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Cross Layer Design and Optimization In Cellular Networks

Authors:

Abstract

Cross-layer design" refers to the sharing of information between layers to make effective use of network resources and achieve high adaptivity. Each layer in a cross-layer design is defined by a few critical parameters and control knobs. Cellular network, also known as a mobile network, is a communication system in which the connectivity between end nodes is wireless. The network is divided into "cells," each of which is supplied by at least one fixed-location transceiver. In today's reality, cellular communication has proven to be extremely difficult. Cellular networks have been at the forefront of many breakthrough technologies up to now. The basic cross-layer and cross-layer architecture for cellular networks will be demonstrated. As cellular networks become more common, so does the amount of data that has to be transferred across them. As a result, the processor's workload increases. As a result, we must build a network that can reduce the disadvantages and assist us in establishing a stable and error-free infrastructure to avoid any stumbling blocks. We will go over all the challenges with cellular networks in detail in this paper, and then propose acceptable solutions.
Cross Layer Design and Optimization
In Cellular Networks
Akhil Reddy Thella
Department of Electrical and Computer Engineering
University of Ottawa, ON, Canada, K1N 6N5
Email- athel095@uottawa.ca
Abstract "Cross-layer design" refers to the sharing of
information between layers to make effective use of
network resources and achieve high adaptivity. Each layer
in a cross-layer design is defined by a few critical
parameters and control knobs.
Cellular network, also known as a mobile network,
is a communication system in which the connectivity
between end nodes is wireless. The network is divided into
"cells," each of which is supplied by at least one fixed-
location transceiver. In today's reality, cellular
communication has proven to be extremely difficult.
Cellular networks have been at the forefront of many
breakthrough technologies up to now. The basic cross-layer
and cross-layer architecture for cellular networks will be
demonstrated. As cellular networks become more common,
so does the amount of data that has to be transferred across
them. As a result, the processor's workload increases. As a
result, we must build a network that can reduce the
disadvantages and assist us in establishing a stable and
error-free infrastructure to avoid any stumbling blocks.
We will go over all the challenges with cellular
networks in detail in this paper, and then propose
acceptable solutions.
Keywords Mobile communication systems, Cellular networks,
Cross Layer design, Mobility prediction, Band width reservation
I. INTRODUCTION
With the introduction of phablet devices in recent
years, there has been a great increase in mobile data
consumption, which has been accompanied by increased
investments in network infrastructure development [1].
The rapid development of technologies to accommodate
the anticipated traffic rise, however, presents significant
revenue challenges for mobile network operators
(MNOs). If MNOs want to turn the exciting prospect of
such a vast market into revenue growth, they must work
on several fronts, including lowering operational costs,
raising average revenue per user for current customers,
and improving new customer acquisition, among other
things. In this research, several deterministic and
stochastic optimization strategies are investigated in
order to provide novel solutions to such problems.
Fig:1 Evolution of Cellular Networks
The capabilities of cellular networks have
advanced at a rate that has spurred both societal
transformation and innovation on a worldwide scale in the
40 years since the birth of mobile telephony. Because of
the capabilities of 3G and 4G networks, which were built
in response to demand for mobile internet, smartphones
and tablets have become commonplace. However, the
inevitable spread of the Internet of Things (IoT) and the
demands of new applications such as AR/AI and self-
driving cars have pushed 4G networks to their limits,
necessitating the development of 5G, the next generation
of cellular networking [2].
Unlike 3G and 4G networking, which were
primarily focused on voice and data services and are
mostly associated with smartphones, 5G promises to
connect billions of devices of nearly any kind. More than
just a quicker wireless connection, 5G has the
potential to completely alter consumer, commercial, and
industrial processes, enabling new levels of efficiency
and creativity and propelling the next wave of global
economic growth.
In this paper we discuss the importance of the cross-layer
design in cellular networks. We propose an efficient,
optimal, and operational solutions for addressing the
identified difficulties, in which important and finite
resources like as time, frequency, and energy are
allocated.
II. CELLULAR NETWORKS
Mobile connection has progressed from telemetry,
machine-to-machine, and internet of things applications
to telemetry, machine-to-machine, and internet of things
applications [3].
Fig:2 Optimized cellular network
The fast expansion of wireless communication
and access, combined with the growing demand for and
sophistication of wireless applications, necessitates
intelligent and dependable systems to handle the
interchange of vast classes of traffic with escalating
quality of service (QoS) standards.
Furthermore, because of the current third
generation universal mobile telecommunication systems
(UMTS) and the emerging fourth generation (4G) long
term evolution (LTE) technologies are expected to
coexist for years, cooperation among cellular systems that
incorporate different radio access technologies is
particularly important. To achieve optimal performance
in heterogeneous networks, radio resource management
(RRM) approaches across different network layers, as
well as spectrum sharing schemes, are critical.
.
Challenges for cellular network operators:
Three primary issues are currently posing challenges to
mobile network operators (MNOs).
Misalignment of traffic demand and network
capacity.
Non-uniform spatiotemporal traffic demand
A decrease in revenue because of significant
network resource investments
As a result, MNOs must increase the capacity of their
cellular networks to meet the expected traffic growth
trend. The most promising ways to meet the goals are to:
improve spectrum efficiency,
increase network density
extend the spectrum to higher frequency bands.
The first and second techniques are the focus of this
paper. However, these technologies have some
limitations that reduce the network's energy efficiency [4].
The later half of the paper provides more information
about cellular networks' energy efficiency.
Heterogeneous cellular networks [5]:
Heterogeneous cellular networks (HCNs) have lately
emerged as a viable solution for dealing with the ever-
increasing traffic demand in bandwidth-hungry cellular
networks. HCNs are often made up of a variety of radio
access technologies, architectures, and base stations (BSs)
with varying transmission strengths. In such networks, a
mix of BSs with varied cell sizes is used to greatly
increase overall network performance. The opportunistic
and dense deployment of BSs, on the other hand, raises
concerns about energy conservation in HCNs.
The operation power and number of BSs in each
tier are generally based on the maximum predicted traffic,
which is a key restriction of such networks.
However, ignoring traffic load fluctuations
across time and space, full power operation of BSs is
substantially higher than necessary, regardless of traffic
load. The variable average transmits powers and BS
density for each tier make analysing an HCN, which
consists of K tiers of randomly distributed BSs, a difficult
task. A deterministic grid model is only suited for
topologies with fixed-size cells and is based on Monte
Carlo simulation of the hexagonal or square regular lattice.
To solve these issues, HCNs are modelled using
stochastic geometry, which gives a tractable yet accurate
performance model for wireless networks.
Fig:3 Heterogeneous cellular networks
Design Challenges:
Because of a limitation of resources such as bandwidth,
processing storage, and energy, there are several major
design issues with Cellular Networks [6]. The following
fundamentals need to be met for better transmission of
data:
Energy Efficiency
Sensor Complexity Location
Data transmission models
Scalability
Strength
Delay
The three fundamental qualities of the future
communication generation are heterogeneity, complexity,
and dynamic nature. The Internet and communication
architecture were not created with these qualities in mind.
The complexity of wireless communication networks has
rapidly increased due to the advent of new technologies
such as the Internet of Things, multicore architectures,
and exponential social adaptation of these new
technologies [11].
This has also raised the bar for applications' QoS
to match the users' QoE. Wireless communication and the
Internet have become a bounded closed loop system with
multiple trade-offs and restrictions to avoid multiple
performance issues because of all these new technologies,
but these strict policies have now become a bottleneck in
designing next-generation communication systems [7].
Fig:4 Challenges in Cellular Networks
Multiple protocols are used in wireless networks
and the Internet to allow communication between
transmitter and receiver. These protocols have a restricted
field of view and ability to share information with the
entire network. When we need to integrate a new system,
we add a protocol to the software and maintain updating
it to fix difficulties that arise due to changing network
conditions, user QoE, and security concerns. Despite
implementing all these fixes, we continue to fall behind
in terms of customer satisfaction, application quality of
service, and security [9].
There are still so many design challenges such as
the signalling overhead, Optimization, Security, Routing
and Network management that play an important role in
implementing the cellular networks. Social requirements
and economic expansion are two primary driving forces
that have played a significant impact in the success of
wireless and internet networks [8].
III. ARCHITECTURE AND DESIGN OF CROSS LAYER
A layered architecture, such as the seven-layer open
systems interconnect (OSI) model, divides the total
networking task into layers and provides a hierarchy of
resources to be given by the various layers.
The layers' facilities are implemented by the
creation of protocols for each tier. The architecture
forbids direct connection between non-adjacent layers;
communication between adjacent layers is limited to
answers and procedure calls. The architect will have two
alternatives when creating a protocol in the context of a
reference layered architecture. The reference
architecture's rules can be used to design protocols. This
would entail developing protocols in a hierarchical
architecture such that a higher-layer protocol just uses the
services provided by the lower layers and is unconcerned
about how those services are supplied. There are multiple
layers to the cross-layer network [10].
The function of each layer is to deliver specific
services to the next higher layer, ensuring secrecy while
shielding upper layers from the specifics of how lower
layer services are used. By splitting down the network
into smaller components with diverse functionality, this
strategy decreases complexity and allows each duty to be
handled more efficiently and implicitly. It also makes
new protocol specifications easier to implement at many
levels of the protocol stack. This formal approach to
network design facilitates in standardisation and inter-
layer interoperability, as well as explaining the peer
relationship between different devices and networks [11].
Fig: 5 Cross Layer Architecture
For the following reasons, cross-layer design is
particularly interesting in wireless networks. First,
notwithstanding its success in wireline networks, the
traditional architectural design method results in a smaller
search space for optimal adaptation. In contrast to
wireline networks, where resources are plentiful, wireless
networks have a compelling need to explore a larger
optimization space, including numerous layers, to make
the most of limited resources.
Second, the current protocol stack is designed for
wireline networks. It may not be appropriate for wireless
networks that differ fundamentally in many ways. For
example, the concept of "connection" has evolved
significantly.
The distance between two nodes and their
transmit powers play a big role in their connectivity.
Because of the unique properties of wireless networks,
parameters formerly located in distinct tiers must be
considered together [13].
In general, cross-layer design entails formulating
and addressing optimization challenges. Although it may
seem enticing to include more parameters from more
levels, care should be taken when selecting parameters
and weighing the trade-off between performance
advantages and added complexity.
The following cross-layer concepts have been presented
in to avoid pitfalls [4]:
The Law of Unintended Consequences and
Interactions: Designers can work on a single
problem without having to worry about the rest
of the protocol stack with traditional architectural
design. In cross-layer design, this is no longer the
case. Unintended impacts on other elements of
the system should be avoided at all costs.
Graph of Dependency: Several adaption loops
that are part of separate protocols are frequently
caused by cross-layer design. A dependency
graph, in which each important parameter is a
node, and a directed edge illustrates the
dependency relationship between the parameters,
will be quite useful.
Separation of Timescales and Stability: The
concept of averaging and temporal separation can
be used to verify stability when a parameter is
regulated and used by two adaptation loops.
Proofs of stability are necessary for each loop in
the dependency network that consists of
interactions with similar timeframes.
Unbridled Cross-Layer Design Creates Chaos:
A cross-layer design's lifespan and upgrading
costs should be carefully weighed against the
performance advantages that can be achieved.
There are even different proposals for cross layer
design such as upward information flow, downward
information flow, Back and forth information flow and
merging of different layers.
Fig:6 Different Cross layer Design proposals [6]
Upward Information flow
A higher-layer protocol that requires any
information from the lower layer(s) at runtime generates
a new interface between the lower layer(s) and the higher
layer. Errors on the wireless link will cause the TCP
sender to make false inferences about network congestion
if the end-to-end TCP route includes a wireless
connection. This results in a decline in performance and
Creating interfaces between the lower levels and the
transport layer to allow explicit communications can
assist to avoid such issues [17].
Downward information flow
Some cross-layer architecture concepts rely on a
direct interface from a higher layer to set parameters on
the lower layer of the stack during runtime. The
downward information flow is designed to provide
guidance to lower layers on how to process application
data.
Back-and-forth information flow
At runtime, two layers performing different tasks
will work together. This usually takes the shape of an
iterative process between the two layers, with data
flowing back and forth. The design problem here is
clearly the two complementing new interfaces.
Merging Adjacent layers [17]
Another technique to implement cross-layer
architecture is to merge two or more neighbouring layers
so that the current superlayer's operation equals the sum
of the constituent levels' services. This eliminates the
need for any extra interfaces to be added to the stack.
From an architectural standpoint, the superlayer
can be interfaced with the remainder of the stack utilising
the interfaces that still exist in the original architecture.
IV. CROSS LAYER DESIGN FOR CELLULAR
NETWORKS
Wireless network infrastructure in cellular
networks refers to the entire wireless physical substrate
network, which comprises towers and antennas, base
stations such as microcells and relays, RF, baseband
processors, and radio resource controllers.
The compatibility of third-generation cellular
networks like the Universal Mobile Telecommunications
System (UMTS) and wireless local area networks
(WLANs) makes them ideal for integration. WLANs
deliver significantly higher data rates than UMTS
networks, which give users with excellent mobility and
always-on wide-area coverage at comparatively low data
rates [19].
Base stations support cellular networks, which
are made up of cells. The base station facilitates in the
broadcast of the message and the building of a network
bond since it is placed in the centre of a cell. When
communication between the two channels begins, the
mobile station (MS) is situated in one cell. MS leaves the
cell for a short duration. The base stations are connected
to the mobile switching centre (MTSC), which serves as
a conduit for all communications. The MSC's job is to
ensure that the user is who they say they are.
Using modules that are built for specific tasks, a
modular architecture enables for performance benefits
and intuitive device enhancements. Cross-layer design
(CLD) allows communication between nonadjacent
layers by using external entities placed into the system's
architecture [5].
A reference model does not specify the
functionality that each new entity (i.e., module) must do
in a cross-layer design solution. Wireless nodes in
wireless cellular networks such as the Universal Mobile
Telecommunications System and Long-Term
Evaluation periodically measure and improve wireless
links to facilitate radio resource management decisions
such as handover and opportunistic scheduling.
Finally, the MTSC is linked to (PSTN). The
diagram below depicts the layout of a cell.
Fig:7 Cross Layer Architecture for cellular Networks
Now we'll look at the benefits of using a cross-layer
strategy, in which physical layer information is
transmitted to upper layers. Supporting TCP traffic over
wireless networks is considered first, followed by data
services in a multi-user wireless network. Finally, based
on network throughput needs, we address deployment
tools and practical difficulties that face the deployment of
access points [23].
TCP OVER WIRELESS LINKS
Transmission Control Protocol is the most widely
used protocol for data transfer over the Internet (TCP).
TCP is an end-to-end data transfer protocol with a
connection-oriented architecture. Its goals are twofold:
o Internet congestion control
o Reliable end-to-end data transmission
achieved through error or loss detection and
retransmission.
Routers in the network indicate congestion by
dropping packets, causing the source to reduce its sending
rate adaptively [25].
The Explicit Congestion Notification (ECN)
mechanism, which notifies the receiver when the network
is congested, is likely to be included in future TCP
deployments. When a router senses congestion, it sets the
ECN bit to one, indicating that the packet has been
flagged. When the marked packet reaches its destination,
the destination tells the source of the mark's value.
Depending on the value of the mark, the source adjusts its
transmission rate.All losses are interpreted as congestion-
related in the current implementation of the TCP protocol.
When packets are lost over a wireless channel,
the TCP source reacts as if it were due to congestion,
lowering the packet transmission rate and reducing
network performance. To address this issue, one method
has been proposed: "smooth" the channel using
appropriate coding and link layer automated repeat
request (ARQ) at a faster timeframe than the TCP control
loop, so that the wireless link is viewed as a constant
channel with lesser capacity. The channel need not be
smoothed because the ECN mechanism provides a means
of explicitly indicating congestion [27].
Fig:8 TCP over wireless links
A crosslayer view of physical layer information
(channel conditions) is used at the network layer in this
strategy, which considerably improves network-layer
throughput performance.
A paradigm for determining how each new CLD
object may work has been proposed. Four separate planes
are introduced in this model, which span visually upright
over the OSI reference model's protocol levels. Each of
these so-called coordination levels encompasses the
behaviour of a CLD algorithm or protocol for resolving a
certain problem. In wireless mobile devices, these
comprise security, mobility, service quality (QoS), and
wireless connection adaption, resulting in four
coordination planes [29].
Coordination plane is a concept used to showcase
the issues that can be solved by cross layered designs and
its benefits. Each coordination plane represents an issue
that would be solved by the cross-layer design.
Below are descriptions of four different types of
coordination planes.
Security: In mobile communication systems, the
traditional TCP/IP protocol produces numerous levels of
encryption across different network layers, resulting in
higher processing and exorbitant costs.
Mobility: Cross-layer designs created to ensure
continuous and uninterrupted wireless connection in a
mobile environment.
Quality of Service: The coordination plane is
concerned with improving service quality by sharing
information amongst non-adjacent levels.
Wireless Link Adaptation: This coordination
plane uses cross-layer designs to reduce increased BER
and channel fading in wireless networks [31].
Fig: 9 Coordination planes in cross layer
Security:
With the rising reliance on technology in our
daily lives, security has become a critical concern. As
more and more valuable data is collected, exposing such
sensitive data poses a significant danger. As a result, a
situation has emerged in which numerous encryption
mechanisms are used on the network at various network
tiers.
Fig:10 Security Encrption plane
These multiple encryption techniques have increased
network security, but they have also resulted in a number
of issues, including increased transmission delay as data
must pass through each level of encryption, as well as
increased processing power and energy consumption due
to the increased encryption. At different network tiers,
several encryption techniques are available.
Encryption should not be done at numerous
network tiers to avoid excessive energy consumption and
transmission delays caused by excessive data encryption.
Instead, to offer the requisite encryption, one network
layer should be chosen.
Mobility:
Through handovers to appropriate cellular base
stations, wireless terminals can travel from one service
region to another. Horizontal handover, which involves
transferring a mobile device between access points that
use the same technology, and vertical handover, which
requires moving a mobile device between access points
that use different technologies, are the two forms of
handover. The top levels must reduce the consequences
of transfer in both scenarios, so that mobility-related
functions can facilitate the generation of transfer
notifications. This will enable for a smooth and,
preferably, seamless transfer of mobile applications to
new wireless technology [32].
The Mobility coordination plane would therefore be in
charge of adjusting the higher layer's services to the
wireless technologies that underpin them. Mobility may
be hampered by channel fading, transmission latency,
high bit rate of error, and other QoS flaws. TDMA and
FDMA are used to increase the number of people served.
CDMA/HDR is used in some crosslayer concepts to solve
waste time slots in wireless systems.
Quality of Service:
With the expanding use of real-time applications
in wireless networks, QoS is becoming more significant.
With a vast user base and limited network resources, the
apps are more demanding than ever. End-to-end service
quality should be considered for achieving QoS. When
numerous programmes are running at the same time, the
less demanding ones should consume the least amount of
resources to minimise a negative impact on a resource-
intensive application like real-time video streaming [34].
Due to a lack of communication between the next
network layers, QoS is handled in a limited manner.
TCP/RTP connections are handled by the application
layer, which is also in charge of establishing distinct QoS
connections.
Wireless link Adaption:
Few attributes, such as bit error rate (BER),
transmission delays, and so on, might have an impact on
the performance of the upper layers, leading to the
incorrect assumption that packets are being created by
congestion in the end-to-end link. In this situation, the
ARQ protocol at the data link layer tries to retransmit
dropped packets from both TCP and ARQ, which could
reduce congestion by half. As a result, coordination
between the two is required to avoid such degradations.
The above-mentioned management model specifies a co-
ordination manager who is responsible for the
fundamental coordination of CLD operations. CLD
introduces management entities that act as either a
performance optimizer or a scheduler, depending on the
task at hand [36].
V. CROSS LAYER DESIGN ISSUES IN CELLULAR NETWORKS
Various network factors, such as energy efficiency,
mobility management, security, cooperation, and QoS, in
future cellular networks require optimization, which can
be accomplished through cross-layer design. The primary
idea behind cross-layer design is to allow protocol
coordination, integration, and joint optimization in
addition to maintaining the functionality associated with
the original traditional layers. However, putting all of this
together is a difficult aspect of the cross-layer design.
Some of these issues are:
A) Cross-layer Optimization [38]:
Because the entire network architecture and
optimization is exceedingly sophisticated, it becomes
even more difficult when considering dynamic real-time
optimization. In dynamic optimization, data is exchanged
between network levels. The system designer must
carefully select the details to be conveyed. It shouldn't be
too difficult to introduce huge delays or computationally
expensive optimization routines. It must, however, be
overly simplistic for fear of transmitting too little
information. This is a major challenge that necessitates
the use of measures (BER, throughput, and routing
efficiency at the PHY, MAC, and Network layers,
respectively) for optimization.
Network control is another optimization-related
issue. It's critical that the process of altering functionality
across levels is governed by something. Otherwise, the
many adaptations can be used for multiple purposes. As a
result, the question of "who is in charge" arises. Although
the appropriate location for the control might be debated
for each layer, the truth remains that this is a substantial
research subject with multiple answers depending on the
end-user application or the physical operating
environment.
Fig: 11 Cross Layer Optimization for resource allocation
problem
B) Network Problems
Congestion happens when a significant number
of users access the same data on the same network [36].
As a result, the network becomes overwhelmed with
subscribers, leaving limited room for new users.
Furthermore, the time users must wait will increase. The
number of users served in centralised networks, such as
Wi-Fi and cellular networks, is limited due to a limited
number of channels, channel interference, and other
factors.
Intra- and inter-call traffic interferences may be
substantial enough to cause an interruption if the target bit
error rate of traffic interferences (BERIT) is not fulfilled.
The outage frequency should be as low as feasible, yet it
can vary depending on the class. This is referred to as a
Traffic management problem.
C) Scheduling [40]:
In recent years, this multi-user scheduling
problem has received a lot of attention in academia and
business. The distinctive characteristics of wireless
networks inspired these scheduling schemes: restricted
resources, mobile subscribers, network congestion, and
time-varying channel conditions such as fading and
mobility.
D)Fading:
Fading can limit the capacity of wireless
networks since the signal intensity scales down when
travelling between a base station and a mobile phone [41].
Slow and fast fading can be used to determine
the size and time cycle variance of a channel as the signal
changes. When the time interval between phases is
smaller than the time an application sends and receives
data, slow fading happens, and when the time interval
between phases is more than the time an application sends
and receives data, quick fading occurs.
Rayleigh fade is caused by multipath reception.
The handheld antenna receives a large number of
reflected and scattered waves, say N. Due to wave
cancellation effects, the instantaneous received power
observed by a travelling antenna becomes a random
variable dependent on the antenna's position. As the
signal travels across a wire, this method implies that its
scale changes with time. Some domain instruments have
the ability to alter the signal's course before it reaches its
destination [42].
Fig: 12 Multipath Fading Mechanism
Multipath fading is a mechanisimin which a
communication signal reaches its intended destination
after diverting from its original path due to a change in
light direction induced by reflection, refraction, or
dispersion. Radio waves can take a multitude of routes
from the mobile station to the base station. The impact of
a number of different pathways, which can be dangerous
and result in burst errors, is lessening. While the device
may be interference restricted, thermal noise may be the
limiting factor on efficiency at times.
E) Issues In QoS in the coordination plane [33]
Transmission Error: TCP packet loss occurs as a
result of the physical layer and link layer having a
negative influence. summarises the factors that lead to
transmission error, as well as a strategy for reducing such
errors utilising a cross-layer architecture called Explicit
Loss Notification (ELN).
ARQ in RTP: Automatic repeat requests (ARQ) in
wireless networks frequently generate jitter and delay,
which has a detrimental influence on overall QoS.
Network delays are extremely important in real-time
applications. There is a lack of information sharing
between the QoS and connection layers in the classic
protocol stack approach; nonetheless, information
sharing between these layers is critical to overcome the
issue. When evaluating the impact of retransmission on
overall QoS, it's sometimes better to discard packets
rather than retransmit them using ARQ techniques.
Transmission Power: New wireless networks use a lot
of energy to transmit data, and this is becoming a bigger
problem. To reduce power consumption, tradeoff
methods can be implemented. With an increase in
transmission delay, power consumption can be lowered,
however transmission delay cannot exceed a particular
threshold level that can be monitored using inference
levels. Reducing the bit rate can help lower energy usage,
but only to a point where the BER is not impacted.
VI. POSSIBLE SOLUTIONS FOR CROSS LAYER
DESIN ISSUES IN CELLULAR NETWORKS
Cross Layer congestion control [9]:
To predict the capability of the underlying cellular
connection, CQIC, a cross-layer congestion control
system, is proposed. CQIC senders employ these power
computations to alter their packet sending behaviour. The
receiver calculates the underlying connection potential
directly from physical layer information in CQIC and
sends the capacity as the approximate end-to-end
bandwidth to the CQIC sender. After collecting the
expected bandwidth, the sender adjusts its congestion
window .
A 'congestion-prevention' strategy, such as one of the
methods that is divided into two parts: When congestion
is present or imminent, the network must be able to alert
transport endpoints. Endpoints must also have a strategy
in place that minimises consumption when this signal is
received while raising usage when it is not. Since packet
loss is always due to congestion and a timeout is always
due to a missing packet, we have a solid contender for the
'network is congested' signal. This is especially true
because every existing network sends this signal out
automatically and without change.
Traffic control Management:
A network quality-of-service management system is
called call admission monitoring (QoS). Although CAC
is only a function of the number of physical channels
available in TDMA-based cellular networks, it is
inextricably tied to the physical layer output in WCDMA
networks since multi-access interference is also a
function of the number of physical channels available
[47].
There is a lot of overlap between the physical and
medium access control layers because of the CAC
architecture. Call admission control (CAC) and
bandwidth reserve procedures are required to tackle this
issue. Because forced call terminations due to handoff
blocking are frequently more disagreeable than new call
blocking, CAC uses the likelihood of handoff falling as
the key connection-level QoS indicator in wireless
cellular networks. Every network has a finite number of
resources, such as nodes, connections, and buffers, as
well as a limitless bandwidth. As a result, the number of
packets in a network at any given time is limited [49].
Opportunistic scheduling:
Good scheduling algorithms in wireless networks can
opportunistically try to modify channel circumstances to
increase network efficiency. Find an N-subscriber
wireless network with a single base station. Assume that
downlink communications, that is, communications from
the base station to the users, or receivers, are time slotted.
The base-station will then pick which user(s) to send to
base on the channel circumstances.
Transmissions to receivers with better channel
conditions allow the base station to use adaptive
modulation and coding methods to broadcast at a higher
rate for a given target bit-error rate. As a result, the base
station may be able to boost network capacity by taking
advantage of channel circumstances [18].
Integrated WLAN/CDMA systems [5]:
Tight coupling and loose coupling inter-working are
two different approaches to developing an integrated
WLAN/CDMA network architecture.
A WLAN is connected to a CDMA core network in the
same way that other CDMA radio access networks are in
a tightly coupled system. WLANs and CDMA networks
would share the same authentication, mobility, and billing
infrastructure in this manner, and the WLAN gateway
would need to support all CDMA protocols required by
the CDMA radio access network. The key benefit of this
method is that the CDMA core network's authentication,
mobility, and QoS mechanisms may be directly utilised
over the WLAN.
Fig: 12 Integrated WLAN and CDMA networks
In the loose coupling strategy, a WLAN is connected
to the Internet rather than CDMA network parts. WLANs
and CDMA networks handle authentication, mobility,
and billing using independent techniques and protocols,
and WLAN traffic would not pass via the CDMA core
network.
This integration requires the support of handoff
between these two networks, which ensures service
continuity and seamlessness. Handoff occurs when a
mobile user goes from one base station (or access point)
to another in a heterogeneous network environment, as
opposed to a homogeneous wireless access system, where
it occurs only when a mobile user moves from one base
station (or access point) to another. Horizontal handoff
refers to handoff inside a homogenous system, whereas
vertical handoff refers to handoff across distinct wireless
access protocols.
Fading:
An equaliser's ability to reduce signal fading will
benefit in the design of cellular networks by allowing the
output spectrum of a system to be assessed. It helps to
reduce interference caused by noise or other fading
effects. The equaliser is used to compensate for the
influence of a signal [48].
VII. CHALLENGES AND ADAPTATION PARAMETERS IN
CROSS LAYER DESIGN
Many researchers have proposed a cross-layer design to
solve the inherent problems associated with the TCP stack
protocol, but there are numerous challenges associated
with the cross-layer design that must be addressed for
cross-layer design concepts to gain widespread
acceptance in the industry.
Adaptation strategy in cross layer design:
In a wireless network, top down, bottom-up,
integrated, and MACcentric techniques are all viable
options for implementing cross-layer architecture. Each
strategy has advantages and disadvantages for data
transmission in a wireless network, and the optimum
approach is determined by the application's complexity
and protocol requirements. Before deciding on the best
strategy to cross-layer design adaptation, all of these
factors should be considered [24].
Challenges in Cross Layer Design:
Coexistence of Cross layer design:
Different cross-layer designs should work
together for cross-layer design systems to gain
widespread acceptance. The fact that different cross layer
designs have distinct communication protocols makes
integrating cross layer systems extremely complex. In
their cross-layer designs, different wireless technologies
use distinct communication standards.
Signalling in Cross Layer design:
Cross-layer signalling is the exchange of cross-
layer information between nodes in a wireless network.
Signalling has control over how information is sent across
a wireless network, including the format in which the
information is exchanged. For cross-layer architectures,
signalling is still an outstanding topic that needs to be
addressed.
Universal Cross Layer Design:
A cross-layer design that was created for one
application cannot be used for another. There is a need for
a universal cross-layer design that can adapt to varied
application requirements dynamically [51].
VIII. CONCLUSION
Cross-layer architecture solutions have made a
substantial contribution to cellular network efficiency.
The transmission control protocol's quality of operation
and efficiency have also been improved.
Because of the structure of cross-layer
architecture, which does not impose any constraints on
passing information between layers, it will become more
prevalent in cellular networks. If these networks become
live, the infrastructure will undergo substantial changes,
as the data rate will increase and the time it takes to send
a message will drop.
Furthermore, the cost of wireless networks would
drop, allowing more people to take advantage of the
infrastructure.
Cross-layer designs allow non-adjacent layer
communication to improve network performance even
more. These cross-layer designs can help increase
network mobility, security, wireless link adaption, and
overall quality of service (QoS). Cross-layer designs, on
the other hand, have their own set of obstacles and limits
that should be considered before they are implemented.
IX. ACKNOWLEDGMENT
I would like to thank Professor Zhaowei Ma for his
guidance in selecting the topic and helping in giving some
reference papers that helped me a lot in writing up this
paper. I would also thank for giving me the flexibility to
select a topic based on our interest.
REFERENCES
1. G. Carneiro, J. Ruela and M. Ricardo, "Cross layer
design in 4G wireless terminals," in IEEE Wireless
Communications, vol. 11, no. 2, pp. 7- 13, Apr 2004.
2. L. Ma, F. Yu, V.C.M. Leung, and T. Randhawa, “A
New Method to Support UMTS/WLAN Vertical
Handover Using SCTP,” IEEE Wireless Comm., vol.
11, no. 4, pp. 44-51, Aug. 2004.
3. F. Yu and V.C.M. Leung, “Mobility-Based Predictive
Call Admission Control and Bandwidth Reservation
in Wireless Cellular Networks,” in Proc. IEEE
INFOCOM’01, Anchorage, Alaska, April 2001.
4. F. Yu and V. Krishnamurthy, “Optimal Joint Session
Admission Control in Integrated WLAN and CDMA
Cellular Networks with Vertical Handoff,” IEEE
Trans. Mobile
Computing, vol. 6, no. 1, pp. 126-139, Jan. 2007
5. S. Bu, F. Richard Yu, Y. Cai, and P. Liu, “When the
Smart Grid Meets Energy-Efficient Communications:
Green Wireless Cellular Networks Powered by the
Smart Grid,” IEEE Trans. Wireless Comm., vol. 11,
no. 8, pp. 3014-3024, Aug. 2012.
6. F. Yu, V. Krishnamurthy, and V.C.M. Leung, “Cross-
Layer Optimal Connection Admission Control for
Variable Bit Rate Multimedia Traffic in Packet
Wireless CDMA Networks,” IEEE Trans. Signal
Processing, vol. 54, no. 2, pp. 542-555, Feb. 2006.
7. B. Fu, Y. Xiao, H. J. Deng and H. Zeng, “A Survey of
Cross-Layer Designs in Wireless Networks,” IEEE
Communications Surveys & Tutorials, vol. 16, no. 1,
pp. 110-126, First Quarter 2014.
8. J. Zuo, C. Dong, S. X. Ng, L. L. Yang and L. Hanzo,
“Cross-Layer Aided Energy-Efficient Routing Design
for Ad Hoc Networks,” IEEE Communications
Surveys & Tutorials, vol. 17, no. 3, pp. 1214-1238,
thirdquarter 2015
9. Luo , F. Richard Yu, H. Ji, and V.C.M. Leung, “Cross-
layer Design for TCP Performance Improvement in
Cognitive Radio Networks,” IEEE Trans. Vehicular
Technology, vol. 59, no. 5, pp. 2485-2495, June2010.
10. M. Zhao, Z. Chen, and Z. Ge, “QS-Sift: QoS and
spatial correlation- based medium access control in
wireless sensor networks, International J. Sensor
Networks, Vol. 2, Nos.3/4, pp. 228 - 234, 2007.
11. Markoč, Angelo Gary, and Gordan Šišul."Quality of
service in mobile networks." ELMAR, 2013 55th
International Symposium. IEEE, 2013.
12. Q. Liu, S. Zhou, and G. B. Giannakis, “Cross-Layer
Combining of Adaptive Modulation and Coding With
Truncated ARQ Over Wireless Links,” IEEE Trans.
Wireless Commun., Vol. 3, No. 5, September 2004.
13. Zaghloul, A. I., & Kilic, O. (2002). Transmission
Impairment Parameters in Multiple-Beam Satellite
Communications Systems. URSI General Assembly.
The Netherlands: Maastricht.
14. S.Shakkottai, T.S. Rappaport, and P.C.
Karlsson,“Cross-Layer Design for Wireless
Networks,” IEEE Commun. Mag., vol. 41, no. 10, Oct.
2003.
15. H. Balakrishnan, and R. H. Katz, “Explicit Loss
Notification and Wireless Web Performance,” Proc.
GLOBECOM Internet Mini-Conference, November
1998.
16. I. Haratcherev et al., Optimized VideoStreaming
Over 802.11 by Cross-Layer Signaling,” IEEE
Commun. Mag. Special Issue on Cross-layer Protocol
Engineering for Wireless Mobile Networks, vol. 44,
no. 1, Jan. 2006.
17. J. Mehlman, “Cross-Layer Design: A Case for
Standardization.”Available:http://www.jeffrey
mehlman.com/EE359 Research Project Final
JAM.pdf,2004.
18. Christos Bouras, Apostolos Gkamas and Georgios
Kioumourtzis “Challenges in CrossLayer Design for
Multimedia Transmission over Wireless Networks” A
European research key challenge, Global
Communications Newsletter,2002.
19. C. Luo, F. Richard Yu, H. Ji, and V.C.M. Leung,
“Cross-layer Design for TCP Performance
Improvement in Cognitive Radio Networks,” IEEE
Trans. Vehicular Technology, vol. 59,1996.
20. S. Khan et al., “Application-Driven CrossLayer
Optimization for Video Streaming Over Wireless
Networks,” IEEE Commun. Mag., Vol. 44, No. 1, pp.
122-130, 2006.
21. ] D. Zhai, M. Sheng, X. Wang, and Y. Li, “Leakage-
aware dynamic resource allocation in hybrid energy
powered cellular networks,” IEEE Trans. Commun.,
vol. 63, no. 11, pp. 45914603, Nov. 2015.
22. Y. Mao, J. Zhang, and K. B. Letaief, “A Lyapunov
optimization approach for green cellular networks
with hybrid energy supplies,” IEEE J. Sel. Areas
Commun., vol. 33, no. 12, pp. 24632477, Dec. 2015
23. Y. Wei, F. R. Yu, M. Song, and Z. Han, User
scheduling and resource allocation in HetNets with
hybrid energy supply: An actor-critic reinforcement
learning approach,” IEEE Trans. Wireless Commun.,
vol. 17, no. 1, pp. 680692, Jan. 2018.
24. F. Guo, H. Zhang, X. Li, H. Ji, and V. C. M. Leung,
“Joint optimization of caching and association in
energy-harvesting-powered small-cell networks,”
IEEE Trans. Veh. Technol., vol. 67, no. 7, pp. 6469
6480, Jul. 2018.
25. W. Lee, L. Xiang, R. Schober, and V. W. S. Wong,
“Direct electricity trading in smart grid: A coalitional
game analysis,” IEEE J. Sel. Areas Commun., vol. 32,
no. 7, pp. 13981411, Jul. 2014.
26. G. Wang, V. Kekatos, A. J. Conejo, and G. B.
Giannakis, “Ergodic energy management leveraging
resource variability in distribution grids,” IEEE Trans.
Power Syst., vol. 31, no. 6, pp. 47654775, Nov. 2016.
27. S. Bu, F. R. Yu, Y. Cai, and X. P. Liu, “When the
smart grid meets energy-efficient communications:
Green wireless cellular networks powered by the
smart grid,” IEEE Trans. Wireless Commun., vol. 11,
no. 8, pp. 30143024, Aug. 2012.
28. J. Xu and R. Zhang, “CoMP meets smart grid: A new
communication and energy cooperation paradigm,”
IEEE Trans. Veh. Technol., vol. 64, no. 6, pp. 2476
2488, Jun. 2015.
29. M. J. Farooq, H. Ghazzai, A. Kadri, H. Elsawy, and
M.-S. Alouini, “A hybrid energy sharing framework
for green cellular networks,” IEEE Trans. Commun.,
vol. 65, no. 2, pp. 918934, Feb. 2017
30. L. Chen, S. H. Low, M. Chiang, and J. C. Doyle,
“Cross-layer congestion control, routing and
scheduling design in ad hoc wireless networks,” in
Proc. IEEE INFOCOM, Barcelona, Spain, Apr. 2006.
31. T. Cover and J. Thomas, Elements of Information
Theory. Hoboken, NJ: Wiley-Interscience, 1991.
32. A. Eryilmaz and R. Srikant, “Joint congestion control,
routing, and MAC for stability and fairness in wireless
networks,” IEEE J. Sel. Areas Commun., vol. 24, no.
8, pp. 15141524, Aug. 2006.
33. N. Gatsis, A. Ribeiro, and G. B. Giannakis, “A class
of convergent algorithms for resource allocation in
wireless fading networks,” IEEE Trans. Wireless
Commun., vol. 9, no. 5, pp. 18081823, May 2010.
34. L. Georgiadis, M. Neely, and L. Tassiulas, “Resource
allocation and crosslayer control in wireless networks,”
Found. Trends Netw., vol. 1, no. 1, pp. 1144, Apr.
2006.
35. A. Giannoulis, K. P. Tsoukatos, and L. Tassiulas,
“Lightweight cross-layer control algorithms for
fairness and energy efficiency in CDMA ad hoc
networks,” in Proc. Int. Symp. Model. Optim. Mobile,
Ad hoc, Wireless Netw., Boston, MA, Apr. 2006.
36. L. Huang and M. J. Neely, Delay Reduction via
Lagrange Multipliers in Stochastic Network
Optimization, Ithaca, NY, 2009.
37. Z. Jiang, Y. Ge, and Y. Li, “Max-utility wireless
resource management for best effort traffic,” IEEE
Trans. Wireless Commun., vol. 4, no. 1, pp. 100 111,
Jan. 2005.
38. [18] P. Liu, R. A. Berry, and M. L. Honig, “A fluid
analysis of a utilitybased wireless scheduling policy,”
IEEE Tran. Inf. Theory, vol. 52, no. 7, pp. 28722889,
Jul. 2006.
39. L. M. Lopez-Ramos, A. G. Marques, J. Ramos, and
A. Caamano, “Crosslayer resource allocation for
downlink access using instantaneous fading and queue
length information,” in Proc. IEEE GLOBECOM
Workshops, Miami, FL, Dec. 610, 2010, pp. 1212
1216.
40. D. J. Love, R. W. Heath, V. K. Lau, D. Gesbert, B.
Rao, and M. Andrews, “An overview of limited
feedback in wireless communication systems,” IEEE
J. Sel. Areas Commun., vol. 26, no. 8, pp. 13411365,
Oct. 2008.
41. S. H. Low, “A duality model of TCP and queue
management algorithms,” IEEE/ACM Trans. Netw.,
vol. 11, no. 4, pp. 525536, Aug. 2003.
42. A. G. Marques, G. B. Giannakis, F. F. Digham, and F.
J. Ramos, “Powerefficient wireless OFDMA using
limited-rate feedback,” IEEE Trans. Wireless
Commun., vol. 7, no. 2, pp. 685696, Feb. 2008.
43. A. G. Marques, G. B. Giannakis, and J. Ramos,
“Optimizing orthogonal multiple access based on
quantized channel-state information,” IEEE Trans.
Signal Process., vol. 59, no. 10, pp. 50235038, Oct.
2011.
44. A. G. Marques, G. B. Giannakis, and J. Ramos,
“Stochastic cross-layer resource allocation for
wireless networks using orthogonal access:
Optimality and delay analysis,” in Proc. IEEE Int.
Conf. Acoust., Speech, Signal Process., Dallas, TX,
Mar. 1419, 2010, pp. 31543157.
45. A. G. Marques, X. Wang, and G. B. Giannakis,
“Dynamic resource management for cognitive radios
using limited-rate feedback,” IEEE Trans. Signal
Process., vol. 57, no. 9, pp. 36513666, Sep. 2009.
46. A. Nedic and A. Ozdaglar, “Approximate primal
solutions and rate analysis for dual subgradient
methods,” SIAM J. Optim., vol. 19, no. 4, pp. 1757
1780, Feb. 2009.
47. M. J. Neely, E. Modiano, and C. E. Rohrs, “Dynamic
power allocation and routing for time-varying wireless
networks,” IEEE J. Sel. Areas Commun., vol. 23, no.
1, pp. 89103, Jan. 2005.
48. A. Ribeiro, “Ergodic stochastic optimization
algorithms for wireless communication and
networking,” IEEE Trans. Signal Process., vol. 58, no.
12, pp. 63696386, Dec. 2010.
49. A. Ribeiro and G. B. Giannakis, “Optimal FDMA over
wireless fading ad hoc networks,” in Proc. IEEE Int.
Conf. Acoust., Speech, Signal Process., Las Vegas,
NV, Mar./Apr. 2008, pp. 27652768.
50. P. Soldati, B. Johansson, and M. Johansson,
“Proportionally fair allocation of end-to-end
bandwidth in STDMA wireless networks,” in Proc.
7th ACM Int. Symp. Mobile Ad Hoc Netw. Comput.,
Florence, Italy, May 2006, pp. 286297.
51. A. Stolyar, “Maximizing queuing network utility
subject to stability: Greedy primal–dual algorithm,”
Queueing Syst., vol. 50, no. 4, pp. 401 457, Aug.
2005
52. L. Tassiulas and A. Ephremides, “Stability properties
of constrained queuing systems and scheduling
policies for maximum throughput in multihop radio
networks,” IEEE Trans. Autom. Control, vol. 37, no.
12, pp. 1936 1948, Dec. 1992.
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