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CHAPTER 1

INTRODUCTION

Wireless LAN (WLAN) provides flexible data communication

systems with the features and benefits of traditional LAN technologies, such

as Ethernet and Token Ring without the limitations of wires or cables. Here,

the connectivity no longer implies physical attachment and the wireless

devices are not restricted by physical connections or to fixed locations. The

infrastructure of WLANs is dynamic and mobile with the freedom and

flexibility, which can be applied to mobile devices as well as to devices

within buildings or between buildings. Also, it combines data connectivity

with user mobility. In general, it improves productivity, convenience and cost

advantage over traditional wired networks. Practically, WLANs provide the

final few meters of connectivity between the wired network and the mobile

user.

1.1 FUNDAMENTALS OF WLAN

Wireless LAN is a transmission system that uses radio waves as a

carrier for the propagation of data. It is also referred to as a wireless network

in its primary form. It consists of three fundamental components namely, (i)

Wireless Hosts (ii) Base Station / Access Point (AP) and (iii) Wireless link.

The workstations with wireless Network Interface Cards (NICs) are

connected to the base stations or to other workstations by using either Infrared

light (IR) or Radio frequencies (RF). RF provides longer range, higher

bandwidth and wider coverage. Most of the WLANs use the 2.4GHz

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frequency band, which is reserved for unlicensed devices. Wireless devices

are often referred to as wireless clients. The base station is also called as

Access Point. IEEE 802.11 standard ensures the successful communication

among these three components and Table 1.1 describes the characteristics of

the same.

1.1.1 WLAN Architecture

IEEE 802.11 standard permits devices to establish either peer to

peer networks or networks based on fixed Access Points with which, the

mobile nodes can communicate. Hence, the standard defines two basic

network architectures, namely, the Infrastructure network and the Ad-hoc

network.

Table 1.1 Description of IEEE 802.11 Standards

Characteristics Description

Frequency Bands 2.4 GHz or 5 GHz

Maximum transmission rates

11Mbps (802.11b), 54 Mbps (802.11a/g)

Range From 10 meters to 100 meters

Physical Layer Direct Sequence Spread Spectrum (DSSS), Frequency Hopping Spread Spectrum (FHSS), Orthogonal Frequency Division Multiplexing (OFDM)

Advantages No cables, ease of installation, flexibility, mobility and competitively priced

Disadvantages Unlicensed spectrum – hence more interference, error rates and security attenuation

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Figure 1.1 Sketch of Ad-hoc and Infra structured network

In Infrastructure network, shown in Figure 1.1, wireless hosts

communicate with a base station which allows the broadcasting, forwarding,

coordination, synchronization and bridging of packets. The base station /

Access Point is the wireless hub. It acts as the gateway for wired network to

wireless network. It is the policy manager and is situated as a part of the wired

network. All communications between stations (STAs) or between a STA and

a wired network client go through AP. The area covered by AP is technically

referred to as Basic Service Set (BSS). A Service Set Identifier (SSID)

identifies every BSS and it is the identification given to devices within a

specific cell to enable wireless communication. A SSID need not be unique.

Hence, a Basic Service Set Identifier (BSSID) is needed to identify an AP and

is usually the Media Access Control (MAC) address of the AP.

Ad hoc network, also called as Independent Basic Service Set

(IBSS), allows a group of IEEE 802.11 wireless stations to communicate with

each other, under peer-to-peer mode without an AP. It is created

spontaneously. It does not extend support for accessing wired networks. This

type of architecture is better suited for conference room set ups. In this thesis,

the focus is on Infrastructure network, due to its predominant use.

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Table 1.2 Certified Standards of Wi-Fi

Standard Data Rate

Frequency Modulation Scheme

Range Security Certified Year

802.11 1 or 2 Mbps

2.4 GHz FHSS / DSSS

< 25m WEP/WPA 1997

802.11a Up to 54 Mbps

5GHz OFDM < 20m WEP/WPA 1999

802.11b Up to 11 Mbps

2.4 GHz DSSS <100m WEP/WPA 1999

802.11g Up to 54 Mbps

2.4 GHz DSSS / OFDM

<100m WEP/WPA, WPA-PSK

2003

1.1.2 WLAN Standards

Wireless LANs coexist with fixed infrastructure networks to

provide mobility and flexibility to users by freeing them from the constraints

of physical wires. A number of wireless data communication systems have

been developed to utilize the 2.4 GHz Industrial, Scientific & Medical (ISM)

and 5 GHz Unlicensed-National Information Infrastructure (U-NII) band. The

first IEEE 802.11 standard was created as a method of extending the IEEE

802.3 (Wired Ethernet) to venture into the wireless domain. The IEEE 802.11

standard is also referred to as Wi-Fi and the Wi-Fi alliance (an independent

organization) provides Wi-Fi certification to products that conform to the

IEEE 802.11 standard.

IEEE 802.11 has been expanded considerably to include a family of

WLAN standards. 802.11a standard operates in 5 GHz band and uses a 52-

subcarrier OFDM with a maximum raw data rate of 54 Mbps. 802.11b has a

maximum raw data rate of 11 Mbps and uses DSSS. IEEE 802.11g works in

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2.4 GHz band, but operates at a maximum raw data rate of 54 Mbps using

OFDM. It offers backward compatibility with 802.11b.

Table 1.3 Unapproved or Under Development IEEE 802.11x Standards

Standard Description

IEEE 802.11d International roaming extensions

IEEE 802.11e Enhancements: QoS including packet bursting

IEEE 802.11f Inter-Access Point Protocol (IAPP)

IEEE 802.11h 5 GHz Spectrum, Dynamic channel / frequency selection (DCS / DFS) and Transmit Power Control for European compatibility

IEEE 802.11i Enhanced Security (ratified on 24 June 2004)

IEEE 802.11j Extensions for Japan

IEEE 802.11k Radio resource measurements

IEEE 802.11l Reserved

IEEE 802.11m Maintenance of the standard: odds and evens

IEEE 802.11n Higher throughput improvements

IEEE 802.11o Reserved

IEEE 802.11p WAVE – Wireless Access for Vehicular Environments

IEEE 802.11q Reserved

IEEE 802.11r Fast roaming

IEEE 802.11s Wireless mesh networking

IEEE 802.11T Wireless performance Prediction (WPP)- test methods and metrics

IEEE 802.11u Inter working with non 802 networks

IEEE 802.11v Wireless network management

IEEE 802.11w Protected management frames

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IEEE 802.11e is a proposed enhancement to 802.11a and 802.11b,

to offer enhanced MAC layer - Quality of Service (QoS) features that include

prioritization of data and voice and video transmission. It enhances the

Distribution Coordination Function (DCF) and the Point Coordination

Function (PCF), through a new coordination function named as Hybrid

Coordination Function (HCF). 802.11n is a standard proposed for throughput

enhancements. Under 802.11n, the raw data rate is estimated to reach a

maximum of 540 Mbps through the use of Multiple Input and Multiple Output

(MIMO), signal processing and smart antenna techniques for transmitting

multiple data streams through multiple antennas. The certified 802.11

standards are shown in Table 1.2 and the unapproved standards are shown in

Table 1.3.

1.1.3 Challenges and Constraints

The following are the challenges and constraints present in the establishment of WLAN.

Frequency allocation

Wireless networks require all the users to operate on a common frequency band and the frequency bands for particular users are allocated and licensed in each country.

Reliability of communication channel

It is measured in average Bit Error Rate (BER). Packet loss

rates for the packetized voice cannot exceed the order of 10-2

and BER of 10-5 is acceptable for uncoded data. Automatic

Repeat Request (ARQ) and Forward Error Correction (FEC)

are used to increase reliability.

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Security

Since the transmission medium is open to anyone within the geographical range of the transmitter, it is more difficult to secure the wireless network. Hence, data privacy is accomplished by using encryption and authentication at increased cost and decreased performance.

Interference

It is mainly caused by simultaneous transmissions and multipath fading. Collisions cause typically due to the result of multiple stations waiting for the channel and start their simultaneous transmissions. Moreover, it is also caused by hidden terminal problem.

Throughput

WLANs are currently targeted at data rates between 1-40 Mbps. The physical limitations and bandwidth do not allow the capacity of WLANs to approach that of wired LANs.

Power consumption

Wireless devices are meant to be portable, mobile and moreover typically battery powered. Hence, incorporation of energy efficient design procedures like sleep mode, idle mode, power down modes of operations and low power display etc., become mandatory. Timing beacons also play an important role in power management.

Mobility

Though freedom of mobility is the primary advantage of WLANs, system designs must accommodate handoff

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between transmission boundaries and route traffic to mobile users.

Human safety

On going research has to confirm whether RF transmissions

from radio and cellular devices are linked to human illness

and vision impairment due to the optical transmitters of Infra

Red based WLAN systems.

1.2 NEED FOR QUALITY OF SERVICE IN WLAN

QoS is the ability to treat packets differently as they transit a

network device, based on the packet contents. It is also the ability to provide

priority assignment to different applications and users to guarantee a certain

level of performance to the data flow. The quality parameters include data

rate, delay, jitter, BER and packet dropping probability etc.. Without QoS, all

packets on the network compete for the same pool of resources, resulting

congestion in the network. Since the network capacity is insufficient, QoS

need to be guaranteed, especially for real time multimedia applications.

However, the Best-effort network service does not support QoS. The

following points indicate the necessity of Quality Assurance (QA) in the

network.

Traffic load and application requirements grow always faster

than the estimate of any good network designer. Hence, QA

schemes are required to feed the timely need.

A clear policy of priority to guarantee the better utilization

of network resources becomes mandatory, mainly because

the random distribution of bandwidth yields risky results.

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Service prioritization is fundamental. After classifying

flows, it is required to control, prioritize and model the

network traffic so that, the critical servers and applications

will receive a guaranteed quantity of available bandwidth.

The protocols like UDP are not designed with self control

and congestion avoidance procedures. Since the real time

multimedia traffic run on UDP need to be incorporated with

QA schemes, to exercise the strict control and observation.

The propagation and growth of viruses steal the useful

bandwidth of the network. To quarantine those flows, QoS

schemes are to be applied beside other technologies.

Hence, the incorporation of QoS mechanisms in the network to

reserve the required resources, to provide service differentiation, and to avoid

congestion in the network becomes mandatory.

1.3 LITERATURE SURVEY

Providing Quality of Service guarantees for real time traffic in

wireless LAN is the primary objective of this dissertation. The quality

provisioning issues of intra networking, internetworking and the quality

assurance issues related with real time data transfer are addressed to a limited

extent. With respect to these issues, the relative literature survey on wireless

channel models, channel access schemes, congestion control, routing

mechanisms, Voice over IP (VoIP) and video streaming is carried out and

presented here.

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1.3.1 Wireless Channel models

Communication channel dictates the performance of any

communication system. Design of wireless networks requires an accurate

characterization of the radio channel. Wireless channels due to their unreliable

behavior differ a lot from the wired channels. The received signal strength

exhibits random fluctuations in wireless environment due to its time varying

nature (Aguiar et al 2003). Wireless channel is an inherently shared medium

leading to multi-user interference. The random and shared nature makes

communication over wireless channels a difficult task (Diggavi 2006).

Received Signal-to-Noise Ratio (SNR) is used to measure channel

quality in time varying wireless channels. They are distinguished by the

propagation environment such as urban, suburban, indoor, underwater and

orbital environments. WLAN primarily operates in an indoor environment

having tremendous amount of impairment and variability. Indoor channels are

heavily dependent on the placement of walls and partitions that dictate the

signal path within the building (Anderson et al 1995). The characteristics of

an indoor radio channel vary between different environments and must be

considered at the time of modeling the radio channel.

Figure 1.2 Basic propagation mechanisms – reflection (R), Diffraction

(D) and Scattering (S)

Source Obstacle

S

R

D

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When a WLAN RF signal radiates through its environment, it

bounces off obstructions like walls, floors and other reflective surfaces.

Figure 1.2 shows basic radio wave propagation mechanisms of reflection,

diffraction and scattering. These give rise to additional radio propagation

paths beyond the direct optical Line of Sight (LoS) between the radio

transmitter and receiver. As a result, multiple signal paths arrive at the

receiver. The characteristics of these multiple paths are variable and fairly

complex. To have a standard way to simulate them, RF channel models are

used. The channel models attempt to generalize the complexities and establish

an average behavior for the channel in an indoor environment. The efficiency

of a model is measured by the computational complexity and its accuracy is

measured by the estimation error (Hassan Ali et al 2002).

Figure 1.3 An overview of wireless channel models

Complex propagation environments present the biggest obstacle to

computational efficiency. Accuracy of a model depends on the accuracy of

the locations, size of buildings and other objects present in the environment.

There are three approaches to modeling of indoor radio channels, namely,

deterministic, statistical and site-specific modeling and are shown in Figure

1.3.

Wireless channel models

Deterministic Model

Statistical Model

Site-Specific Model

- Uniform Theory of Diffraction Model - Par-Flow Model

- Saleh Valenzuela - Log Distance Path Loss Model - WSSUS - GSCM

- Ray Tracing Model - FDTD

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1.3.1.1 Deterministic Channel Models

Deterministic or theoretical approach is based on the principles of

physics and provides an accurate knowledge of channel behavior necessary

for multimedia transmission (Combeau et al 2004). They require exact data

about the terrain leading to a huge database of environmental characteristics.

The consideration of huge amount of terrain data makes these models highly

accurate, even when applied to different environments. Though these models

do not require any measurements to be made, measurements are used to check

the accuracy. Algorithms for deterministic modeling are highly complex and

lack in computational efficiency. This resulted in restricting them to modeling

smaller areas of micro cell or indoor environments. Uniform Theory of

Diffraction (UTD) and Frequency Domain ParFlow (FDPF) constitute

deterministic propagation models.

A. Uniform Theory of Diffraction Model

UTD is a high frequency method for solving electromagnetic

scattering problems. According to UTD (Kouyoumjian et al 1974), a high

frequency electromagnetic wave incident on an edge in a curved surface gives

rise to a reflected wave, an edge diffracted wave and an edge excited wave

which propagates along a surface ray. The pertinent rays and boundaries are

projected onto a plane perpendicular to the edge at the point of diffraction.

Diffraction coefficients are determined for a perfectly conducting wedge

illuminated by plane, cylindrical, conical and spherical waves. The results are

extended to the curved wedge.

Ray tube method (Hae won son et al 1999) is based on UTD and it

overcomes some of the limitations in ray tracing methods. It is a point-to-

point technique that guarantees high accuracy and is applicable to any

complex environment. Three types of ray tubes namely, the transmitter,

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reflection and diffraction ray tubes are defined on the plane view of quasi 3D

environment. The ray tubes are shown in Figure 1.4. The transmitter ray tube

represents bundle of rays from a transmitter and is described by the position

of the transmitter and the tube angle of 2π radian. The reflection ray tube

represents bundle of rays reflected by wall. It is described by the position of

the image on the wall, the wall number and the tube angle are less than π

radian. The diffraction ray tube consists of family of rays diffracted by corner

and is described by the position of the corner, the corner number and the tube

angle.

Parametric formulation of UTD (Huihui wang et al 2005) enables

faster and more accurate evaluation of diffracted field in propagation

prediction models for indoor environments. It finds potential use in real-time

propagation computations. Earlier models neglected diffracted rays for the

purpose of simplicity, leading to poor estimation of diffracted field in the

shadow region, which is particularly prominent in indoor environments. By

using inverse problem theory, a better approximation of the diffraction

coefficient for a dielectric wedge is determined, leading to accurate

estimation.

B. ParFlow Model

The ParFlow model is based on the Lattice-Boltzmann method

(LBM), which is developed for gas-kinetic representation of fluid flow and

can be used for modeling electromagnetic wave propagation. The ParFlow

algorithm describes a system going from the excited state to the equilibrium

state, using a regular structure of data to allow parallel computation of the

diffusion process. It is based on the concept of partial flows used for the

discretization of the Maxwell's equations and applied either to the electrical

field, magnetic field or both (Gorce et al 2001). The indoor environment is

described by a 2D grid. It offers better accuracy when spatial resolution is

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high. Higher resolution increases the grid size and the computation time. It

can be used in time domain and frequency domain.

Figure 1.4 Types of ray tubes

Time Domain ParFlow (TDPF) is used as a discrete simulation

method for propagation analysis in indoor and hybrid indoor-outdoor

environments. The time average of the amplitude of the electric field is

computed in each point and is based on the direct discretization of the

Huygens principle. It exploits a regular grid to propagate the free-space field

along wires. The field at each pixel is divided into four components as shown

in Figure 1.5, represents directive flows along wires. Space and time are

discretized in terms of finite, elementary units and are related with the speed

of light and the space dimension (De Sousa et al 2005). Time domain

technique is accurate, when real-time and near-field measurements are not

required.

Frequency Domain ParFlow (FDPF) is a method used to solve discrete

ParFlow equations. In a narrow band system, a linear inverse problem in the

frequency domain is used to compute the steady state. A Multi Resolution

formulation in frequency domain (MR-FDPF) is used to simulate indoor radio

wave propagation. It is based on the fact that all reflections and diffractions

Reflection ray tube

Diffraction ray tube

Transmitter

Pd1

Pd2

Pr2 Pr1

Transmitter ray tube

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are taken into account with no impact on the computational load (Dela Roche

et al 2006). It involves two stages: a preprocessing stage where binary tree is

built and scattering matrix is computed for each node, followed by a

propagation stage where radio coverage map is computed for each source.

This method is efficient in improving the computation time, when multiple

coverage maps corresponding to different sources are considered.

Figure 1.5 Parflow node and its outward flow

1.3.1.2 Statistical Channel Models

In statistical or empirical models, the statistics of channel

parameters are collected from actual measurements at various locations of the

transmitter and receiver. As these models are independent of the layout and

structural details of the coverage area, the requirement to survey layouts for

individual applications is eliminated. They include selective parameters

measured from representative categories of coverage areas (Pahlavan et al

1995). Even when all the environmental factors can be separately recognized,

they are implicitly taken into account during modeling. The accuracy of these

models is dependent on the similarity between the environment considered for

analysis and the environment from which the measurements are taken. Since

these models average all the objects within the environment, they do not

f E

f S

f W

f N

f 0

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report variations in signal strength around any particular object. In addition,

the relationship between site layout and channel response at a specific

location cannot be provided using statistical models. On the positive side,

they offer better computational efficiency and are easy to generalize. The

Saleh-Valenzuela model and Log-distance path loss model with Log-normal

shadowing are the empirical models used in modeling indoor radio

environments.

A. Saleh-Valenzuela (SV) Model

Saleh-Valenzuela model is based on the physical realization that the

received signal rays arrive in clusters, with each cluster comprising of several

rays (Saleh et al 1987). The clustering phenomenon is based on the

observation of measured pulse responses. The arrival times of the first rays of

the clusters and subsequent rays within each cluster are modeled as a Poisson

process with different fixed rates. It was found that the arrivals come in one or

two large groups with a 200ns observation window and the expected power of

the rays in a cluster, decayed faster than the expected power of the first ray of

the next cluster. The main drawback of this model was that it did not have any

information about the angles of arrival (Spencer at al 1997), but assumed that

they are independent random variables uniformly distributed over the

interval (0, 2π).

B. Log-distance path loss model with log-normal shadowing

This model is used to compute path loss exponent, a factor

dependent on propagation environment (Rappaport 2002). Average large

scale path loss for an arbitrary Transmitter-Receiver (T-R) separation is

expressed as a function of distance using a path loss exponent. The presence

of obstructions increases the value of path loss exponent which further

increases the signal loss. For a given fixed distance, frequency and

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transmission power, the received signal power varies due to the objects in and

around the signal path. These stochastic, location dependent variations are

called shadowing. Random shadowing effects occurring over a large number

of measurement locations having same T-R separation, but different levels of

clutter on the propagation path results in a phenomenon referred to as log-

normal shadowing. This model includes close-in reference distance, path loss

exponent and standard deviation of the zero mean Gaussian distributed

random variable, to statistically describe the path loss model for an arbitrary

location with fixed T-R separation (Akl et al 2006). The average path loss

PL (d) for a T-R separation d becomes,

0 0( ) ( ) 10 log( / )PL d PL d n d d X (1.1)

Where,

0d - Close-in reference distance

n - Path loss exponent

- Standard deviation

X - Zero mean Gaussian distributed random variable

By accounting for variations in environmental clutter, this model

leads to measured signal, close to the average value.

C. Wide-Sense Stationary Uncorrelated Scattering (WSSUS)

model

Time and frequency selective fading occurring due to multi path

propagation is characterized using WSSUS propagation model. This model

requires two sets of parameters, Power Delay Profile (PDP) and Doppler

Power Spectra (DPS) to describe the propagation effects. A single function

called scattering function characterizes the WSSUS model. It is based on the

assumption that channel is wide sense stationary so that the autocorrelation

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function in time is a function of only the time difference. Uncorrelated

scattering implies that the autocorrelation function in frequency is a function

of only the frequency difference. Using these assumptions, autocorrelation

function, in time and frequency yields spaced-frequency, spaced-time

autocorrelation function (Bug et al 2002). Performance of broadband mobile

communication systems can be analyzed using this model.

Figure 1.6 GSCM model

D. Geometry-Based Stochastic Channel Model (GSCM)

GSCM is based on the directional channel. It provides a

geometrical description of base station and mobile station in polar coordinates

(Cosovic et al 2002). In real propagation environment, the scatterers are

distributed in groups and are known as clusters. Figure 1.6 shows GSCM

model based on the cluster representation of the scatterers. The model is

based on a cluster of scatterers each representing single multi path

component. One cluster moves along with Mobile Station (MS) and is known

as near cluster. The rest are called far clusters, distributed throughout the cell

and each one has certain visibility regions. Each visibility region is the area

visible from the corresponding cluster for the MS on its way through the cell.

Circular regions are defined as visibility regions over the route of MS. When

MS

BS

Local scatterers

Far scatterer cluster

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MS enters a visibility region, the far cluster becomes visible and scatterers

start to create additional paths at the receiver.

When the MS leaves this region, the cluster is made inactive.

Visibility region covers specific part of MS route depending on the cell type.

GSCM distinguishes macro cells (outdoor urban), micro cells (outdoor city)

and Pico cells (indoor). Each of them uses different parameters for the

placement of clusters and scatterers. While macro cells require small number

of clusters (1-2), Pico cells require a mean number of 16 clusters for accurate

channel modeling (Kaltenberger et al 2000).

1.3.1.3 Site-specific Channel Models

Site-specific models are based on numerical methods and can have

detailed and accurate input parameters (Iskander et al 2002). The advantage of

these models is that it can accurately simulate simple indoor environments.

The large computational overhead may prohibit these models from being used

for complex environments. Ray tracing and Finite-Difference Time-Domain

(FDTD) models fall under this category.

A. Ray Tracing Models

The ray-tracing algorithm has been used for accurately predicting

the site-specific radio propagation characteristics. It is gaining importance for

propagation simulation of micro cells and Pico cells. Ray tracing is a

technique of modeling the light path by following light rays as they interact

with optical surfaces. Radio waves are similar to light waves in that the

phenomena of reflection, refraction and scattering apply to both (Nidd et al

1997). This has facilitated ray tracing approach in predicting the signal

strength of radio waves propagating in an indoor environment. Ray-tracing

approaches lead to accurate path loss models. In this approach, the region

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around the transmitting antenna is divided into a cluster of rays and each ray

is traced from source to receiver. The attenuation suffered in each path is

computed. Reflections and diffractions are taken into account while tracing

each distinct ray path. Finally, all the signal components that arrive at the

receiver are added together. Ray tracing method may produce only

approximate results for realistic propagation environments when there is an

inaccuracy in the environmental database.

There are several types of ray tracing methods. Brute force method

(Seidal et al 1992) involves the transmission of large number of rays with

fixed angular separation. Intersection tests are performed on each ray to

determine the scattering points followed by reception test. The advantage of

this method is its applicability to complex environments. But it is

computationally complex and accuracy is heavily dependent on separation

angle.

Image ray tracing method (Tan et al 1996 ; Naruniranat et al 1999)

is a point-to-point tracing technique that produces accurate results without the

necessity for reception tests. The accuracy of the results is dependent on the

input data accuracy. Computational time is dependent on the number of the

input obstacles and it is improved, because the rays that do not reach the

receiver are not considered. Due to the difficulty in selecting scattered rays, it

is not applied to complex environments.

Two-ray ground reflection model (Silva Jr et al 2004) considers

both the direct path and reflected propagation path between transmitter and

receiver. Two-ray model, shown in Figure 1.7, is used when a LoS path exists

between the transmitting and receiving antenna. Though this model is best

suited for predicting large scale signal strength over long distance radio

systems with tall towers, it provides reasonably accurate results for line of

sight micro cell channels in urban environments.

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Visibility tree approach is used to carry out an exhaustive search of

propagation sequences between the transmitter and the receiver (Sanchez et al

1996). Visibility tree has a layered structure comprising of nodes and

branches. Each node represents an object and each branch represents LoS

connection between two objects. The root node represents the transmitting

antenna and the tree is constructed in a recursive manner, starting from root

node. After building the visibility tree, path of each ray is back-tracked from

the leaf to the root and the rules of geometrical optics are applied at each

traversed node. It can be used for any propagation environment, as path

selection process does not depend on geometry. The creation of visibility tree

becomes increasingly complex as one move from 2D to 3D environments.

Figure 1.7 Geometrical two ray model

B. Finite-Difference Time-Domain (FDTD) Model

FDTD method provides a simple and effective technique for

modeling the field distribution in an indoor environment. WLAN planning

requires numerous simulations for different access point locations, and thus

needs fast computation of AP’s coverage area (Gorce et al 2005). In practical

situations, FDTD is employed to reduce the complexity and to ease the

simulation of reflection and diffraction.

Radio propagation characteristics can be derived by solving

Maxwell’s equations of electromagnetic wave propagation. FDTD results in a

h A

α α

Direct path

Reflected path

A

B

h B

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numerical solution of Maxwell’s equations. Maxwell’s time dependent

equations are approximated by a set of finite-difference equations with respect

to specific field positions on an elementary lattice (Talbi et al 1996). The

scheme was proposed by Kane Yee and the lattice structure is known as Yee

Lattice. In the Yee lattice shown in Figure 1.8, the electric field components

correspond to the edges of the cube, and the magnetic field components to the

faces. A grid is defined over the area of interest and initial conditions are

specified. By employing central differences to approximate spatial and

temporal derivatives, Maxwell’s equations are solved directly. Solutions are

determined iteratively at the nodes of the grid.

Figure1.8 Yee Lattice

FDTD uses a leapfrog scheme for marching in time wherein the E-

field and H-field updates are staggered. Spatial staggering leads to locating

each E-field vector component midway between a pair of H-field vector

components. Conversely, H-field components can also be located between

pair of E-field components. The advantage of using this explicit time-stepping

scheme is, to avoid the need for solving simultaneous equations and yielding

dissipation-free numerical wave propagation. On the negative side, an upper

bound on the time-step is necessary to ensure numerical stability. Number of

nodes and the simulation runtime increase proportionately with the size of the

analyzed environment.

H y

E z

E y

H z

H x

(i, j, k) (i+1, j, k)

(i, j, k+1)

(i+1, j+1, k)

(i+1, j+1, k+1)

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Hence, from this survey, it is observed that the deterministic

models have proven to be computationally complex, statistical models lacked

in accuracy and site-specific models offered limited applicability making

them unsuitable for complex environments. Models are developed in the

recent past to overcome these limitations. Deterministic plus statistical models

combine the two approaches to solve some of the inherent problems

(Domazetovic et al 2005). Hybrid methods that use two or more approaches

divide the original problem into a number of sub-problems and treat each one

using the most suitable approach (Skarlatos et al 2005). The theory of Neural

Networks also yielded models like Artificial Neural Network (ANN) model,

which use multilayer perceptron to compromise the limitations offered by

deterministic and statistical models (Nescovic et al 2000). Further

advancements in environmental database and computational resources may

pave the way towards development of new models with improved accuracy.

1.3.2 Channel access schemes of wireless Medium Access Control

Layer – A Survey

The Quality of Service can be improved in Data Link Layer (DLL)

by properly selecting the channel access mechanism for real time multimedia

traffic. This part of the survey provides an eye opener for the existing medium

access schemes.

MAC specifications for IEEE 802.11 has similarities to 802.3

Ethernet wired line standard. Its MAC defines two medium access

coordination functions – the basic Distributed Coordination Function (DCF)

and the optional Point Coordination Function (PCF) (Q.Ni et al 2004). It can

operate both in contention based DCF and contention free based PCF mode

and it supports two types of transmissions – Synchronous and Asynchronous.

This standard is originally designed for best-effort services. It is to be noted

that, the error rate at physical layer of WLAN is more than three orders of

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magnitude larger than wired LAN. Moreover, high collision rate and frequent

retransmissions cause unpredictable delays and jitters, which degrade the

quality of real time data transmissions. Hence, QoS-aware coordination is

necessary to reduce overhead, prioritize frames and prevent collisions, to meet

the delay and jitter requirement under mobile environment.

To incorporate QoS support, the general architectural approaches

called Integrated services (IntServ) and Differentiated Services (DiffServ) are

devised. Intserv provides fine-grained service guarantees to individual flows,

but its setting of states in all routers along the path is not scalable. However,

Diffserv provides coarse-grained controls to the aggregates of flows, but it is

difficult to map between different service domains or sub networks such as

802.11 WLAN. To overcome the problems of these two architectures, QoS

enhancement schemes for infrastructure and ad-hoc networks are proposed.

Figure 1.9 shows the classification of service differentiation based schemes.

To introduce priorities for IEEE 802.11 standard, three techniques

have been proposed in Access Control (AC) scheme (Aad et al 2001). They

are classified as (i) Different back-off increase function, (ii) Different DCF

Inter Frame Space (DIFS) and (iii) Different maximum frame lengths. To

introduce both priority and fairness, an access scheme called Distributed Fair

Scheduling (DFS) (Vaidya et al 2000), is proposed and it utilizes the ideas of

self clocked fair queuing in the wireless domain. To support service

differentiation, a VMAC scheme (Veres et al 2001), based on DCF is

proposed with completely distributed service quality estimation, radio

monitoring and admission control approach. Here, a virtual MAC algorithm

monitors the radio channel and estimates locally achievable service levels.

The main goal of the black burst scheme (Sobrinho et al 1996) is to minimize

the delay of real time traffic. It strictly imposes certain requirements on high

priority stations. But the main drawback of this scheme is that, it requires

25

constant access intervals for high priority traffic; otherwise, performance

degrades considerably. The DC scheme (Deng et al 1999) requires the

minimal modifications on the basic 802.11 DCF.

Figure 1.9 Classification of service differentiation based schemes

It uses two parameters of MAC, the back off interval and Inter

Frame Space (IFS) between each data transmission to provide the

differentiation. Priority based PCF and Distributed TDMA are the schemes

proposed under the station based service differentiation using PCF

enhancement. The Per-flow differentiation scheme (Aad et al 2002) proposed

under Queue based service differentiation has all packets entered in the same

queue independent of their priorities; but, it introduces mutual interferences

Service differentiation based schemes

Station based Schemes Queue based Schemes

DCF based Schemes

AC scheme

DPS

VMAC

Black burst

PCF based Schemes

Priority based PCF

AEDCF

802.11e EDCF

PerFlow scheme

DCF based

802.11e HCF

PCF based

DC scheme

Distributed TDM

26

between priorities and the possible solution is to assign different queues to

different flows in AP. To improve the differentiation than that of Per-flow

scheme, IEEE 802.11e EDCF (IEEE 802.11 WG Draft, 2003) extends the

basic DCF to support up to four DCF queues in one station and each queue

contends for Transmission Opportunity (TxOP) in one station to send the

packets. But this scheme does not consider dynamicity of wireless channel

conditions.

Hence, in Adaptive EDCF (AEDCF) (Romdhani et al 2003),

relative priorities are provisioned by adjusting the size of the Contention

Window (CW) of each traffic class by taking into account both application

requirements and network conditions. Another queue based service

differentiation scheme (Fischer 2001) proposed by IEEE 802.11e working

group using both DCF and PCF enhancements is known as Hybrid

Coordination Function (HCF). It combines the advantages of distributed

contention access of EDCF and centralized polling access of PCF methods.

1.3.3 Packet Routing – Fundamentals and a Survey

Quality improvement on packet routing enhances the quality of

service demanded by the time constrained data traffic at network layer. In

general, the routing protocols use the following metrics to evaluate and

compare the best path for the packets to travel.

Path length

Hop count

Delay

Bandwidth

Load

27

Reliability and

Communication cost

Path length is the most commonly used routing metric. It is the sum

of costs associated with each link traversed, for the routing protocols that

assign arbitrary cost to each link. Hop count is the metric that specifies the

number of passes through routers that a packet must take en-route from a

source to a destination. Routing delay refers to the length of time required to

move a packet from source to destination through the inter network. It

depends on the bandwidth of intermediate network links, the port queues at

each route along the way, network congestion on all intermediate network

links and the physical distance to be traveled. Bandwidth refers to the

available traffic capacity of a link. It is the rating of the maximum attainable

throughput on a link. Load refers to the degree to which a network source,

such as a router, is busy. Load calculation includes CPU utilization and

number of packets processed per second. Reliability in the context of routing

algorithm refers to the dependability of each network link. It is usually

described in terms of Bit Error Rate. Since the performance and the operating

expenditures are important, the communication cost need to be calculated.

Generally, the routing algorithms are classified by type. Key

differentiators include,

Static versus Dynamic

Single path versus Multi path

Flat versus Hierarchical

Host intelligent versus Route intelligent

Intra domain versus Inter domain

Link state versus Distance vector

28

So far, many QoS-based routing algorithms have been proposed.

Most of them start from extending the ability of current best-effort routing

algorithms. However, the current Internet routing protocols are based on

either Distance Vector algorithm or Link-State algorithm. In Distance Vector

algorithm, neighboring routers exchange routing information periodically.

Thus, every router can learn the routing information from others. Based on

that information, the shortest path to every destination can be computed. This

is also called as Bellman-Ford algorithm. While in Link-State algorithm,

every router advertises its link state information to the whole network; thus,

every router can receive the link-state information. Such information is

maintained in a local database of every router, from which the routing table is

calculated using Dijkstra's shortest path algorithm. The advertising is

triggered by events, and it also happens periodically.

In wireless routing protocols, the routes are selected with the

shortest-path or minimum hop count to the destination. However, the bad

signal quality and the longer hop length links are not considered in the route

selection process. Hence, to eliminate the bad links, there are several studies,

proposals and even new routing architectures that address the instability of

wireless channel and the link quality are suggested. The Roofnet team

provides an extensive experimental study of the wireless characteristics of a

multi-hop 802.11b network in an urban environment. Based on the link

measurements, it is concluded that, routing through the shortest path is not

sufficient in multi-hop wireless networks (Couto et al 2003). It is also

reported that the correlation between SNR and loss rate on the link with

intermediate quality is rather weak. Quantifying the wireless link quality by

using different information and technologies are also suggested. In

Associativity Based Routing (ABR) (Toh 1997) and Stability based Adaptive

Routing (SSA) (Dube et al 1997), temporal link stability is used as the route

selection criteria. In (Punnoose et al 1999), Global Positioning System (GPS)

29

information is used with the propagation model to predict the received signal

power which is used to calculate the link quality metric. To find the high

throughput path on the multi-hop wireless network, Dynamic Source Routing

(DSR) and Dynamic destination Sequenced Distance Vector (DSDV) are

modified to use the Expected Transmission count metric (ETX) (Couto et al

2003a) as the route selection criterion. In (Hsin Mu Tsai et al 2006), the link

quality aware routing protocol which selects the route, based on the link

quality and hop count under AODV protocol, is discussed. In this algorithm,

routes with bad links are eliminated, while maintaining the connectivity of the

network.

1.3.4 Congestion Control – An overview and a survey

Most networks fail to tell applications, how much bandwidth is

available at any given instant. As a result, applications have no basis on which

transmission is controlled. When applications send more data than that of the

network handling capacity, buffers fill up and may overflow. Then, the

application must retransmit the data, which adds more traffic and further

causes congestion in the network. Apart from this, whenever the total input

rate is greater than the output link capacity, congestion occurs. It also occurs

due to shortage of buffer space, slow links and slow processors. Hence,

congestion control is necessary to ensure the negotiated Quality of Service for

the users. To exercise congestion control at protocol level in the network,

TCP based congestion control algorithms are developed. However, protocol

design in wireless networks requires the consideration of several additional

aspects of communication than that in the wired networks. These complexities

arise from the wireless communication link as well as due to the mobility of

the hosts involved. It is understood that, there are two major issues that need

to be considered for TCP to work in WLAN: non-congestion loss and packet

reordering.

30

Non-congestion loss

Two types of non-congestion packet losses occur. The first type is

the random packet loss that occurs due to the corruption of bits. Such packets

are discarded by the routers or the end hosts. These random losses occur in

the environments where the various factors unexpectedly disturb the

communication. The second type of packet loss is the disconnection packet

loss that occurs when the mobile host completely disconnects or moves out-

of-range from the wireless network. This type of packet loss is a characteristic

of the infrastructure networks (WLANs) and occurs either when a mobile host

becomes physically too distant from the base station or when it moves

between two adjacent wireless networks (handoff). The loss will occur on

every packet transmitted until the mobile host reconnects to the original base

station or the neighboring one.

Packet reordering

The problem of packet reordering occurs, when a packet reaches

the destination sooner than the packet(s) previously sent. Since the destination

host expects in-order packet delivery, it will respond with the duplicate ACK.

When the delayed packets reach the destination, the correct cumulative ACK

is created and the communication resumes as normal. When the packet

reaches the host out of order, the host reacts exactly the same way as it would

in the case of a packet loss.

The major classification of TCP congestion control schemes

include, Slow start and Congestion avoidance, Fast retransmit and Fast

recovery.

31

1.3.4.1 Modifications of TCP on wireless

Conventional TCP schemes may suffer from a severe degradation

in performance in mixed wired and wireless environment. The types of

performance enhancement approaches include:

End-to-end

Split connection

Link layer

In split connection schemes (Bakre et al 1995; Brown et al 1997;

Wang et al 1998), a single TCP connection is split into two TCP connections.

It requires large amount of memory and increases the delay too. An example

is I-TCP. In Link Layer schemes (Balakrishnan et al 1995; Keshav et al 1997;

Ratnam et al 1998; Balakrishnan et al 1998), Forward Error Correction,

Retransmission schemes at link layer are employed. But there are no accurate

timeout mechanisms, and retransmission delays. The examples include

SNOOP and ELN. In End-to-end schemes, no modifications at intermediate

router are required. It is also simple to implement. The examples include TCP

New Reno, Vegas and Westwood.

Hence, the literature survey of TCP variants based on end to end

approach is further discussed. Until the mid 1990s, all TCPs’ set timeouts and

the measured round-trip delays were based only upon the last transmitted

packet in the transmit buffer. University of Arizona researchers, Larry

Peterson and Lawrence Brakmo introduced TCP Vegas, in which timeouts

were set and round-trip delays were measured for every packet in the transmit

buffer. In addition, TCP Vegas used the concept of additive increase and

additive decrease in the congestion window. However, this variant was not

widely deployed. In end-to-end approach, TCP-Peach (Akyildiz et al 2001) is

32

designed for satellite communication environment, where large bandwidth-

delay product is the nature. Its important assumption is that the routers must

support priority queuing. In TCP-Peach plus (Akyildiz et al 2002), the actual

data packets with lower priority replace the low priority dummy packets as

the probing packets, to further improve the throughput. TCP-Westwood

(Casetti et al 2001) is a rate based end-to-end approach, in which the sender

estimates the available network bandwidth dynamically by measuring and

averaging the rate of returning ACKs.

In TCP New Reno (Floyd et al 1999), an extension of TCP Reno

with improved fast recovery algorithm, the multiple losses cannot invoke the

slow down and fast recovery phase. It terminates the recovery, when it

receives a full ACK. It cannot distinguish between congestion and wireless

packet losses (Balakrishnan et al 1996). Moreover, reduction in transmission

rate based on Additive-Increase-Multiplicative-Decrease (AIMD) algorithm

reduces the throughput in wireless networks. In TCP New Jersey (Xu et al

2005), the bandwidth estimator and congestion warning mechanism

differentiate the cause of packet loss at the intermediate router. But its

throughput may be reduced due to the background traffic pattern. Moreover,

when it detects a packet loss or retransmission timeout is expired, it may not

recover the dropped sending rate effectively based on the cause of loss. The

TCP-NJ+ (Kim et al 2007), a modified version of TCP New Jersey, increases

the throughput by increasing the dropped sending rates, with its improved

RTO mechanism and error recovery mechanism. All these variants use

normal ACKs, to indicate the successful acknowledgements of received

packets from the receiver. But an ACK can give information about a single

packet loss to the sender. So, when multiple losses occur, the sender can know

only about the first lost packet and it has to wait for more ACKs from the

receiver to retransmit all the lost packets.

33

However, to provide optimal service for unicast multimedia flow

operating in wired Internet environment, a TCP Friendly Rate Control

(TFRC) protocol is used. It is based on TCP Reno’s throughput equation. To

eliminate the throughput degradation of TFRC in wireless networks, the

network supported and end-to-end approaches are devised. Network

supported enhancements of TFRC need the support of intermediate nodes in

the network such as routers, proxies, access points or other kinds of devices.

The typical examples are, ECN based TFRC (Choudhary et al 2003), Proxy

based TFRC (Huang et al 2002), WM-TFRC (Pyun et al 2003), and AED

based TFRC (Arya et al 2003). These schemes suffer from deployment

difficulties. In contrast, the end-to-end enhancements avoid these difficulties

since their modifications on TFRC only involve the end-to-end node.

MULTFRC (Chen et al 2004) belongs to this category and it only modifies

TFRC protocol on the sender side. Another protocol TFRC Veno (Binzhou et

al 2007) is a kind of end-to-end enhancement which modifies TFRC only at

the sender side. It replaces the throughput equation of TCP Reno by using the

more advanced equation derived from wireless TCP Veno protocol.

1.3.4.2 ECN and SNACK schemes

Explicit Congestion Notification (ECN) mechanism (Floyd 1994)

conveys the network congestion from the routers to end stations, through the

Congestion Experienced (CE) bit of the IP header. The CE bit is enabled

when the average queue occupancy of the router exceeds the threshold. The

TCP receiver echoes this information back to the sender via the Explicit

Congestion Echo (ECE) bit, until the sender takes action on the congestion

notification. Therefore, the ECN router explicitly marks the packets to alert

the sender of incipient congestion.

The Selective Negative Acknowledgement (SNACK) (Consultative

Committee Book, 1999) combines the goodness of Selective

34

Acknowledgement (SACK) (Mathis et al 1996) and Negative

Acknowledgement (NAK). In this scheme, the sender receives the entire

status of the receiver buffer (received and missed segments) within a single

ACK. Moreover, for specifying the bit-vector the SNACK requires only a

small number of bytes in TCP header to indicate all lost packets. This is

useful, when the out of sequence queue is very large. It is also useful when

large windows are operated over long delay path. SNACK improves the

performance of TCP in wireless networks (Cheng et al 2006).

1.3.5 Video Streaming fundamentals and a survey

Multimedia transmission over wireless network has added a new

dimension to the way users communicate. QoS support has a profound role in

ensuring better audio-visual experience to the end users. The stringent QoS

requirements of encoded video make video streaming over wireless links, a

challenging problem. One of an approach to provide QoS guarantees in

WLAN is through resource reservation mechanisms. A key component in the

reservation scheme is the characterization of traffic. Traffic characterization

overcomes the difficulty in resource allocation by accurately specifying the

traffic arrivals on a video connection and verifying the resource availability to

support traffic at the desired QoS. Moreover, real-time applications like video

transmission require the delivery of video content in a defined period. Hence,

the bounds on initial delay and buffer size dictate the performance of the

system. It is obvious that frequent buffer underflows and overflows affect the

QoS parameters like delay and throughput. Increasing the initial delay would

reduce the occurrence of an outage at the expense of increase in buffer size.

Therefore, determination of minimum initial delay and the minimum required

buffer size provides a guaranteed QoS in wireless environment. The related

literature survey is presented here.

35

Concord algorithm (Sreenan et al 2000) for delay-sensitive

applications uses historical information of delay probabilities to predict the

total end-to-end delay experienced by audio and video traces traversing the

Internet. Minimal playback delay and client buffer size for the optimal

smoothing of MPEG-2 video over ATM network has been discussed in (Le et

al 2000). Upper bounds for estimation of minimum delay and buffer size for

MPEG-4 video transmission over UMTS Terrestrial Radio Access Network

(UTRAN), has been derived through simulation in (Stockhammer et al 2004).

Deterministic traffic characterization by a family of rate-interval pairs is

discussed in Deterministic Bounding INterval Dependent (D-BIND) (Wrege

et al 1996). Adaptive media playout strategies discussed in (Steinbach et al

2001), are aimed at reducing the client buffer size while preserving the same

resilience against buffer underflow. They have been employed to reduce the

latency in video streaming applications. The performance of algorithms for

adaptively adjusting the play-out delay of audio packets in an interactive

packet-audio terminal application, under varying network delays is

investigated in (Ramjee et al 1994). It is observed that an adaptive algorithm

which explicitly adjusts to the sharp, spike-like increases in packet delay can

achieve a lower rate of lost packets for both the given average playout delay

and the given maximum buffer size.

1.3.6 Voice over IP – An overview

Voice over IP (VoIP) is defined as the routing of voice signals over

any IP-based network. It has the ability to make telephone calls like in

the Public Switched Telephony Network (PSTN), and to send facsimiles

over IP-based data networks with a suitable Quality of Service. It is one of

the emerging trends feasible for carrying voice and call signaling messages

over the Internet by adopting standards like H.323, SIP, etc., The

following are the issues related to VoIP.

36

A significant problem that is associated with VoIP systems

is the delay in packet transmission. This delay results in echo

and talk overlaps. An echo is usually caused, if the round

trip delay of the signal from destination back to the source is

more than 50mS. Talk overlap occurs, if the packet delay in

any direction exceeds 250mS. These problems can be solved

by implementing echo control and cancellation methods in

VoIP systems.

The inter-packet delay called jitter is another problem

associated with the VoIP transfer. It causes distortion in

talks. It can be minimized by holding the packets until the

slowest packets arrive at the destination. However, it

increases the delay in playing the voice.

Another severe problem affecting the packet network is

packet losses and it is solved by packet retransmission.

However, voice packet transmission needs different

approaches. Some of the solutions include, replaying the last

packets until new packets arrive, sending redundant

information to compensate for the loss and the hybrid

approach that combines these two methods.

Recent online security measures cannot adequately handle

VoIP processing requirements and changes in protocols and

mechanism will take time for a hassle free, secure VoIP

service. This includes internet vulnerabilities like Denial of

service attacks, Phishing, snooping and spoofing.

Backward compatibility is another major factor. VoIP

protocols do not effectively work with older firewalls and

37

Network Address Translation (NAT), which is a part of

some LAN and WAN networks.

From the related literature survey, it is found that, in the previous

work on VoIP over WLAN, the references (Chen et al 2002 ; Veeraragavan

et al 2001), assumed the use of PCF at their MAC layers to support VoIP.

Since PCF is not supported in most of the IEEE 802.11 products, references

(Bladwin et al 2001 ; Garg et al 2002 ; Hiraguri et al 2002 ; Banchs et al

2001) studied the use of DCF to support VoIP. The various schemes for

improving the voice capacity are also investigated and they all require

modification in the MAC protocol used by the VoIP stations. Moreover, there

have been many schemes proposed (Kuri et al 1999 ; Sun et al 2002 ; Tang

et al 2000) for reliable multicast of voice over IP networks.

1.4 JUSTIFICATION OF RESEARCH

The next generation wireless networks are targeted at supporting

various real time multimedia applications over packet switched networks. In

these networks, person to person communication can be enhanced with high

quality voice and video transmission. Hence, providing QoS guarantees to

various applications is an important objective in designing the forth coming

networks. Even though, there are two approaches (end to end and network

centric) practiced to support QoS guarantees, the best one devised is network

centric approach. That is routers, switches and base stations in the network are

required to provide QoS support to satisfy the demands requested by

applications. Hence, in this dissertation,

An analysis on appropriate channel access mechanisms of

MAC layer that suits real time data transfer is done, and the

following are proposed.

38

a cross layer routing algorithm (TLAODV) for secured

routing at network layer

congestion control algorithms (SNACK-TCP-ECN and

ETFRCV) at transport layer

a deterministic model for the quality transfer of MPEG-4

video streams

an integrated model for VoIP transmission under wireless

distribution system.

Some of the proposed schemes are also implemented in network

processors as application modules with constraints, to validate certain results.

1.5 AN OVERVIEW OF PROPOSED SCHEMES AND REPORT

ORGANIZATION

The success in the deployment of next generation networks

critically depends upon how efficiently the wireless networks can support

traffic flows with QoS guarantees. To achieve this goal, QoS provisioning

mechanisms need to be efficient and practical. For this reason, the focus is

primarily on designing efficient and practical algorithms for channel access,

routing, congestion control, rate control and admission control. The

dissertation also contains an integrated model for VoIP and a deterministic

model for video streaming. Apart from this, the implementations in the

network processors for the proposed schemes are presented as application

modules. An overview of the proposed models is shown in Figure 1.10.

In Chapter I, the literature survey of wireless channel models,

channel access mechanisms, routing and congestion control algorithms and

39

fundamentals of video streaming algorithms and VoIP algorithms are

discussed to the required proximity.

Chapter II analyzes the support and limitations of basic and

adaptive channel access schemes for real time traffic. It is well known that,

the channel access schemes play an important role in aiding the demand of

real time flows, to guarantee the QoS requirements. From the analysis, it is

proved that the adaptive channel access mechanisms perform better than the

basic schemes. The observations are validated with an application of efficient

video transmission using adaptive channel access techniques in an existing

QoS enhanced cross layer architecture.

Figure 1.10 An overview of proposed models

In chapter III, a wireless routing protocol named TLAODV, to

eliminate the routes with bad links is proposed. The route selection process of

this algorithm is based on a cross layer Route Metric, which considers the

PHY Layer

MAC Layer

Network Layer

Transport Layer

Application Layer

Study on Wireless channel models

Analysis of channel access mechanisms to suit real time multimedia data transfer and validate with an application

Proposed TLAODV algorithm for secured QoS enhanced routing and implementation of security

algorithms at core and edge routers

Proposed SNACK-TCP-ECN algorithm and ETFRCV algorithm for congestion control at

protocol level

Proposed a deterministic model for MPEG-4 video streaming and proposed an integrated

model for Voice over IP traffic

40

frame transmission efficiency of the MAC layer and the Signal to Noise Ratio

(SNR) of the PHY layer. Moreover, the security enhancements on the

proposed routing protocol involve the establishment of trust relationships

among nodes depending on successful and failed states of communication.

Since voice traffic is very sensitive to network delay, and voice packets are

more vulnerable to threats and attacks, the proposed algorithm is tested with

VoIP sessions. Also, the applications on firewall type screening / filtering

technique to secure the edge routers and the implementation of RSA and AES

algorithms in core routers using the network processors are also presented in

this chapter.

Though the performance of TCP is appreciable in wired

environment, it requires adequate improvement in wireless scenario. The

performance degradation is mainly because of its inability, to distinguish the

congestion losses and other types of link losses. Hence to address the issue of

loss differentiation, a SNACK – TCP with ECN algorithm is proposed in

chapter IV. It is based on the TCP variant TCP-NJ+. The SNACK (Selective

Negative Acknowledgement) is incorporated to indicate the multiple packet

losses at one time and the Explicit Congestion Control is incorporated to

forecast the congestion status. Moreover, another issue of using TCP as a

transport layer protocol in video streaming applications is also addressed with

an Extended TFRC Veno (ETFRCV) algorithm. It is an enhancement of

TFRC veno protocol, proposed to meet the special needs of video streaming.

It decouples the wireless link loss from that of congestion loss, based on the

queuing delay incurred in the routing buffer.

In chapter V, a deterministic approach towards QoS provisioning

for MPEG-4 video streaming is proposed. Since the bounds on initial delay

and playout buffer size dictate the performance of the video streaming in a

system, the computation of minimum initial delay and optimal playout buffer

41

size becomes mandatory, to provide a guaranteed QoS for a given video

sequence transmitted over WLAN. Frame loss, Peak Signal-to-Noise Ratio

(PSNR) and Mean Opinion Score (MOS) are computed for different playout

buffer sizes to prove that, the minimum playout buffer size and latency is

sufficient to provide QoS guarantee. This chapter also incorporates an

application model for an adaptive buffer management technique that reduces

the packet loss at the video player. Here, the maximum buffer size that can be

offered at the maximum transmission rate of the video packets is obtained.

This model is implemented using IXP 2400 Network Processor hardware.

In chapter VI, an integrated model for VoIP transmission over

WLAN under Wireless Distribution System (WDS) is proposed. The

conventional VoIP transmission in WLAN suffers from the problems of

header overhead being larger than the VoIP payload, and the quality of VoIP

along with the back ground traffic is not assured. The quality deterioration of

voice in WDS environment is mainly due to larger network delay. The

proposed model addresses these issues through a novel compression /

decompression algorithm and also with the improved Codec system. This

chapter also includes an application model implemented using network

processor, to regulate the traffic at the access points, with an efficient Call

Admission Control (CAC) algorithm.

The results from each chapter are consolidated and discussed in

Chapter VII. The scope for future research and the direction in which future

work can be carried out is also discussed in this chapter.