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Chapter 2
Research Methodology
2.1 Introduction :
The chapter endeavors to provide technical background materials to
provide basis for understanding technologies viab le for development
of cognitive radio based networks tools used to design cognitive radio
based network are typically considered to be of two type (i) hardware
or SDR (software defined radio) with their ancil laries ( i i) software to
control network. First of al l we are going to discuss technologies
available for hardware design and development of cognitive radio .
The basic technologies used for
2.2 Software-defined radio (SDR):
SDR, is a radio communication system where components are
implemented by means of software on a personal computer or
embedded computing devices with minimal e lectronic hardware l ike
antenna, RC coupled circuits etc.
A basic SDR system can be made up of a general purpose
computing devices equipped with a sound card, or other analog -to-
digital converter, preceded by some form of Radio Frequency front
end (RFEE). Significant amounts of signal processing are handed over
to the general -purpose processor, rather than being done in special -
purpose hardware l ike FPGA or DSP.
Software-defined radio (SDR), refers to wireless
communication in which the transmitter modulation is ge nerated or
2. Research Methodology 15
defined by a software program and computing device , and the
receiver uses a computing devices and software to recover the signal .
To select the desired modulat ion type, the proper programs must be
run by computing system that control the transmitter and receiver.
The most significant asset of SDR is i t’s flexibil i ty and
dynamicity . Wireless systems employ protocols that vary from one
service to another. Even in the same typ e of service, for example
wireless fax; the protocol often differs between geographic locations .
A single SDR set with an all - inclusive software repertoire can be used
in any mode anywhere in the world. Changing the service type, the
mode, and/or the modulation protocol involves simply select ing and
launching the particular computer software .
The ult imate goal of SDR technology is to provide a single
radio transceiver capable of playing the roles of cordless tele phone,
cell phone, wireless fax , wireless e-mail system, pager, wireless
videoconferencing unit , wireless Web browser, Global Posit ioning
System (GPS) unit , and other functions st i l l in the realm of science
fict ion, operable from any location on the surface of the earth, and
perhaps in space as well .
2.3 SDR System Architecture:
Figure 2.1 i l lustrate an SDR-based wireless base station that
you can reconfigure to support multiple standards. To reconfigure the
entire system, an ideal SDR base stat ion has to perform all signal
processing tasks as digital signal processing. However, current -
2. Research Methodology 16
generation wideband data converters are not capable to support the
processing bandwidth and dynamic range required across different
wireless s tandards. As a resul t , the analog -to-digi tal converter (ADC)
and the digital - to-analog converter (DAC) are usually operated at
intermediate frequency (IF) and separate wideband analog front ends
are used for subsequent signal processing to the radio frequency (RF)
stages, as shown in Figure 2.1
1. DUC: Digital up converter
2. CFR: Crest factor reduction
3. DPD: Digital pre distort ion
4. DDC: Digital down converter
5. PA: Power amplifier
6. LNA: Low noise amplifier
2. Research Methodology 17
Figure 2.1 SDR Architecture Based on Current -Generation
Technology1
1 Courtsey ALTERA FPGA solution For SDR (www.altera.com/end-
markets/wireless/advanced-dsp/sdr/wir-sdr.html )
2. Research Methodology 18
2.3.1 Digital IF Processing:
Digital IF extends the scope of digital signal processing (DSP)
beyond the baseband domain out to the antenna to the RF domain.
Digital IF processing increases the flexibil i ty of the system while
reducing implementation costs. Moreover, digital frequency
conversion provides greater flexibil i ty and higher performance than
tradit ional analog techniques. Altera Stratix®
series FPGAs, with their
high-performance embedded DSP blocks, Nios®
II embedded soft
processors, Tri Mat rix memory architecture, and high -speed
interfaces, provide a highly flexible and integrated platform to
implement computat ionally intensive digital IF functions including
digital up-down converters.
2.3.2 Digital Up converter:
Data formatting often required between the baseband processing
elements and the up converter can be seamlessly added at the front
end of the up converter, as shown in Figure 2.2. This technique
provides a fully customizable front end to the up converter and allows
for channelization of high-bandwidth input data, which is essential
part of many wireless system. Logic or a Nios II embedded processor
can be use to control the interface between the up converter and the
baseband processing element.
2. Research Methodology 19
Figure 2 .2. Digital Up converter2
1. RRC = Root-raised cosine
2. NCO = Numerically controlled oscil lator
2 Courtsey ALTERA FPGA solution For SDR (www.altera.com/end-
markets/wireless/advanced-dsp/sdr/wir-sdr.html )
2. Research Methodology 20
In digital up conversion, the input data is baseband fi l tered and
interpolated before i t is quadrature modulated with a tunab le carr ier
frequency. To implement the interpolating baseband finite impulse
response (FIR) fi l ter , Altera ‘s FIR Compiler can be use, which can
support optimal fixed or adaptive fi l ter architectures can be buil t for
a part icular standard through speed -area tradeoffs. Altera also has the
NCO Compiler intel lectual property (IP) core that can generate a wide
range of architectures for oscil lators with spurious -free dynamic
range in excess of 115 dB and very high performance. Depending on
the number of frequency assignments to be supported, you can easily
instantiate the right number of digital up converters in a
programmable logic device (PLD).
2.3.3Crest Factor Reduction:
Wireless code-division multiple access (CDMA)-based systems
and multi -carrier systems such as orthogonal frequency division
multiplexing (OFDM) exhibi t signals with high crest factors (peak -to-
average ratios). Such signals drastically reduce the efficiency of PAs
used in the base stations. Altera FPGAs has a reconfigurable platform
for SDR base stations to implement CFR techniques that are
customized to each standard.
2.3.4 Digital Pre distortion:
The wireless standards and their high-speed mobile data
versions employ non-constant envelope modulation techniques such as
quadrature phase shift keying (QPSK) and quadrature amplitude
2. Research Methodology 21
modulation (QAM). These places has str ingent l inearity requirements
on the power amplifiers. DPD linearization techniques, including both
look-up table (LUT) and polynomial approaches, can be efficiently
implemented using Stratix series FPGAs. The multipliers in the DSP
blocks can reach speed up to 480 MHz and can be effectively t ime -
shared to implement complex multiplications. Whe n used in SDR base
stations, we can implement Stratix series FPGAs to implement the
appropriate DPD algorithm that efficient ly laniaries the PA used for a
specific standard.
2.3.5 Digital Down converter:
On the receiver side, digital IF techniques can be used to sample an
IF signal and perform channelization and sample rate conversion in
the digital domain. Using under sampling techniques, high frequency,
IF signals ( typically 100+ MHz) can be quantified. For S DR
applications, since different standards have different chip/bit rates,
non-integer sample rate conversion is required to convert the number
of samples to an integer mult iple of the fundamental chip/bit rate of
any standard. Al tera’s DSP Builder tool has facil i ty of programmable
resample block that can perform non -integer decimation with
conversion ratios between 0.5 and 1
2. Research Methodology 22
Figure 2.3. Digital Down converter3
3 Courtsey ALTERA FPGA solution For SDR (www.altera.com/end-
markets/wireless/advanced-dsp/sdr/wir-sdr.html )
2. Research Methodology 23
2.3.5 Baseband Processing:
Wireless standards are evolving leap and bound to support
higher data rates through the introduction of advanced baseband
processing techniques such as adaptive modulation and coding, space -
time coding (STC), beam forming, and multiple -input mult iple-output
(MIMO) antenna techniques. The baseband signal processing devices
require enormous processing bandwidth to support such
computationally intensive algor ithms. Altera FPGAs has solution for
such applications with examples being channel coding for HSDPA and
beam forming.
The baseband components has to be flexible enough to enable
SDR functionali ty that is required to support migration between
enhanced versions of the same standard as well as the capabil i ty to
support a completely different standard. The remote upgradeabil i ty
feature using the Nios II based embedded processor, along with the
availabil i ty of a wide array of IP cores make Altera FPGAs an ideal
choice to enable such SDR functionali ty in both transmit and receive
signal processing data paths. Figure 2.4 shows a scenario where
Altera FPGAs can be easily reconfigured to support the baseband
transmit functions for ei ther WCDMA/HSDPA or 802.16a standards
through available Mega Core®
functions and reference designs such as
the Reed-Solomon encoder and inverse fast Fourier transform (IFFT).
2. Research Methodology 24
Figure 2.4. SDR baseband data path reconfiguration4
4 Courtsey ALTERA FPGA solution For SDR (www.altera.com/end-
markets/wireless/advanced-dsp/sdr/wir-sdr.html )
2. Research Methodology 25
2.2.6 Co processing Features:
As shown in Figure 2.5 , SDR baseband processing requires both
processors and FPGAs, where the processor handles system control
and configuration functions while the FPGA implements the
computationally-intensive signal processing data path and control ,
minimizing the latency in the system. To by standards, the processor
can switch dynamically between major sections of software while the
FPGA can be completely reconfigured, as necessary, to implement the
data path for the particular standard.
Data
Control
Figure 2.5 Co-Processing Architecture for SDR5
Altera FPGA coprocessor interface with a wide range of DSP support
with general-purpose processors provide increased system
performance and lower system costs . Altera’s SOPC Builder, which
includes an extension of the MATLAB’s Simulink environment,
known as DSP Builder, is a robust tool to facil i tate coprocessor
programming and integration. With DSP Builder, we can assemble
5 Courtsey ALTERA FPGA solution For SDR (www.altera.com/end-
markets/wireless/advanced-dsp/sdr/wir-sdr.html )
Processor
FPGA / SDR
Co Processor
2. Research Methodology 26
parameterized blocks representing a plethora of functions ranging
from mixes through fully parameterized FIR fi l ters. Once a dataflow
system has been captured in DSP Builder, i t can be exported for use
as a coprocessor in any processor -based system assembled by SOPC
Builder . Using SOPC Builder’s interactive menus, we are able to set
the parameters of the components they intend to use and then can
choose the optimal Avalon system interconnect to connect the
selected components. In addit ion, you can store function blocks
created using SOPC Builder for reuse in future designs, providing
addit ional t ime and cost benefits .
2. Research Methodology 27
Fig 2.6 Typical SDR
2. Research Methodology 28
2.4 OMNET++
OMNET++ is open source tool to study protocol s for wireless
network. We use MIXIM , a mixed simulator combining various
simulation frameworks developed for wireless and mobile simulations
in OMNET++. It provides detailed models and protocols, as well as a
supporting infrastructure.
Environment models : Defines relevant parts of the real world and
i t ’s reflect ion, such as obstacles and others which can hinder wireless
communication
.Connectivity and mobility Defines movement in nodes and objects
and their influence on other nodes in the network varies. The
simulator has to track these changes and provide an adequate
graphical representation to i t .
Reception and coll ision In wireless environment , movements of
objects and nodes have an influence on the reception of a message.
The reception handling take care of modeling a transmitted signal
changes on i ts way to the receivers, taking transmissions of other
senders into account .
Experiment support The experimentation support is necessary to
help the researcher and scholar in comparing the results with an ideal
state, help him to find a suitable template for his implementation and
support different evaluation methods.
Protocol l ibrary MiXiM has a r ich set of protocol l ibrary , which
enables researchers to compare or enhance their ideas with already
implemented ones.
2. Research Methodology 29
I t provides these so lutions by aggregating the approaches of several
exist ing simulation frameworks into one viz. the mobil i ty support ,
connection management, and basic design structure is taken from the
Mobili ty Framework (MF). The radio propagation models and air
interface are taken from the Channel Simulator and the protocol
l ibrary is taken from the MAC(Medium Access Control) simulator , the
Posit i f Framework and from the Mobili ty Framework. From the
experience gathered usages of each of these simulators, MIXIM
introduces unique extensions l ike 3D object support , models generate
from walls and obstacles that can influence the mobili ty and the
attenuation of radio signals, different frequencies and transmission
media (radio waves, ultrasound), ful l duplex multi-channel support in
3D of t ime, space and frequency, enabling Orthogonal Frequency
Division Multiplexing (OFDM) and Multiple Input Multiple Output
(MIMO) simulations, and support for various MAC protocols. Apart
from that researcher can develop their own protocol stack or enhance
exist ing one
MIXIM supports the simulation of networks having 1000 Plus
nodes, i t ’ low memory consumption and modular structure a llows the
adaptation of the level in detail . A TCL/TK basedd graphical
configurat ion interface helps to choose the right modules, stack them
in layers, and assign values to their parameters.
2.4.1 Simulation modules:
To collect global parameter l ike dimension of network in 2 -D or
3-D environmental model is used. MIXIM uses objects to model the
2. Research Methodology 30
environment used in simulat ion . Objects has influence on radio
wave propagation and the mobili ty of other objects and nodes . . The
Object Manager is responsible for managing objects, providing
services to the rest of the simulation including calculating which
objects interfere within a given l ine -of sight between two nodes.
The Connection Manager module take charge of dynamic al
management of connections between interfering nodes. Connection
Manager knows exact posit ion of al l nodes and can query object
posit ions from the Object Manager. In general , MiXi M supports
multiple connection managers, responsible for different freque ncy
ranges such as radio waves and ultra sound in different bands l ike
GSM, UNNI, ISM etc. . Which can be used for Cognitive Radio based
application simulation
Node modules:
In a network network, MAC physical layer are found which are
connected by OMNET++ gates .A pair of gates are used for
connecting two different layer. First pair for passing up and down
messages and control messages. Second pair is used to support control
communication. Message exchange system between connections in
NIC can be used to exchange control messages between layer. In
wireless system MAC and PHY are t ightly coupled and specific to
different communication system. MiXim supports flexible MAC PHY
system to support Cognitive Radio system.
MiXim supports different modules to simulate different aspects
or aggregation module of wireless system like mobili ty module
2. Research Methodology 31
responsible for mobili ty of node and object and their effect there
after. The battery module responsible for energy system of a
part icular module. ARP module for mapping address from MAC to
network .
The uti l i ty module is derived Mobili ty Framework ’s blackboard
module. I t has two main tasks: First one to provides a interface for
collecting statist ical data of a simulation. I t col lects stat ist ical data
with minimal impact on the performance of the simulation and
supports f lexibil i ty for different analysis methods. Finally , the uti l i ty
module maintains parameters that need to be accessed by information
sharing module within a node .
2.4.2 Base framework and protocol l ibrary:
MIXIM is divided into two parts: the base framework and the
protocol l ibrary. First one the base framework provides functionali ty
essential to simulate any wireless system. It contains connection
management, mobili ty, and wireless channel modeling. The protocol
l ibrary complements the base framework with a r ich set of standard
protocols . Which contains mobili ty models , MAC protocols . MIXIM
has a base module for each module in OMNET++.
2.5 MiXiM BASE MODELS:
as we are aware that s imulating a wireless communication
systems requires a suitable abstraction of the environment, the radio
channels , and the physical layer. To accomplish this MIXIM provides
a modeling framework. In t he basic modeling approaches discusses ,
the assumptions behind these approaches, and implementation
2. Research Methodology 32
relevant aspects such as model abstraction level and model support
for trading off accuracy and calculation complexity.
Environmental model:
Simulations of any wireless system are carried out on a l imited
area, 10m X 10m, on which nodes and objects are placed. Nodes are
wireless devices with their protocol stack and are modeled as
isotropic radiators not having any physical dimension. An object is
anything or everything with a physical dimension that resides in the
propagation environment and can possibly affect (attenuate) a
wireless s ignal. Both, objects and nodes may be mobile. Nodes may
even be combined with objects
MiXiM simulation system model mobili ty as continuous
process and level of accuracy of model. In i t accuracy and
computational complexity can be user defined. MiXiM provides user
defined mobili ty parameter . This also provides specific updates of
posit ion in specific interval as defined facil i tat ing better and faster
interpolat ion.
MIXIM provides the Object Manager acts as a central authority
for managing objects in the wireless environment. Objects are
characterized by posit ion, dimensions, angle of rotation (optionally),
and frequency-dependent attenuation factors in 3-D. An object can
obstructs the l ine-of-sight between any pair of interconnected nodes
causes addit ional signal losses during transmission Since enti t ies can
be mobile, intersections of the l ine -of-sight of two nodes with one or
more objects must be de termined dynamically at runtime . For any
2. Research Methodology 33
intersection with an object , i ts frequency -dependent attenuation factor
is sumed up to yield the addit ional at tenuation caused by the objects .
Connection modeling:
Connectivity modeling is a challenging task in wi reless s imulations.
In wireless system the “channel” between two nodes is the air or
vacuum, which is a broadcast medium and i t is difficulty to model as
one connection. MIXIM has a solution by dividing the modeling into
two parts. The first one is the wireless channel and i ts at tenuation
property. The second one is the connectivity between nodes .
A signal sent out by one node affects al l other nodes in the
environment. However, the signal is at tenuated, so that the received
power at nodes may vary and node very far away from the sending
node may receive very low signal that t is negligible . To reduce the
computational complexity in MIXIM, nodes are connected only when
they are within the range of maximal interference distance. The
maximal interference distance is a bound on the maximal distance up
to which a node possibly can disturb the communication of a
neighbor. A connection in MIXIM is probably better defined by i ts
complement: All nodes that are not connect, definitely they do not
interfere with each other. Following this concept, a node that wants to
receive a message from a communication peer, also receives al l
( interfering) signals and can, thus, decide on the interference level.
The presence of objects in the environment al so impacts the
maximal interference distance due skin effect . Objects may shield
two nodes from each other as shown in Figure2.7 because the
2. Research Methodology 34
additional at tenuation that they imply reduces the maximal
interference distance.
Figure 2.7 3-D Channel Model6
Thus, objects may cause two nodes to be disconnected, increasing the
probabil i ty of the hidden node problem.
2.5.1 Wireless channel models:
Channel models in MiXiM express radio propagation effects as t ime
variant factors of the Signal -to-Noise Ratio (SNR) g of the received
signal . Although such SNR-based models represents abstract behavior
of the exact signal behavior, and, thus, adjusting the required
accuracy by selecting the modeled effects and t ime -scale. On this
SNR-level , MIXIM already includes the following widely accepted
channel models for path-loss, shadowing, large and small -scale fading
as defined in i t .
6 A.Kopke Etl . omnet++ workshop ’08
2. Research Methodology 35
Physical layer models:
Physical layer implements modulation and Forward Error
Correction (FEC) coding and decoding functions define the bit error
rate and throughput of a system. The effect of these functions can be
modeled at SNR-level for wireless system.
FEC introduces a so-called coding gain at the receiver, which
can be expressed by a factor g to the SNR of the detected signal. This
coding gain depends on the used code, i ts rate Rc, and the employed
decoding algorithm . An encoded transmission has characterist ic of
g = 1, typical channel codes provide coding gains larger than 2 . This
SNR value compared to an SNR threshold in modeling transmission
errors in the decider module and a transmission error is assumed at
the receiver if g < thg . Typically, the SNR threshold thg calibrates
the system to stay below a given Packet Error Rate (PER) bound . I t is
selected well in advance depending on the receiver sensit ivity for the
chosen modulation scheme as per transceiver data shee t or
approximated. Selecting thresholds and coding gain independently per
terminal can model terminals employing different PHY parameters .
Furthermore, varying thresholds and coding gain over t ime support
rate adaptation .
SNR-based model easily extends to may type of receivers where
several channels are joined before the bit detection is made. Such
systems exploit differently faded channels and employ a fi l ter , l ike
Maximum Ratio Combining (MRC) to combine the signals received
2. Research Methodology 36
from L channels to a single signal used for bit detection This
combining model can be used to model diversity receivers combining
signals in different dimensions, e.g. OFDM subcarriers, or multi -
antenna systems.
2.5.2 Connection management
The Connection Manager module is responsible for establishing
connections between nodes that are within the range of maximal
interference distance of each other and tearing down these
connections once they not in range. The loss of connectivity can be
crashed node or due to mobil i ty ( i .e. the nodes move too far apart) or
due to a change in t ransmission power etc.
An important factor influencing the maximal interference distance to
objects within the l ine -of-sight of two nodes as shown in Figure cause
the attenuation. As we know that , each object has a frequency
dependent attenuation factor. All objects have to register with the
central authority for managing objects in the simulation (Object
Manager). The object manager implements a l ine segment intersection
method that checks whether a l ine connecting two points in the
propagation environment intersects with the borders of one or more
objects. The addit ional at tenuat ion is then determined by summing up
the frequency dependent attenuation factors of the objects intersecting
the l ine. The connection manager uses this value for adjusting the
maximal interference distance of two nodes. MIXIM supports multiple
2. Research Methodology 37
frequency ranges by means of mult iple connect ion managers ,
supporting Cognitive Radio Network .
As we know that , i t is possible to have multiple connection
managers in MIXIM. This enables the simulation of different
orthogonal spectrum ranges without compromising with performace in
term of memory consumption and computational complexity. I t is the
responsibil i ty of the NIC (and not the node) to register with the
desired connection manager. The respective connection managers wil l
connect only registered NICs .
Physical layer:
Physical layer is the central part of a node in MIXIM. I t is
responsible for sending and receiving messages, bit error calculation ,
and coll is ion detect ion . I t is also responsible for applying the channel
models used in the environment. Physical layer is divided into three
parts . The Base- PHY-Layer provides the interfaces to the MAC layer
and the physical layers of other communicating nodes . The Analogue
Models simulates the attenuation (l ike shadowing, fading and path
loss) of a received signal. The Decider is responsible for evaluation
of received signal (classification as noise or signal) and
demodulat ion (bit error calculation) of the received signal . To provide
a clear interface and to avoid memory overhead the analogue models
and the decider are implemented as pure C++ classes instead of
separate OMNET++ modules.
2. Research Methodology 38
The signal concept:
The environment i t travels through influences the signal
strength of a message sent from one node to another . I t is modeled
with attenuation factors caused by shadowing, path loss and fading. A
message can be sent using multiple frequencies (e.g. OFDM) and
using multiple antennas (MIMO). Adding up all those possibil i t ies, a
message can have varying, at tenuation , sending power and bit -rate
(modeling modulation and coding) in 3-D of t ime, space and
frequency. MIXIM has a signal class to model this complex process.
Each message has to attach i t self with a signal object representing,
at tenuation, sending power and bit -rate in the 3-D of t ime, frequency,
and space. An example for the sending power (TX) is shown in Figure
2.8. To send a message, a node has to specify the sending power and
bit-rate in the appropriate dimensions. The receiving node then adds
the attenuation. On the basis of whole signal , bit errors can be
calculated.
BasePhyLayer:
Apart from message sending and receiving, the Base PHY Layer
acts as an interface between Air Frames , the Analogue Model and the
Decider . For better flexibil i ty and modulari ty, different analogue
models and deciders can be used with the physical layer. After
receiving a message, the physical layer first passes the message to the
analogue model, which calculates the attenuation part of the signal.
The physical layer is responsible for simulating the propagation and
transmission delay of the message.
2. Research Methodology 39
Figure 2.8 Signal for sending power (TX)7
The message is passed at least twice to the decider: at the start of
message and at the end of the message. The decider can also request
to get the message at arbitrary t imes in between. After the decider
calculates the bit errors, the message has to be handed to the MAC
layer.
7 A.Kopke Etl . omnet++ workshop ’08
2. Research Methodology 40
The physical layer stores all messages in to a class called the
Channel Info class. The Channel Info class act as service provider
that keeps track of all Air Frames on the channel. Channel Info
provides a function that returns all Air Frames intersecting with a pre
determined t ime interval. The decider uses this function in order to
calculate the SNR of a given signal .
Analogue models:
The receiving power of a received message is a given by
function f : Rn R from 3-D of t ime, frequency and space to
receiving power. MIXIM has to simulate features l ike path loss,
shadowing and fading.
Each of these attenuation sources can be represented by another
function a i : Rn R from 3-D of t ime, frequency and space to
attenuation. The attenuation of a signal is calculated by
implementations of shadowing, fading and path-loss models. Any
arbitrary number of analogue model s can be plugged into the physical
layer. Each analogue model is basically a fi l ter class for signals.
Summed attenuation of all analogue models g ives the
attenuation part of al l signal, which is calculated at the start of the
reception of a message. Together with the sending power of a
received packet the decider can later on calculate the SNR and bit
errors.
Decider:
The Decider has three main tasks to do . First one , the decider
has to classify and decided on incoming messages into receivable
2. Research Methodology 41
messages or noise. Second one , at the end of receiving a receivable
message, the decider has to calculate the BER for the message. At last
i t has to provide information about the curren t state of the channel .
There are several models for determining how and when a
physical layer decides whether a message can be received or is just
noise. The decider in MiXiM supports al l of these models. After
arrival of a message, the physical layer pa sses the message to the
decider module. The decider can decide right away whether to treat
the message as noise or not, or i t can request the physical layer to
resubmit the packet after a certain t ime. This concept even enables
the decider to revise i ts decision (e.g. if a second, much stronger
message arrived in the meantime).
The maximum time that the decider can reques t the message
from the physical layer is at the end of the receiving process of the
message. This is also the t ime when the decider has to calculate the
BER for the message. In order to do so, i t requests al l intersecting
messages from the Channel Info in order to calculate the SNR for the
received message. I t then can either make a simple binary decision
(received correctly or not) or i t can calculate BER and posit ions,
depending on the complexity of the particular decider model.
The last task of the decider is to provide information about the
channel s tate. This channel st ate is needed at the MAC layer. The
MAC layer can request the decider to sense the channel for a certain
amount of t ime. The decider then returns state of the channel.
2. Research Methodology 42
2.6 MiXiM PROTOCOL LIBRARY:
MIXIM allows every module in the simulation to be replaced by
another module, adding or overriding functionali ty to the base
implementation. For some of these modules there is already a wide
choice of implemented protocols available.
2.6.1 MAC protocols:
A Medium Access Control (MAC) protocol is designed to take
decisions on the sharing of a medium for communication between
nodes. In wireless systems, that shared medium is the air . A MAC
protocol needs to conclude when a node should send out messages,
such that the messages do not interfere with messages of other nodes.
MIXIM provides a wide variety of different MAC protocols
encompassing a significant proportion of the current design space for
MACs from 802.11 to 802.16 .
2.6.2 Network layer protocols:
MIXIM supports different networking protocols for a wide
variety of traffic paradigms (source -to-sink; any-to-any; local
neighborhood; etc), and these are further supported by the other
simulation modules, protocols. MIXIM is currently being used to tes t
the Ley Line Routing protocol .
2. Research Methodology 43
Mobility models
MIXIM has a r ich l ibrary of mobili ty modules implemented,
which includes simple modules l ike “constant speed mobili ty” and
“circle mobili ty”, but also modules that parse ANSim trace fi les and
Bonn Motion fi les . I t is also support custom and user defined
mobili ty modules, by sub-classing from the Base Mobili ty class. The
Base Mobili ty class provides all the functionali ty needed for mobili ty
handling in MIXIM only the specific mobili ty pattern has to be
implemented in order to create a new mobili ty module.
2.7 Conclusions:
In early days network are analyzed on physically developed
tested, but due to high cost and scarcity of resource i t could not
widespread. Now a days due to advent of high end ICT technique i t is
possible to have virtual labs with support of real t ime remot e access
to ICT enable research equipment or emulator, simulator based
research . In same line research on protocol development for
cognitive radio networks were carried out with SDR and OMNET++
simulation tools .In which OMNET++ has rich set of wireless l ibrary
with support of MIXed Simulator (MiXiM) . MiXiM has support for
modeling 2D as well as 3D networks . I t also has support for TDM to
CDMA access mechanism. Physical modulation l ike PSK to OFDM
are also supported by the simulator . Through full f eature development
platform l ike OMNET++ gives near l ike simulation environment for
providing 3D wave propagation. The simulation setup include specific
2. Research Methodology 44
feature for Indian climatic conditions. SDR are also used to gain some
inside story on reconfigurable r adio. The SDR also gives first hand
idea of various waveform and physical activity of CRN(Cognitive
Radio Network).