard are used as Wide Area Networks (WAN)
for long-range applications. The choices of
standards/utility vary with applications such as
security, speed, and network size.
From a historical point of view, wireless networks
are a concept which is over a century old. The
architectural evolution from point-to-point
communication systems to networks of different
topologies has now advanced to a large-scale
network where users at different locations, with
different devices and standards, can communicate
with each other in a seamless manner. A wireless
network is composed of various hardware and
software components, which we discuss in detail in
the next chapter. From the hardware point of view,
we require different components at the transmitter
and receiver. Also, at both the transmitter and
receiver, we need proper signal conversion
components
that
can
make
wireless
communication possible. The software counterpart
talks about the communication protocols and the
standards, without which communication is not
possible. The primary function of these protocols is
to ensure data transfer between two points
without loss of data or distortion of the original
data.
2. Current Challenges in Wireless Networks
Though wireless networks have greatly advanced
in the past decade and are increasingly popular
with computer users and mobile phone users for
their flexible mobility and low infrastructure costs,
they have their limitations. Relative to their wired
counterparts, the most significant difficulties
pertain to unreliable communication links,
interference and noise-induced attenuation,
difficulty providing quality of service guarantees,
lack of guaranteed higher bandwidth levels
necessary for various multimedia applications, and
scalability issues. Together, these challenges can
negatively affect the performance of wireless
networks and thereby limit the number of
prospective users and the user experience.
A low signal-to-noise ratio in wireless network
communications can result in high rates of packet
error, degrading the performance of the hosts. In
lossy networks, where throughput decreases for
each packet that is generated, congestion control
has a significant impact on the performance of the
network. The number of successful transmissions
on a link before a packet needs to be retried is
significantly less when exposed to bit errors, and
not all the correcting bits are effective in
identifying and correcting errors. Wireless
networking standards, such as wireless local area
networks (WLAN) IEEE 802.11, detect error
checking codes when a corrupted frame has the
potential to be sent to the receiver but cannot do
anything about the error.
2.1. Bandwidth Limitations
One of the main challenges in wireless networks is
the limited spectrum, which means the bandwidth
limitations that are associated with it. Decreased
bandwidth has a strong impact on the time of data
transmission, as the available throughput of the
channel directly influences the capacity of the
network. Reducing the amount of information that
can be sent during any given period of time directly
reflects the effective capacity. The network deals
with this reduced throughput and the time needed
to transmit the data is called latency, which is the
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time from the beginning of the data transmission
until the last bit is sent. Another issue that is caused
by the limited bandwidth is the raised probability
of network congestion. In the case of independence
and distribution of random data, the increased
number of input traffic positively influences the
output traffic. However, network congestion is
caused by multiple nodes that are trying to send
large packets at a time, which will significantly
reduce the performance of the network. Therefore,
a fundamental property of high-performance
wireless networks is the efficient allocation of
bandwidth.
There are multiple factors that can cause limited
bandwidth, ranging from high network traffic
volumes to simply bad network design. Regardless
of the cause, bandwidth limitations are extremely
dangerous for the whole network performance.
Scarce bandwidth will create congestion that
decreases the quality of service (QoS) for
transmission of multimedia data. Typically in
wireless networks, bandwidth is the primary
bottleneck. Therefore, reducing the impact of
bandwidth limitations on the performance of these
networks is one of the primary engineering
challenges for wireless networking.
3. Emerging Technologies in Wireless Networks
The
latest
advances
and
technological
improvements
in
communications
and
computation standards have made digital wireless
communications a reality. This applies to a variety
of wireless networks such as personal area
networks (PANs), local area networks (LANs),
metropolitan area networks (MANs), and wide
area networks (WANs). The market has been
reshaped by the ongoing rapid advancement of
wireless communications networks in terms of
capacity, user numbers, application setup,
deployment and management, and quality
improvement.
In the near future, some emerging trends,
revolutionary services, and applications will
significantly impact wireless network usage and
raise new research issues in the field of wireless
communications. A wireless network, or a wireless
communication network, provides the crucial
connectivity and data exchange between the
parties. The burgeoning wireless communications
field is producing a wide range of user-centric
networks. As a result, signals are transmitted and
delivered to users via wireless networks instead of
wired connections.
In summary, wireless networks, from LANs to the
extreme ends of backhaul systems and deep cloud
data centers, are considerably improved by
worldwide advancements
in tomorrow’s abilities
and attributes. This requires networks, training,
and the best possible technologies. For the sake of
simplicity, in this section, the outcomes of
operational technological expansions in numerous
applications are discussed briefly and thoroughly.
What’s more, a few sample networks illustrate key
component discontinuities and in that light, the
network is analyzed throughout.
The functionality and enhanced systems of the
future are greatly facilitated by streamlined and
ultramodern figure systems. 6G supports 6P. It is
necessary to rapidly grow networks and systems in
order to realize them. In order to promote AI
distances,
more
complex
networks
and
technologies, along with greater skill activation, are
needed. Moreover, 6G command, and supply data
for center inputs, are part of the work. Centering on
6G innovations and material developments can be
assessed through the functions and methodologies
that produce true enlightenments. Everything,
including scheduling of 6G conventions enhanced
by resource management and the parental roots of
issues
linked
to
mobile
networks/telecommunications and networking
flexibility, is the result of this approach.
Wireless networking, from the center to an
extensive zone, is counted as being essential for
continuing AI. 6G wireless technology is essential
for equipping AI for the future. This is because it
provides a wealth of data and AI machinery and a
wide and diverse framework. Both AI resources
interconnected by NCA and by distribution
(cyber/physical objects within the 6G ecosystems)
also can be used.
3.1. 5G and Beyond
5G technology is expected to have enormous
potential for wireless as it is rapid and information
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change network to access dispersed computing
resources at full speed. It will lead to extraordinary
network
development
with
far-reaching
implications. 5G networks have seven famous
attributes. It is expected to enable a minimum of a
ten-time increase on average in the total cell
capacity.
The
principal
technological
advancements will include ultra-dense networks
(UDN), heterogeneous cloud radio access networks
(H-CRAN), and full-duplex sharing. In order to meet
the requirements of contemporary densely
populated urban living, the 5G-enabled cellular
Internet of Things (IoT) network, which will have
20
–
25 billion connected devices and a maximum of
a 60% penetration rate, would have an ultra-dense
network with a 500
–5000 hotspots max km−2,
with a capacity of up to 800
–4000 Gb s−1 km−2.
The deployment of 5G New Radio (5G-NR) with
advanced mobile (radio) communication systems
will be a critical element of 5G systems. In a dense
network centric design, a novel approach to UDN-
based 5G-HetNet was proposed. The H-CRAN has
three-layer or n-tier cells, which include macro cell
base stations (BS), small-cell BS, and user
equipment (UE) directly connected to servers or a
cloud via fiber links. Also, adaptation has to be
made to deal with mobiles in 5G, since the speed of
nodes in traffic is much higher in moving vehicles
such as cars, trucks, and buses to boost
connectivity with local information. Some of the
issues in 5G are the improved connectivity and
capacity of the network, and the ultra-reliable
short-latency communication and integrated
mobile traffic with a vehicle-to-vehicle (V2V),
vehicle-to-infrastructure (V2I), and vehicle-to-
everything (V2X) will be addressed.
4. Performance Metrics in Wireless Networks
There are many performance metrics that use
specific metrics to evaluate the wireless network's
efficiency. Signal quality is measured by the
strength of the carrier signal in decibels (dB) and
the signal-to-noise ratio. Detector distance
formulations, the generalized log likelihood ratio
(MEL), are given by max log (0k = 1, 2...n|0|Rl = 12
and Log (0k = 1,2... n = 1 H the density of distance
between the probability function (PDF) shown in
equation detection. Test statistic, the detector
average probability of error Pc, symbol error rate
of performance measures. Received Power (R) of
signal-to-noise ratio or received power dBm. The
main performance metrics to measure the network
are the following: the rate of data transfer, the user
expectations of rates, network available volume
(bandwidth), the average time to wait until inform,
the average time of latencies, etc.
The average bit error probability can integrate all
wireless systems performance more accurately,
but more complexity or a large number of
performance measurements were not usually good
plain value of the system. Section 3 addresses the
performance metrics used and the most used data.
The bit error probability in Eq1 will be influenced
by: The noise averaged over the period of the
receiver and the moment, if usually does not
depend on the Soviet Union and the vol het carrier,
the average moment ei over the time considered. If
a fixed power p3o and the local minimum average
electrical noise spectrum density No is defined as
Nw = NoW, then the signal-to-noise ratio at the
receiver is given by given as given rewards.
Performance measures are very important, as they
are always used to integrate all the wireless system
performances, such as coverage, throughput,
mobility, QoS, QoE, to provide better user
experience expectancy accuracies, and reduce
allocation redundancy. In addition, performance
measurements are also used to evaluate the
wireless effect of the development of new
technology network system's to improve the As a
success,
data
shown
in
performance
measurements the improvement of the goals
achieved and to what extent.
4.1. Throughput and Latency
One of the most important performance metrics for
wireless networks is the speed at which they can
deliver data to their users. This metric is referred
to as throughput, meaning the rate of data
transmission between a sender and receiver. In the
case of last-mile communication systems, like Wi-
Fi or LTE, high throughput is one of the main selling
points of the entire wireless data provisioning.
Throughput is a multidimensional topic that
depends not only on over-the-air communication
between the sender and receiver but also on the
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higher layer protocols and transport technologies.
In an ideal scenario, a high-throughput
communication system would be able to fully
saturate its link in a single hop. Nevertheless, in
practice, few challenges impair the delivery of a
high throughput. Indeed, the transmission peak
rate is cited to be decreasing with the increasing
coverage radius. Various impairments inherent to
the radio channel operation act adversely to reduce
the data rate performance majorly including
fading, shadowing, multiuser interference, etc.
Another main performance metric usually
considered for wireless networks is the network
latency (L). Even though there is no universally
accepted definition of latency, in simple words, it is
often used to characterize the responsiveness of a
network in terms of time taken by a packet to truly
propagate from the source to destination (or from
the sender to receiver). In most cases, the total
latency comprises three main components:
propagation time, queuing time, and system
processing time. Latency directly depicts the round
trip time (RTT) experienced by an application.
Usually, low-latency is desired by applications so as
to enhance the overall system performance and
user experience. Lower latency values shall reduce
the waiting duration until the first bit is completely
available at the receiver side.
5. Case Studies of Improved Wireless Networks
There are various examples of where major
wireless network improvements occurred using
new technologies. Improvements occur when
throughput, latency, range, energy consumption,
and charges are significantly improved compared
to current wireless networks. The Kookaburra
project implemented a low-latency TDMA network
robust against packet loss and for LEACH from the
ground up for urban parks based on a novel
collision-resilient broadcast schedule. RelayDG and
BackTrack have improved ClusterTree. RIOT
kernel energy consumption increased 3x over the
last 7 years with new energy consumption
reduction techniques added. The initial October
2019 release of the ATMEGA and Nordic targets
added SADP, did a mesh test on the RZRAVEN
boards, and then merged in the first of SPDX. The
2021.10 release will introduce GNRC Flow to
improve CoAP scalability.
A SURFnet test bed wireless Dutch technology
called LPWAN (Low-Power Wide Area Network)
has been proven to be low cost, low power, and
much easier to scale from 100 to 5000 wireless
devices compared to Wi-Fi mesh networks. The
province of Groningen installed a LoRaWAN to
measure soil moisture, air humidity, temperature,
and sunshine of agricultural land measuring paths
through remote and less populated areas.
Thousands of farmers in the east use a Dutch-
designed Aolu wireless system to herd cattle. These
tracking collars have a battery life of 5 years and
report cattle locations up to 50 times per day on a
satellite network that AT&T invented in 2017 using
Qualcomm's IoT modems. In the US, as
Microwaves101 note in their articles below, Ingenu
networks are building a similar IoT network which
is now active in 28 US cities. Helium has built and
is presently selling an IoT wireless hotspot
designed
to
improve
range,
reliability,
infrastructure, and the payment model for IoT
networks. A Hare data center heat energy recovery
system in South Australia was monitored by an
Adelaide company called Net2Edge to monitor the
wireless IT security system. Net2Edge has no
further details on their web page. In China, the 32.5
square kilometer Nanjing Smart City, which serves
5 million people, uses a smart radio frequency
identification (RFID) system designed by Nordic
Semiconductor. "Distris" can coincide with the
move towards Industry 4.0 with Smart
Manufacturing systems. For example, Bosch
started early in 2018, as shown in the video, to use
Distris to increase storage equipment near its
autonomous mobile robots to increase throughput
of power tool storage areas alongside its factory
production lines. Bosch worked with NavMat, a
German company based in Pforzheim, Germany,
which has been integrating RFID-based AVIATOR-
987 CMRITag-based RFID and low-cost, low-power
433.92MHz BoLinks Passive RFID grid locator
choke hazmat tags. Bosch actively contributed to
the specification of the SoC for its manufacturing
requirements. Emerging Low-Power Wide-Area
Network (LPWAN) wireless 2.4GHz technology
product gateways include Multitech Conduit (2x
USB, 2x Ethernet, 2x RS232, RS485, etc.). MTAC-
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GLTE-L (Q26 UltraLite cellular modem) helps the
local gateways link to the cellular 3G 4G backhaul
to cloud services. Another option in the US is
Hologram Nova. _entry cites the proven and
successful LEACH article he found.
5.1. Smart Cities
Due to the tremendous advances in wireless
networks in recent years, it has become possible to
implement a wide variety of smart city applications
using wireless communication. Since potential
connectivity and quality of services are the keys to
success in smart cities, it is imperative to improve
wireless networks in order to accommodate the
increasing volume of data and expected devices.
Some current smart city scenarios suffer from
degraded performance because a large number of
sensors are directly connected to the cloud without
restrictions, as required to guarantee the
continuity of operations.
In the context of smart cities, we have a large
number of potential objectives to achieve. For
instance, voluminous data related to feedback can
provide solutions for achieving environmental
sustainability by monitoring and controlling the air
pollution in urban areas and/or areas of interest.
An efficient storage space and proper design of
environmental databases lead to beneficial uses
and the betterment of the standard of living in
urban environments.
This systematic review first presents a basic review
of the smart city and then discusses wireless
networks in smart cities. As a part of this
discussion, priorities of wireless technology in
smart cities, potential benefits of wireless
technology, challenges for applying wireless
technology, and available solutions in the
deployment of new technologies and enhancing of
algorithms are provided. Furthermore, four types
of wireless technologies (LoRa, 5G, Sigfox, and Wi-
Fi) are applied as facilitating technologies in the
smart city, and an analysis of these improvements,
challenges, and purposes are featured.
6. Security Considerations in Enhanced
Wireless Networks
The security of wireless systems is of utmost
concern. It is well understood that the wireless
channel communicates data in a broadcast manner,
thus making it susceptible to numerous types of
attacks. As wireless radio uses frequencies in the
electromagnetic spectrum, wireless transmissions
are
publicly
accessible
and
broadcast
communication. It is possible to overhear the
communication. Advanced encryption standards,
strong authentication, and privacy-enhancing
mechanisms are the technical components used to
protect wireless communication from threats. In
recent years, vast developments have been
observed in wireless communication systems. 5G
and beyond technologies are novel standards for
wireless communication that provide higher data
rates and enhance current systems. High data rates
and better performance have increased the
demand for using wireless networks. For example,
now people use free Wi-Fi even outside our regular
house, such as connecting their mobiles in malls,
international airports, amusement parks, etc. The
number of wireless networks increases day by day.
However, Wi-Fi networks are vulnerable to threats,
attacks, and risks that affect the wireless network's
performance. A brief detail is as follows.
The vulnerabilities of the network are the
weaknesses and loopholes that weaken the
security of a system. The occurrence of different
threat types results in implementing various types
of
security
countermeasures
(techniques)
designed to diminish potential threats. Risks pose
a constant threat to the security and performance
of wireless networks, business activities, and
network data. Risks have an enormous effect on
wireless networks and distort data; hence,
assessing threats is essential for a secure wireless
network. Some of the commonly known threats
that affect wireless networks are DoS attacks, Man
in the Middle (MitM), Inside Wire-Tap and
snooping,
Evil
twin,
Honeypot,
Security
Misconfigurations.
6.1. Encryption and Authentication
It is vital to secure the transmission of data
between network entities. It is important for
SMARTICs to use a high level of security to encrypt
the transmitted plant data, as well as banking
information and user data. Data encryption is the
process of converting the data into non-readable
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form using encoding so that only an authorized
person can convert that data into a non-readable
form; for securing the plant data, Advanced
Encryption Standard (AES) with a 256-bit key is
used. Some protocols and mechanisms are
available to execute the above-mentioned
technologies; for example, the Transport Layer
Security (TLS) and the Secure Socket Layer (SSL)
protocols are used to enable device authentication.
This technology made the user verify the devices
and application executed in it, ensuring the data on
the vehicles is only received by its authorized
party. A major and critical aspect that must be
considered is that this advanced security can cause
a high load on the network. Thus, wireless
encryption, with the use of AES, has an influence on
network performance.
Wireless encryption is not enough to secure the
networks; it is important to secure the plant data in
vehicles from rogue applications. The rogue
applications can collect the plant data and disclose
that information to other users, which violates the
vehicle user's privacy. To prevent rogue
application deployment inside the vehicle, the user
has to authenticate the applications running in the
vehicles. When a vehicle is running two or more
applications, the network must ensure that the
vehicle
can
authorize
each
application
independently; such a technique is called
application
verification
or
application
authentication. Authentication is a technique used
to verify and check the identity of the entity (i.e.,
user, device, or web-based application), where
every session that is created for the first time
requires an authentication method to be
performed. The user has to authenticate to access
any web page and send plant data to a database,
which ensures that only authorized persons can
access data from the database.
7. Future Directions in Wireless Network
Performance Enhancement
The many improvements we have seen in wireless
networks are driven by a mix of new theory,
techniques,
technologies,
paradigms,
and
methodologies such as those documented in this
chapter. In the coming years, new and more
improved techniques and innovative methods are
expected to steer the trajectory of wireless
network improvements in general. Some of the
anticipated future trajectories for enhancing
wireless network performance include improved
modulation and coding techniques combining
advanced
digital
theory
with
increasing
computational power. These can be used to design
modems that can handle very high frequency bands
in which the communication suffers from extreme
attenuation.
Artificial intelligence and machine learning are
expected to be game changers in modern
engineering optimization, including wireless
network performance optimization. AI and ML can
provide stronger abstraction, thus sparing the
designers from low-level optimization of individual
parameters. Learning techniques and AI can adapt
the communication system to rapidly changing
environments and even maintain performance in
occasions of lacking or even bad data. This learning
computational power will be the basis for networks
that can learn and adjust themselves automatically.
The development of low Earth Orbit (LEO) satellite
constellations that can provide broad internet
coverage at a fraction of today's satellite cost in a
lucrative market. An example is Elon Musk's
SpaceX Starlink constellations. In the not too
distant future, reliable low-latency internet on the
airline passenger’s mobile device will be
commoditized. This will be enabled through an
increase in satellite coverage and the capability of
handover from one satellite to another, and finally
from a satellite to a ground-based mass network.
7.1. Artificial Intelligence and Machine
Learning
Machine learning in general, and more specifically
artificial intelligence, have become very attractive
technologies to improve the performance of
wireless networks. The specific applications of
AI/ML in wireless networks are very broad,
emanating from applications that improve wireless
network performance up to applications that are
designed to re-architect the entire wireless
network. Many ML techniques, especially deep
learning models, have shown a dramatic
improvement
over
existing
conventional
techniques in a vast array of applications for
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wireless communications. In order to understand
how AI/ML can be used to design future wireless
communications, it is first necessary to
demonstrate the core principles, applications, and
limitations of these AI models. Then, specific
applications that improve, inter alia, wireless
network performance, as well as applications that
extend to re-architecting the wireless network, are
discussed in-depth.
A main drawback of using AI/ML in wireless
communications is that these powerful AI models
can make it difficult to gain an intuitive
understanding of the reasons behind why the AI
model made a certain decision. For example, in RF
sensing, it may be difficult to ascertain why a
certain user was selected or why a certain beam
selection was made. Hence, AI models might not be
preferable to use for latency or other applications
that demand an explanation of decisions. A few
important applications exist where AI can improve
the efficiency of wireless communications
networks. For example, AI can facilitate in the
physical layer because physical layer imperfections
in conventional wireless communications can be
better predicted with AI algorithms. Additionally,
some AI models can aid in CRAN networks.
Networks can attempt to predict the trajectory of
certain wireless stations moving throughout the
network to optimize the resource allocation of
subsequent users. This predictive AI algorithm is
represented as a mixture density network that
jointly generates the deterministic input of the
network t and the conditional density of xt given
the input. Other applications of AI algorithms in
wireless systems include automatic modulation
classification representing practical applications of
AI/ML models in wireless networks.
8. CONCLUSION
The demand for wireless network services is
tremendous all over the world. The demand can
only be satisfied with enhancements in the
capacity, coverage, and quality of the wireless
networks. High frequency bands can provide the
opportunity to use wider bandwidths, which can
enhance the performance of wireless networks. To
overcome the propagation loss, it is necessary to
design new transmitters to produce high gain
radiations. Furthermore, it is necessary to adopt
new multiple access techniques to separate the
signals of different users. These requirements can
be met by employing amplifying antennas as
transmitters at the basestation sites. The signals of
different users can be separated by using massive
MIMO (Single Carrier or OFDM/OFDMA based)
multiple access schemes.
In conclusion, improvements in the performance of
wireless networks can be made by using higher
frequency bands. Due to numerous advantages in
the usage of higher frequency bands, these bands
are considered leaders in the future of
communications and will be highly beneficial to
cater to the demands of the users in a more efficient
and reliable manner. This technology also offers
several potential research directions, such as
designing intelligent massive MIMO based adaptive
arrays to maintain reasonable gains over the large
band of the communication spectrum. This can be
done by employing low-fidelity models for
estimating the statistics of the channel.
Furthermore, the authors can also predict link
quality to show the intelligent wireless transport
connections using massive MIMO antennas. A
satellite internet/cellular network can be designed
to serve the rural areas of underdeveloped
countries while providing seamless global mobility
for the wealthy urban communities. The system
design must be sustainable, with low energy
consumption in addition to good Quality of Service
(QoS).
8.1. Summary of Key Findings
We are aiming not only at building a connected
world relying on wireless networks, but also
making it seamless and transparent to the end user.
Since the demand for high speed and quality
wireless communications is increasing, this study
focuses on the main current challenges and
proposes some possible solutions based on the
exploitation of advanced technologies. In fact, the
designed mobile and wireless networks have to
cope with some critical radio conditions, e.g., high
losses and variations in the radio environment,
resulting in an overall reduced performance. In
addition, statistical characterization of traffic is an
important input to properly plan wireless
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networks capacity.
In order to face the challenges arising in the
wireless networks, this study proposes two
solutions: considering the use of visible lights and
exploiting the envelope level crossing rate as a
possible traffic characterization parameter.
Indeed, due to the wide use of mobile devices and
the large number of data that require exchange
between the core network and the remote
terminals, wireless networks are exploited in an
intensive way today. The subsequent evolution of
the scheme is toward the so-called fifth generation
(5G) mobile communication systems. The main
objectives would be to enhance some system
metrics such as the throughput, the spectrum
resource efficiency, the spectral leakage region,
and the spectral nuisance level, not only for
advanced users' services but also to directly
support the increased data traffic demands. The
usage and the wide coverage of wireless networks
raise the problem of suitable traffic patterns
analysis as part of planning. Unfortunately, the
existing literature is mainly focused on the radio
planning, leaving the analysis of the current
wireless world architecture as innovative and
predominantly
unexplored.
Given
these
considerations, the main contributions of the essay
are summarized in the following.
9. REFERENCES
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