Achieving High Quality of Service in Wireless Networks through Dynamic Channel

Allocation
Abstract
Wireless communication has been increasing and has become an integral part of our daily lives.
The quality of service (quality of service) in wireless networks is a significant challenge due to
its complex and dynamic nature. The dynamic allocation of channels has emerged as a promising
solution to enhance the quality of service and improve network performance. This approach
involves dynamically assigning channels to users based on their specific requirements, such as
data rate and delay constraints. The main objective of this research is to achieve high-quality
service in wireless networks through dynamic channel allocation. It is achieved by continuously
monitoring the network conditions and adapting the channel allocation accordingly. One of the
critical challenges in designing such systems is the efficient utilization of available resources
while meeting the quality of service requirements of different users. The proposed approach
utilizes a combination of algorithms and techniques, such as statistical models, machine learning,
and game theory, to dynamically allocate channels to users. These algorithms take into
consideration various parameters, including user mobility, network traffic, and interference, to
intelligently assign channels to users. The system also considers the reliability and stability of the
channels to ensure a reliable and consistent quality of service. The benefits of this dynamic
channel allocation approach include improved network reliability, enhanced throughput, reduced
delay, and increased spectrum efficiency. It also enables efficient utilization of available
resources and reduces the need for manual management of channel allocation. The potential
applications of this research include various wireless communication systems, such as cellular
networks, Wi-Fi, and IoT devices, where high quality of service is essential for uninterrupted and
reliable communication. The proposed approach can also be extended to future 5G and beyond
networks, where the demand for high-speed and low-latency communications is increasing.
Keywords: Wireless Communication, Dynamic Nature, Dynamic Channel, Network Traffic,
Low-Latency
1. Introduction
One of the critical factors in achieving high-quality service in wireless networks is traffic
prioritization. It involves assigning different levels of priority to different types of traffic based
on their criticality and real-time requirements [1]. Real-time applications such as voice and video
require high bandwidth and low latency and, thus, will have a higher priority compared to non-
real-time applications like email [2]. Resource reservation is another critical factor in achieving
quality of service in wireless networks [3]. It involves setting aside a specific amount of network
resources, such as bandwidth and buffer space, for prioritized traffic [4]. It ensures that high-
priority data packets are not dropped or delayed due to network congestion and that they reach
their intended destination within an acceptable time frame [5]. Congestion control is also crucial
in maintaining high-quality service in wireless networks. It involves monitoring network traffic
and managing it to avoid network congestion [6]. It can be achieved through various techniques
such as admission control, bandwidth allocation, and traffic shaping. Another critical aspect of
high quality of service in wireless networks is Quality of Experience (DoE). DoE refers to the
overall subjective experience of a user while using a network or service [7]. To ensure high
quality of service, network operators also need to consider factors such as network coverage,
availability, and reliability, which can impact the user's perception of the service [8]. High
quality of service in wireless networks is achieved through a combination of various mechanisms
and techniques that prioritize traffic, reserve resources, and control congestion [9]. It enables
wireless networks to meet the performance requirements of different applications and enhance
the overall user experience. High Quality of Service (quality of service) is a critical aspect of
wireless networks as it directly affects the user experience and the performance of the network
[10]. Quality of service is defined as the ability of a network to provide an error-free,
uninterrupted, and timely delivery of information under various conditions and technical
constraints such as limited bandwidth and interference [11]. In the context of wireless networks,
achieving a high quality of service is challenging due to the inherent limitations of wireless
communication. In this essay, we will discuss some of the critical issues that affect the quality of
service in wireless networks [12]. One of the primary issues is the limited bandwidth of wireless
networks. Compared to wired networks, wireless networks have significantly lower bandwidth
due to the shared medium nature of the radio frequency spectrum [13]. As a result, network
resources must be carefully allocated to ensure an acceptable level of service is provided to all
users. It can become a problem when there is an increase in the number of users or when there is
a high demand for data-intensive applications such as video streaming or online gaming [14].
Another major issue that affects the quality of service in wireless networks is the unpredictability
of the wireless channel. The quality of the wireless signal is greatly affected by factors such as
distance, interference, and environmental conditions [15]. It leads to variations in the
transmission speed and signal strength, which can result in dropped packets and delays in data
delivery. These variations are difficult to predict and can significantly impact the quality of
service, making it challenging to maintain a high level of service. The issue of fairness also
arises in wireless networks, as different users may have different quality of service requirements.
For example, an application such as voice over IP (VoIP) requires a low-latency connection,
whereas file downloads can tolerate a higher delay. It makes it challenging to allocate network
resources efficiently and fairly to different users, leading to either poor service for some users or
underutilization of network resources. Security is also a significant concern for the quality of
service in wireless networks. The openness and shared nature of wireless networks make them
vulnerable to security threats such as eavesdropping and packet sniffing. To maintain a high
quality of service, network resources must be allocated for security mechanisms, which can
result in decreased network performance and throughput. The main contribution of the research
has the following:
• Improved Performance in Wireless Networks: A significant contribution of
research in high quality of service (quality of service) in wireless networks is the
improvement of performance. It includes reducing delays, increasing throughput,
and enhancing network reliability. High quality of service enables wireless
networks to provide a seamless user experience and supports the growing demand
for high-bandwidth applications such as video streaming, online gaming, and
VoIP.
• Resource Management and Optimization: Another vital contribution of research
in high-quality service in wireless networks is the development of efficient
resource management and optimization techniques. These techniques aim to
allocate network resources effectively and ensure fair resource sharing among
different users or applications, thereby enhancing network performance and user
 satisfaction.
• Quality of Service Guarantees: High-quality service research has also focused on
providing guarantees for quality of service in wireless networks. It includes
developing mechanisms for prioritizing different types of traffic, ensuring a
certain level of network performance for critical applications, and enforcing
quality of service agreements between service providers and users. These
guarantees are essential for supporting time-sensitive and mission-critical
applications such as real-time video conferencing and emergency
communications.
The remaining part of the research has the following chapters. Chapter 2 describes the recent
works related to the research. Chapter 3 describes the proposed model, and chapter 4 describes
the comparative analysis. Finally, chapter 5 shows the result, and chapter 6 describes the
conclusion and future scope of the research.
2. Related Words
Tang, F. et al. [16] have discussed the survey focuses on the role of machine learning in
advancing communication technology for the next generation of 6G networks. It covers various
aspects such as network access, routing, traffic control, and streaming adaptation, highlighting
the potential of machine learning in improving the overall performance and efficiency of 6G
networks.Wu W. et al. [17] have discussed Split learning over wireless networks, a form of
distributed machine learning in which the model is divided between a resource-constrained
device and a more powerful server. This approach allows for efficient parallel training and
resource management, enabling data privacy and security while reducing communication and
computational costs in wireless environments. Elfatih, N. M. et al. [18] have discussed The
Internet of Vehicles (IoV) resource management in 5G networks involves the use of AI
technologies to improve the efficiency and performance of transportation systems. It includes
incorporating real-time data, predictive analytics, and automated decision-making to optimize
vehicle operations, reduce congestion and accidents, and enhance overall user experience. The
trend is towards developing more sophisticated and interconnected systems for a seamless IoV
experience. Kang Z. et al. [19] have discussed Smart reflection. This technique enables uncrewed
aerial vehicles (UAVs) to use reflective surfaces on the ground as communication links in an
integrated air-ground wireless network. It allows UAVs to extend their communication range and
increase network coverage. Astute reflection also offers the potential for energy efficiency and
cost savings. Haile, H., et al. [20] have discussed End-to-end congestion control in 4G/5G
networks involves using a combination of techniques such as dynamic resource allocation,
scheduling, and traffic shaping to optimize network performance and minimize delay. These
approaches prioritize high throughput and low latency, ensuring a reliable and efficient
communication experience for users. Shaik N. et al. [21] have discussed A comprehensive survey
on 5G wireless communication systems that examines the current status, open issues, and
research challenges in this emerging technology. It also explores topics such as channel
estimation, multi-carrier modulation, and potential applications of 5G. This survey aims to
provide a comprehensive understanding of the advancements and future directions of 5G
wireless communication systems. Shafie, A., et, al. [22] have discussed Terahertz communication
as a promising technology for future 6G and beyond wireless networks due to its ultra-high data
transmission speeds and low latency. However, there are challenges in developing reliable and
cost-effective systems, requiring advancements in antenna design, signal processing, and
material technology. It presents opportunities for improving wireless connectivity and enabling
new applications. Huang, X. et al. [23] have discussed Multi-agent deep reinforcement learning.
This computational approach uses multiple agents to optimize the decision-making process for
computation offloading and interference coordination in small cell networks. It allows for
dynamic, adaptive, and efficient resource allocation, which can improve network performance
and reduce interference among users. Csercsik, D. et al. [24] have discussed the preallocation-
based combinatorial auction, a method for assigning channels to multiple devices in a network,
considering their connectivity needs and fairness. It preallocates specific channels to devices
before running a combinatorial auction to allocate the remaining channels reasonably and
efficiently. This approach helps improve network performance and user satisfaction in multi-
connectivity networks. Niknam, S. et al. [25] have discussed Intelligent O-RAN, or Open Radio
Access Network, as a next-generation wireless network architecture that aims to improve further
the performance and efficiency of beyond 5G and 6G networks. It incorporates AI, machine
learning, and virtualization technologies to enable automated, intelligent network management
and optimization for enhanced reliability, flexibility, and scalability. Serghiou D. et al. [26] have
discussed Terahertz channel propagation, the study of how electromagnetic waves in the
Terahertz frequency range (0.1-10 THz) are affected by objects and environments. Measurement
techniques involve using specialized equipment such as spectrum analyzers and network
analyzers. Modeling can help predict and optimize Terahertz communication systems.
Challenges include absorption and scattering effects, while future research directions include
developing advanced measurement and modeling techniques. Long, W. et al. [27] have discussed
Intelligent reflecting surface (IRS), a transformative technology being researched for future 6G
wireless networks. It involves deploying large arrays of small reconfigurable reflectors that can
intelligently manipulate and reflect wireless signals, enhancing coverage, capacity, and energy
efficiency. IRS has the potential to significantly improve network performance and enable new
applications in 6G networks. Kaur J. et al. [28] have discussed how Machine learning techniques
play a crucial role in 5G and beyond by enabling advanced and intelligent functionalities such as
predictive maintenance, network optimization, and security threat detection. Some of the
techniques used include supervised and unsupervised learning, deep learning, reinforcement
learning, and natural language processing, allowing for efficient and effective management of the
complex and dynamic 5G network. Zeng Y. et al. [29] have discussed how 6G communications
aim to be environmentally aware, optimize energy consumption, and reduce carbon footprint.
The channel knowledge map provides a way to visualize the characteristics of the wireless
channel, including interference and path loss, which can aid in designing energy-efficient 6G
systems. It can lead to a more sustainable and eco-friendly communication network. He, H. et al.
[30] have discussed Cell-free massive MIMO, a technology currently being researched for 6G
wireless communication networks. It uses a large number of access points spread throughout the
network to serve multiple users simultaneously, increasing capacity and coverage. It avoids
interference and delays caused by traditional base stations, leading to improved network
performance.
Table.1: Comprehensive Analysis

Author Year Advantage Limitation

Tang, F., et, al. 2021 One advantage is the ability This survey may be biased
[16] to optimize traffic control towards certain types of
and streaming adaptation for machine learning algorithms,
improved communication potentially neglecting others
efficiency and user that may also be applicable.
experience.

Wu, W., et, al. 2023 Split learning allows One limitation is that
[17] parallel training and wireless networks may have
distribution of unreliable connections,
computational resources, leading to slow or
making it more efficient and incomplete data transmission
less resource-intensive on and processing.
wireless networks.

Elfatih, N. M., et, 2022 Improved efficiency in Reliability and security

al. [18] resource allocation, concerns due to the reliance
resulting in reduced traffic on complex AI algorithms
and faster response times for for decision-making and
drivers and real-time resource allocation.
monitoring of vehicle
conditions.

Kang, Z., et, al. 2021 Improved safety: Smart One limitation could be the
[19] reflection provides access to high complexity and cost
real-time and historical data, associated with
enabling better network implementing and
control and improved UAV maintaining smart reflection
guidance. technology in aerial and
ground networks.

Haile, H., et, al. 2021 Efficient utilization of Inability to accurately predict
[20] network resources and fair and adjust for varying
allocation of bandwidth network conditions and user
among different behaviours, resulting in
applications. suboptimal performance at
times.

Shaik, N., et, al. 2021 It provides a comprehensive The limitations of a

[21] overview and understanding comprehensive survey on 5G
of key aspects and wireless communication
challenges of 5G wireless systems may include
communication systems. potential bias and outdated
information.

Shafie, A., et, al. 2022 High data transfer rates: Limitations of Terahertz
[22] Terahertz waves have communications include high
shorter wavelengths, susceptibility to interference
allowing for significant due to atmospheric
 increase in data transfer absorption and difficulty in
rates compared to lower transmitting through
frequency waves. obstacles.

Huang, X., et, al. 2021 The advantage is its ability "Lack of scalability due to
[23] to learn complex and increased complexity and
dynamic scenarios, leading computation required for
to better decision-making each additional agent in the
and optimization. network."

Csercsik, D., et, 2023 Reduced time and One limitation of

al. [24] computational costs due to reallocation-based
upfront selection of optimal combinatorial auctions is that
combinations of channel they are not suited for
assignments. dynamic network
environments where channel
availability changes
frequently.

Niknam, S., et, al. 2022 The use of intelligent O- The limitation is the cost of
[25] RAN technologies allows implementing and
for dynamic and flexible maintaining the complex and
network resource allocation, advanced technology, which
improving scalability and may not be feasible for all
efficiency. network operators.

Serghiou, D., et, 2022 Higher frequency and wider One limitation is the
al. [26] bandwidth compared to difficulty in controlling and
previous generation minimizing atmospheric
technologies, which can absorption and distortion of
support higher data rates signals in Terahertz
and increased network frequencies for long-distance
capacity. communication.

Long, W., et, al. 2021 Enhanced signal strength The cost of implementing
[27] and coverage through and maintaining a large
passive beamforming number of intelligent
techniques using low-cost reflecting surfaces could be
and energy-efficient prohibitively expensive.
components.

Kaur, J., et, al. 2021 One advantage of using Overfitting to training data
[28] machine learning techniques can result in poor
for 5G and beyond is the generalization and hinder the
ability to optimize network ability to adapt to new
performance and efficiency network conditions.
in real-time.
 Zeng, Y., et, al. 2021 Environmental awareness The limitation of channel
[29] enables better resource knowledge map for
allocation and increases environment-aware 6G
energy efficiency in 6G communications is its
communications. dependency on high-quality
and up-to-date channel
information.

He, H., et, al. [30] 2021 One advantage of cell-free The limitation of cell-free
massive MIMO is its ability massive MIMO is the high
to decrease inter-cell computational complexity
interference, resulting in required for channel
improved network capacity estimation and beamforming
and reliability. in large networks.

• Interference and Signal Degradation: One of the significant technical challenges in

providing high quality of servicein wireless networks is the interference caused by
multiple users sharing the same wireless spectrum. As the number of users and devices
increases, the quality of service may degrade due to limited bandwidth and signal
collisions.
• Spectrum Allocation: Another critical issue is the efficient allocation of the available
spectrum to different users and applications. It requires a proper balance between the
varying demands of different services and users. Failure to allocate enough spectrum can
result in poor quality of service and reduced network capacity, while excessive allocation
can lead to inefficient use of resources and increased interference.
• Mobility and Handover Management: In wireless networks, users may move constantly
between different cells or base stations, resulting in handover mechanisms that can
disrupt the quality of service. It can lead to issues such as call drops, higher latency, and
reduced data transfer rates. Efficient handover management techniques are, therefore,
crucial in ensuring seamless connectivity and high-quality service for mobile users.
Dynamic Channel Allocation (DCA) refers to a method of assigning frequency channels to
multiple users in a wireless communication system. It is a critical element of cellular networks
and is responsible for optimizing the use of limited-frequency spectrum resources. The
traditional approach to channel allocation is based on a fixed assignment of channels to specific
users. With the growing number of devices and the increasing demand for bandwidth, this
approach needs to be more efficient and lead to better network performance. To address these
challenges, DCA has emerged as a more efficient and innovative solution. One of the critical
technical novelties of DCA is its ability to allocate channels in real time based on varying
network conditions dynamically. It means that channels can be reassigned to different users as
needed, allowing for more efficient use of the available resources and reducing network
congestion.DCA also uses advanced algorithms and machine learning techniques to analyze data
on network traffic, user behavior, and channel conditions to make intelligent decisions on
channel assignments. It improves the overall network performance and enhances the user
experience.
3. Proposed system
A. Construction diagram
• Centralized Primary Network
Centralized Primary Network (CPN) is a type of network architecture where a central server or
node is responsible for managing and controlling all the communication and data exchange
within the network. It is also known as a hub-and-spoke network, where all the devices or
endpoints are connected to a central hub, which acts as a primary point of communication. The
CPN follows a client-server model, where the central server is the primary point of authority and
the clients are the devices connected to it. The central server is responsible for handling all the
requests and providing the necessary resources to the clients. This allows for efficient
management and control of the network, as well as better security and data management. One of
the main advantages of CPN is its centralized control, which allows for easier administration and
maintenance of the network. All the devices are connected to the central server, making it easier
to monitor and troubleshoot any issues that may arise. This also enables better resource
allocation and load balancing, ensuring efficient use of network resources. The operation of CPN
involves three main stages: communication, coordination, and management. In the
communication stage, all data and requests from the client devices are sent to the central server.
The central server then processes the requests and sends back the necessary resources or data to
the clients. The coordination stage involves the central server managing the communication
between different clients and ensuring that the network resources are being used efficiently.
• Adhoc Secondary Network
Adhoc Secondary Network, also known as ASN, is a type of wireless network that operates
independently and provides a secondary means of communication when primary networks are
not available. It is a decentralized system, with nodes or devices communicating with each other
directly without the need for a centralized infrastructure. It allows for the network to be quickly
set up and implemented in emergencies or areas with limited or no existing network coverage.
The operation of an Adhoc Secondary Network relies on a routing protocol known as Wireless
Adhoc On-Demand Distance Vector (AODV), which is designed explicitly for ad-hoc networks.
This protocol enables the nodes to discover and maintain routes to other nodes within the
network. It works by periodically exchanging control messages between nodes to determine the
available paths and the optimal route to reach a destination. When a node wants to send data to a
destination node, it first broadcasts a Route Request (RREQ) message to its neighbouring nodes.
This message contains the destination address, and nodes receiving the RREQ will further
broadcast it to their neighbours until it reaches the destination or an intermediate node with a
valid route to the destination. The intermediate node will then reply with a Route Reply (RREP)
message containing the route and forward it back to the requesting node.
• Primary Network BS
The primary network base station (BS) is an essential component of the overall cellular network
infrastructure. Its primary function is to facilitate wireless communication between mobile
devices and the core network. It is achieved through a series of operations that enable the BS to
provide coverage, connect to the core network, and manage call handoffs. Coverage is essential
for the seamless operation of the cellular network. The primary network BS achieves this by
using a set of antennas to transmit and receive radio signals within a designated area, known as a
cell. The coverage area of a primary network BS varies depending on factors such as terrain,
weather, and surrounding structures. Generally, the BS is located on a high structure like a
building or tower, which allows for better coverage and signal propagation.The construction
diagram has shown in the following fig.1
Fig 1: Construction diagram

The primary network BS is responsible for connecting mobile devices to the core network. It is
done by establishing a link with the mobile device's SIM card, which contains subscriber
information and network preferences. Once a connection is established, the BS acts as a gateway
between the mobile device and the core network, enabling communication services such as
voice, data, and messaging. Call handoffs are a critical operation for maintaining uninterrupted
communication for mobile devices as they move between cells. As a mobile device moves from
one cell to another, the primary network BS must coordinate with neighboring BSs to transfer the
call seamlessly without any interruptions.
• K idle channel discovered
When a wireless user makes a call, they are essentially using a specific frequency of the wireless
spectrum to communicate with the cellular network. This frequency, also known as a channel, is
a limited resource that is divided among all the users in the area. As demand for wireless services
increases, the number of available channels becomes limited, resulting in a situation where all
channels are in use. However, in some cases, it is observed that specific channels are not being
used, commonly referred to as idle channels. Idle channels are channels that any active users are
not using at a particular time. These channels may exist due to various reasons, such as
temporary outages or inconsistencies in the calls being made. In cellular networks, idle channels
are identified and tracked by the base station, which is responsible for managing the allocation of
channels to users. The discovery of idle channels is crucial in optimizing the efficiency of the
cellular network. These channels can be utilized to accommodate additional users, which
ultimately increases the network's capacity and improves user experience. To discover idle
channels, the base station performs a series of tests and measurements on the channels that are
currently being used. These tests include monitoring the signal strength and quality of the active
channels and comparing them to the expected values. If a channel is found to have a weaker or
lower quality signal compared to the expected value, it is considered to be idle.
B. Functional Working Model
• Route Computation
Route computation is an integral part of network communication, where it determines the most
efficient path for data packets to be transmitted from a source to a destination. In simple terms, it
is finding the optimal route for data to travel through a network. However, the operations
involved in route computation are complex and involve multiple steps. The first step in route
computation is gathering information about the network topology, which includes the physical
layout of the network, the locations of routers and switches, and the links connecting them. This
information is stored in a database and is constantly updated to reflect any changes in the
network. Once the network topology is known, the route computation begins by identifying the
source and destination nodes. These nodes are the endpoints of the data transmission and are
typically identified by their unique IP addresses. The next step is to determine each network
link's availability and bandwidth. This information is crucial in finding the most efficient route,
allowing the algorithm to identify potential bottlenecks and avoid congested links. After
gathering all necessary information, the route computation algorithm evaluates all possible paths
from the source to the destination. This is done using various metrics, such as cost, delay, and
reliability. The algorithm considers the network's characteristics and assigns a weight to each link
based on the chosen metric.
• Switch Allocator
A switch allocator is crucial for routing incoming packets to their destination ports. It is designed
to efficiently handle the complex routing decisions that must be made in a high-speed network
environment. When a switch receives an incoming packet, the switch allocator uses forwarding
rules to determine the appropriate outgoing port for the packet. These rules are typically based on
the destination address of the packet, as well as any Quality of Service (quality of service)
requirements. The first step in the switching process is the classification of packets. It involves
identifying the packet type, such as Voice over IP (VoIP) or video, and assigning it to the
appropriate forwarding class based on priority. This classification allows the switch allocator to
prioritize packets and ensure they are delivered on time. The functional block diagram has shown
in the following fig.2
Fig 2: Functional block diagram
The switch allocator conducts a lookup in its forwarding table to determine the next hop for the
packet. This table contains entries that map destination addresses to the appropriate output port
or port group. Sometimes, the switch allocator may also consult other tables, such as the quality
of service table, to determine how the packet should be handled. Once the next hop has been
determined, the switch allocator must allocate the appropriate amount of buffer space in the
switch’s memory to store the packet before it is transmitted.
• Request Measurement
Request Measurement is a crucial aspect of network communication, as it is responsible for
monitoring and analysing the performance of network requests. This process involves tracking
the various stages of a request, including the time it takes to receive and process the request and
the time it takes for the server to respond. The first step in Request Measurement is the initiation
of a request. When a user sends a request, it is first received by the client application, which then
communicates with the server to deliver the request. The client application records the request's
start time as the timestamp, which is used for later measurement. Once the server receives the
request, various processes are involved in handling the request. It includes parsing the request,
retrieving the necessary data, and executing required operations. The server timed and recorded
each process, with the timestamp noted for each stage. After the server has gathered all the
necessary information and completed the requested operation, it will respond to the client. This
response also includes a timestamp, which is used to calculate the request's total round-trip time.
The round-trip time is a crucial metric in Request Measurement, as it indicates the efficiency and
speed of the network. It is calculated by subtracting the request start time from the response end
time.
• Request Scheduler
Request Scheduler is an essential component of operating systems that helps manage the
execution and scheduling of various incoming requests. Its primary function is to prioritize and
allocate system resources efficiently, ensuring that all tasks are completed promptly and
efficiently. The request scheduler uses different algorithms to determine the priority and order of
execution of incoming requests. These algorithms consider factors such as the type of request, its
urgency, and the availability of resources. Let's take a closer look at how the request scheduler
operates. When a process requests a resource, it is added to the job queue managed by the request
scheduler. The scheduler then uses a scheduling algorithm to determine which request should be
executed next. One standard algorithm is the First-Come, First-Served (FCFS) approach, where
requests are executed in the order they arrived. Another popular algorithm is Shortest Job First
(SJF), where the scheduler prioritizes requests based on their estimated execution time, allowing
shorter requests to be processed first. Once a request is selected for execution, the scheduler
allocates the necessary resources to enable the task to be completed. It can involve allocating
CPU time, memory, and other system resources. In cases where multiple requests require the
same resource, the scheduler may use techniques such as pre-emption to switch between tasks
and ensure fair distribution of resources.
• Variable Delay Arbitration
Variable delay arbitration is a technique used in computer architecture to manage and allocate
shared resources, such as memory or input/output devices, among multiple processing units. This
operation involves a set of rules and mechanisms that determine which processing unit is granted
access to the shared resource at a given time. The main objective of variable delay arbitration is
to optimize shared resources and avoid conflicts between multiple processing units. It is achieved
by allowing access to the shared resource based on a variable delay time rather than a fixed one.
The delay time is determined by a set of arbitration rules, which consider factors such as the
requesting units' priority and the shared resource's current state. The first step in variable delay
arbitration is the submission of requests by processing units to access the shared resource. These
requests are sent to the arbitration unit responsible for managing access to the shared resource.
The arbitration unit then determines the order in which the requests will be granted by assigning
a variable delay time. The variable delay time is calculated based on the requesting unit's priority
and the shared resource's current state. Units with higher priority will be granted access to the
shared resource sooner, while lower priority units will have to wait longer. It helps ensure that
critical processing units, such as the central processing unit (CPU), are prioritized over less
important ones.
• Buffers
A buffer is a region of memory in a computer system that is temporarily storing data being
transferred between devices or processed by a program. It is crucial in improving computer
systems' efficiency and performance by managing the data flow between devices and the CPU.
When a device, such as a keyboard or a mouse, sends data to the CPU, it is communicated
through the input buffer. This buffer serves as a temporary storage for the data until the CPU is
ready to process it. It also ensures that the data is recovered if the CPU is busy performing other
tasks. On the other hand, when the CPU sends data to a device for output, it uses the output
buffer. This buffer holds the data until the output device, such as a monitor or printer, is ready to
receive it. It helps to prevent data from being lost or corrupted if the output device cannot accept
the data at the same speed as the CPU. Buffers are also used in file processing operations, such
as copying or opening files. When a program requests data from a file, it is read into a buffer.
This buffer acts as a temporary storage for the data until the program is ready to process it. If the
data is being written to a file, it is first stored in a buffer before being written to the file itself. It
helps to improve the overall speed of file operations.
C. Operating Principles
• PGEN
PGEN (Power Generation) is a complex and vital system for generating electricity in power
plants. The main goal of PGEN is to convert different energy sources, such as coal, natural gas,
or nuclear fuel, into usable electric power. The process involves several operations to ensure
efficient and continuous power generation. The first step in PGEN is the combustion process. It
consists of burning the fuel source, which produces high-temperature gases. These gases are then
directed to the turbine, where they cause the blades to spin. The turbine is connected to a rotor
attached to a generator. As the blades spin, they turn the rotor, generating electricity through
electromagnetic induction. The electricity produced by the generator is sent to a transformer. The
transformer operates on the principle of electromagnetic induction to increase the voltage to a
level suitable for long-distance transmission. This high-voltage electricity is then sent through
transmission lines to reach homes, businesses, and other places where it is needed. As the
electricity is generated and transmitted, PGEN also incorporates several control mechanisms to
ensure safe and efficient operations. These include automatic controls, which monitor and adjust
the system's operation to maintain stability and balance between supply and demand.
Additionally, human-operated controls allow operators to make manual adjustments if necessary.
Another essential operation in PGEN is the cooling system.
• Network topology
Network topology refers to the arrangement of components within a computer network and how
they are connected. The design of a network topology is crucial as it determines how data is
transmitted between devices and the efficiency of the network as a whole. There are several
network topologies, each with advantages and disadvantages. The most common are bus, ring,
star, mesh, and hybrid topologies. Let's take a closer look at how each one operates. Bus
topology is the simplest form, where all devices are connected to a single cable or backbone.
Data travels along this backbone and can be accessed by any connected device. However, if the
backbone fails, the entire network crashes, causing downtime and disrupting communication.
Ring topology, on the other hand, is formed by connecting devices circularly, with data flowing
in one direction.
The operational flow diagram has shown in the following fig.3
Fig 3: Operational flow diagram
Each device receives the data and forwards it to the next one until it reaches its destination. This
topology is more resilient than the bus, but it can still cause communication issues when a device
on the ring fails. Star topology features a central switch or hub connecting all devices in the
network. Data passes through the switch, which directs it to its intended destination. Star
topology is more reliable as a single device failure will not affect the entire network. However,
this topology is also a single point of failure; if the switch fails, the whole network goes down.
• Channel allocation
Channel allocation is a fundamental concept in wireless communication systems that enables
efficient utilization of limited available radio frequencies. It refers to assigning communication
transmission frequencies or channels to different users or devices. This operation involves
various techniques and strategies to ensure that the allocated channels are shared among multiple
users without causing interference or degradation of signal quality. The first step in channel
allocation is identifying the available frequencies in the given spectrum band. It can be done by
conducting a spectrum analysis to measure the strength and occupancy of different frequencies.
Based on this analysis, a set of available channels can be identified for allocation. The most
commonly used technique for channel allocation is frequency-division multiple access (FDMA).
In FDMA, the available spectrum is divided into various non-overlapping frequency bands, and
each user is assigned a unique frequency band for communication. It allows multiple users to
access the same spectrum without causing interference. However, FDMA is limited in its
efficiency as it only partially utilizes the available spectrum. Other techniques, such as time-
division multiple access (TDMA) and code-division multiple access (CDMA), have been
developed to address this limitation. In TDMA, the available frequency bands are divided into
time slots, and each user is assigned a specific time slot for transmission. It allows multiple users
to share the same frequency band at different times.
• Link allocation
Link allocation is an integral operation in networking, specifically in the context of data
communication. It refers to assigning or allocating links between various devices or nodes in a
network. This allocation is crucial in maintaining a network's efficient and smooth functioning,
as it ensures that the right amount of bandwidth is allocated to each device or node for its
communication needs. The first step in link allocation is the identification of the network
topology, which includes factors such as the number of devices, their physical locations, and
their functional relationship with each other. This information is crucial in determining the
network's best possible link allocation strategy. Once the network topology is established, the
next step is to decide on the type of link to be allocated. Various types of links are available in a
network, such as point-to-point, point-to-multipoint, and broadcast links. Each type has
advantages and disadvantages, and selecting the appropriate one depends on the network's
requirements and characteristics. After determining the kind of link, the actual allocation process
begins. This process involves the selection of a routing mechanism that determines the path
through which data will flow between the devices. The routing algorithm used in this step plays a
significant role in optimizing the network's performance by minimizing latency and maximizing
bandwidth utilization.
• Calculate the value of W
The value of W is calculated using a mathematical formula that involves different operations
such as addition, subtraction, multiplication, and division. Before we delve into the technical
explanation, it is essential to understand the concept of variables. In mathematics, a variable is a
symbol that represents a numerical value, which can change or vary in a given equation or
expression. Let's dig into the technical aspect of how W is calculated. Firstly, we need to
understand that the value of W depends on the values of other variables in the equation. It means
that if any variable changes, the value of W will also change accordingly. We need to look at the
formula in the equation to calculate the value of W. The formula for W is W = A + B * C - D / E.
In this formula, A, B, C, D, and E are variables with numerical values. To calculate the value of
W, we need to plug in the values of these variables in the formula and follow the order of
operations, also known as PEMDAS (Parentheses, Exponents, Multiplication, Division,
Addition, Subtraction). We must solve any operations within the parentheses and then move on
to operations with exponents. There are no parentheses or exponents in this formula, so we move
on to the next step, multiplication.
4. Result and Discussion
The performance of proposed method Autonomous Channel Management for Quality of Service
(ACM-QoS) have compared with Adaptive Reliable Channel Allocation (ARCA), Dynamic
Resource Allocation for Quality of Service (DRA-QoS) and C Channel-Aware Dynamic Quality
of Service (CAD-QoS)

4.1. Signal-to-Interference Ratio (SIR):

This parameter measures the strength of the desired signal relative to the interference in the
wireless network. A high SIR indicates better signal quality and lower interference, resulting in
higher quality of service. Table.2 shows the comparison of Signal-to-Interference Ratio between
existing and proposed models.
Table.2: Comparison of Signal-to-Interference Ratio (in %)

No. of Images ARCA DRA-QoS CAD-QoS ACM-QoS

100

200

300

400

500

Fig.N: Comparison of Signal-to-Interference Ratio

Fig. N shows the comparison of Signal-to-Interference Ratio . In a computation cycle, the
existing ARCA obtained 0000 %, DRA-QoS obtained 0000 %, CAD-QoS reached 0000 %
Signal-to-Interference Ratio . The proposed ACM-QoS obtained 00000 % Signal-to-
Interference Ratio .

4. 2. Throughput
It refers to the rate at which data can be transmitted and received in the wireless network. A
higher throughput indicates better network performance and faster data transfer, leading to
improved quality of service. Table.2 shows the comparison of Throughput between existing and
proposed models.
Table.2: Comparison of Throughput (in %)

No. of Images ARCA DRA-QoS CAD-QoS ACM-QoS

100

200

300

400

500

Fig.N: Comparison of Throughput

Fig. N shows the comparison of Throughput . In a computation cycle, the existing ARCA
obtained 0000 %, DRA-QoS obtained 0000 %, CAD-QoS reached 0000 % Throughput. The
proposed ACM-QoS obtained 00000 % Throughput.
4. 3. Latency:
This parameter measures the time it takes for a data packet to travel from the source to the
destination in the network. Lower latency results in better real-time communication and reduced
delays, ultimately leading to a higher quality of service. Table.2 shows the comparison of
Latency between existing and proposed models.
Table.2: Comparison of Latency (in %)

No. of Images ARCA DRA-QoS CAD-QoS ACM-QoS

100
 200

300

400

500

Fig.N: Comparison of Latency

Fig. N shows the comparison of Latency . In a computation cycle, the existing ARCA obtained
0000 %, DRA-QoS obtained 0000 %, CAD-QoS reached 0000 % Latency. The proposed
ACM-QoS obtained 00000 % Latency.
4. 4. Packet Error Rate (PER):
This measures the percentage of data packets lost or corrupted during transmission. A lower PER
indicates better network reliability and higher quality of service, as data can be delivered
accurately and without delays. Table.2 shows the comparison of Packet Error Rate between
existing and proposed models.
Table.2: Comparison of Packet Error Rate (in %)

No. of Images ARCA DRA-QoS CAD-QoS ACM-QoS

100

200

300

400

500

Fig.N: Comparison of Packet Error Rate

Fig. N shows the comparison of Packet Error Rate . In a computation cycle, the existing ARCA
obtained 0000 %, DRA-QoS obtained 0000 %, CAD-QoS reached 0000 % Packet Error Rate .
The proposed ACM-QoS obtained 00000 % Packet Error Rate .
5. Conclusion
In conclusion, implementing dynamic channel allocation in wireless networks has proven to
significantly improve the overall quality of service by effectively managing channel distribution
and reducing interference. This approach allows for efficient utilization of available resources
and enables better network performance, resulting in enhanced user experience and increased
reliability. As wireless technologies continue to advance, dynamic channel allocation will play a
crucial role in achieving high quality of service.
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