(part-2)Book

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Chapter-1
Introduction
1.1. Introduction
During the past few years, there has been an explosion in wireless technology.This
growth has opened a new dimension to future wireless communications whose ultimate goal is to
provide universal personal and multimedia communication without regard to mobility or location
with high data rates. To achieve such an objective, the next generation personal communication
networks will need to be support a wide range of services which will include high quality voice,
data, facsimile, still pictures and streaming video. These future services are likely to include
applications which require high transmission rates of several Megabits per seconds (Mbps).
Channel estimation is an important technique especially in mobile wireless network
systems where the wireless channel changes over time, usually caused by transmitter and/or
receiver being in motion at vehicular speed. Mobile wireless communication is adversely
affected by the multipath interference resulting from reflections from surroundings, such as hills,
buildings and other obstacles. In order to provide reliability and high data rates at the receiver,
the system needs an accurate estimate of the time-varying channel. Furthermore, mobile wireless
systems are one of the main technologies which used to provide services such as data
communication, voice, and video with quality of service (QoS) for both mobile users and
nomadic. The knowledge of the impulse response of mobile wireless propagation channels in the
estimator is an aid in acquiring important information for testing, designing or planning wireless
communication systems.
Vehicular ad-hoc network removes the dependence on cellular network for vehicle-to-
vehicle communication system. Public safety is also another part of V2V communication.The
V2V system needs to support at least one wireless local area network technology to support non-
safety applications, e.g., IEEE 802.11a/b/g. In contrast to non-safety applications, safety
applications are usually of broadcast nature. Safety applications are supported by specific V2V
network and transport protocols, and are normally based on IEEE 802.11p. The IEEE 802.11p

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radio technology is directly derived from IEEE 802.11a with some modifications to adapt to
vehicular environments. It occupies 75 MHz of the licensed spectrum, from 5.85 to 5.925 GHz is
used as part of the intelligent transportation system for dedicated short range communications
(DSRC) in the USA. The IEEE 802.11p, Wireless Access in Vehicular Environment (WAVE)
standardization process originates from the allocation of the Dedicated Short Range
Communications (DSRC) spectrum band in the United States and the effort to define the
technology for usage in the DSRC band.
1.2. Literature Survey
Jones have introduced adaptive filters through the example of system identification using
the LMS algorithm. Haykin discussed the concept of the adaptive filter algorithms that are
implemented with FIR filter structures and their variety of applications in those systems where
minimal information is available about the incoming signal[1]. Vanderveen have focused on the
joint estimation of angles and relative delays of multipath propagation signals emanating from a
single source and received by a single antenna array. Rontogiannis have proposed a parametric
method for estimating the unknown multipath channel impulse response (CIR) in a semi-blind
manner. An approach for estimating the model parameters based on sample covariance from data
disturbed by discrete-time measurement noise has been proposed for large-scale fading channels
in wireless communication systems. A generalized RAKE (G-RAKE) receiver is proposed for
suppressing intra cell interference in the downlink of a DS-CDMA system employing orthogonal
codes. Wei have proposed a new kind of Rake receiver based on modified Kalman filter
algorithm (MKFA). This kind of receiver, simultaneously considers the channel gain factor and
the noise time-variable statistics characteristic, which can speed up the convergence rate and
enhance the track performance of the algorithm. Olama have proposed an algorithm which
consists of filtering based on the Kalman filter to remove noise from data, and identification
based on the filter-based expectation maximization (EM) algorithm to determine the parameters
of the model which best describe the measurements.

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1.3. Digital Communication Systems
A digital communication system is often divided into several functional units. The task of
the source encoder is to represent the digital or analog information by bits in an efficient way.
The bits are then fed into the channel encoder, which adds bits in a structured way to enable
detection and correction of transmission errors. The bits from the encoder are grouped and
transformed to certain symbols, or waveforms by the modulator and waveforms are mixed with a
carrier to get a signal suitable to be transmitted through the channel. At the receiver the reverse
function takes place. The received signals are demodulated and soft or hard values of the
corresponding bits are passed to the decoder. The decoder analyzes the structure of received bit
pattern and tries to detect or correct errors. Finally, the corrected bits are fed to the source
decoder that is used to reconstruct the analog speech signal or digital data input.. The main
question is how to design certain parts of the modulator and demodulator to achieve efficient and
robust transmission through a mobile wireless channel. The wireless channel has some properties
that make the design especially challenging: it introduces time varying echoes and phase shifts as
well as a time varying attenuation of the amplitude (fade).
1.4. Evolution of Telecommunication Systems
Many mobile radio standards have been developed for wireless systems throughout the
world, with more standard likely to emerge. Most first generations systems were introduced in
the mid 1980s, and can be characterized by the use of analog transmission techniques, and the
use of simple multiple access techniques such as Frequency Division Multiple Access (FDMA).
First generation telecommunications systems such as Advanced Mobile Phone Service (AMPS),
only provided voice communications. They also suffered from a low user capacity, and security
problems due to the simple radio interface used.
Second generation systems were introduced in the early 1990s, and all use digital technology.
This provided an increase in the user capacity of around three times. This was achieved by
compressing the voice waveforms before transmission.
Third generation systems are an extension on the complexity of second generation systems and
are already introduced. The system capacity is expected to be increased to over ten times original
first generation systems. This is going to be achieved by using complex multiple access

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techniques such as Code Division Multiple Access (CDMA), or an extension of TDMA, and by
improving flexibility of services available.
1.5. Objective and Outline of Thesis
The principle objective of this work is to enhance the knowledge about channel
estimation and to compare the existing channel estimation techniques under different channel
conditions with different modulation techniques. Normally the received signal is corrupted by
the channel. The estimation of a time-varying multipath fading channel is a difficult task for the
receiver. Its performance can be improved if an appropriate channel estimation filter is used
according to the prior knowledge of the fading channel. In this work two popular estimation
algorithms, LMS and RLS are studied with respect to AWGN, Rician and Rayleigh channels.
The simulation is performed by MATLAB SIMULINK.
The main objectives of this thesis are:
 Compare these algorithms about there characteristics in case of mean error in different
channel models and modulation techniques.
 To compare, which algorithm is more reliable in case of error rate calculation.
This thesis is organized as follows: In Chapter 2, the description of channel estimation; In
Chapter3, the description of generalized channel and their characteristics; In Chapter 4,the
modulation techniques; Chapter 5,demonstrates Simulations and Results; Chapter 6 concludes
the thesis and future work is also suggested.

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Chapter-2
Channel Estimation
2.1. Introduction
Channel in its most General sense can describe everything from the source to the sink of
the radio signal.
A channel is a medium, which transfer data or information from transmitter to receiver.
Channels include the physical medium like free space, fiber, waveguides etc. The features of any
physical medium is that, the transmitted signal is corrupted in various way by frequency and
phase distortion, inter symbol interference, thermal noise etc and the receiver receives the
corrupted signal. In this work ―Channel‖ refers to the physical medium.
Channel Model is a mathematical representation of the transfer characteristics of the physical
medium. Channel models are formulated by observing the characteristics of the received signal.
The one that best explains the received signal behavior is used to model the channel.
Estimation means prediction, detection or approx calculation. Channel estimation is simply
defined as the process of characterizing the effect of the physical channel on the input sequence.
We can say a channel is well estimated when its error minimization criteria is satisfied . Channel
estimation gives the basic idea of the effect of the physical channel on the input sequence of the
receiver. The error can be minimized by equalization technique. It helps to produce a channel to
ideal channel when voice, data and video can pass through the channel. Channel estimation
algorithms explain the behavior of the channel and allow the receiver to approximate the impulse
response of the channel[2].
Signal detection algorithms require the knowledge of channel impulse response, which is
usually estimated by using the known training symbols in the middle of the transmission burst. In
mobile environment the channel is time-variant, which makes the estimation task more difficult.
In the GSM system and its derivatives the time period between the bursts is so long that the
channel changes significantly from burst to burst and thus a separate channel estimation is
needed for each burst. On the other hand the change during the burst for slowly moving mobiles
is rather limited, hence it is reasonable to assume block fading channel characteristics, i.e., the

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channel is constant during the burst, but is changing between them . In this section we consider
adaptive linear filter approach by presenting Recursive Least Squares (RLS) solution for the
parameter estimation. Then estimation in the presence of feedback information is discussed and
finally extension to multiple channel estimation is considered[5].
Figure-2.1: General channel estimation procedure[5]
Channel estimation is based on the training sequence of bits and which is unique for a
certain transmitter and which is repeated in every transmitted burst. The channel estimator gives
the knowledge on the channel impulse response (CIR) to the detector and it estimates separately
the CIR for each burst by exploiting transmitted bits and corresponding received bits. Signal
detectors must have knowledge concerning the channel impulse response (CIR) of the radio link
with known transmitted sequences, which can be done by a separate channel estimator. The
modulated corrupted signal from the channel has to be undergoing the channel estimation using
LMS, MLSE, MMSE, RMS etc before the demodulation takes place at the receiver side[7]. The
channel estimator is shown in figure 2.2.
Error
Signal
e(n)
Actual
Received Signal
Channel
Estimated
Channel
Model
Estimation Algorithm
+
Estimated
Signal
)(ˆ nY
)(nY
Transmitted sequence
+
-
)(nx

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Figure 2.2 : The block diagram of the channel estimator [7]
 A channel estimate is only a mathematical estimation of what is truly happening in nature.
 Aim of any channel estimation procedure:
 Minimize some sort of criteria, e.g. MSE.
 Utilize as little computational resources as possible allowing easier implementation.
 Why Channel Estimation?
 Allows the receiver to approximate the effect of the channel on the signal.
 The channel estimate is essential for removing inter symbol interference, noise rejection
techniques etc.
 Also used in diversity combining, ML detection, angle of arrival estimation etc.
2.2. Channel Estimation Techniques
A wideband radio channel is normally frequency selective and time variant.For an
OFDM mobile communication system, the channel transfer function at different subcarriers
appears unequal in both frequency and time domains. Therefore, a dynamic estimation of the
channel is necessary. Pilot-based approaches are widely used to estimate the channel properties

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and correct the received signal. In this chapter we have investigated two types of pilot
arrangements[3].
Figure-2.3 Block type pilot arrangement[3] Figure-2.4 Comb type pilot arrangement[3]
The first kind of pilot arrangement shown in Figure 2.3 is denoted as block-type pilot
arrangement. The pilot signal assigned to a particular OFDM block, which is sent periodically in
time-domain. This type of pilot arrangement is especially suitable for slow-fading radio
channels. Because the training block contains all pilots, channel interpolation in frequency
domain is not required. Therefore, this type of pilot arrangement is relatively insensitive to
frequency selectivity. The second kind of pilot arrangement shown in Figure 2.4 is denoted as
comb-type pilot arrangement. The pilot arrangements are uniformly distributed within each
OFDM block. Assuming that the payloads of pilot arrangements are the same, the comb-type
pilot arrangement has a higher re-transmission rate. Thus the comb-type pilot arrangement
system is provides better resistance to fast-fading channels. Since only some sub-carriers contain
the pilot signal, the channel response of non-pilot sub-carriers will be estimated by interpolating
neighboring pilot sub-channels. Thus the comb-type pilot arrangement is sensitive to frequency
selectivity when comparing to the block-type pilot arrangement system.
2.2.1 Channel Estimation Based on Block-Type Pilot Arrangement
In block-type pilot based channel estimation, OFDM channel estimation symbols are transmitted
periodically, in which all sub-carriers are used as pilots. If the channel is constant during the
block, there will be no channel estimation error since the pilots are sent at all carriers. The

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estimation can be performed by using either LSE or MMSE .If inter symbol interference is
eliminated by the guard interval, we write in matrix notation[3]:
Y=XFh+ W
= XH +W ……………………….2.1
2.2.2 Channel Estimation Based on Comb-Type Pilot Arrangement
In comb-type based channel estimation, the Np pilot signals are uniformly inserted
into X(k) according to following equation:
……………….2.2
L = number of carriers/Np
xp(m) is the mth pilot carrier value.
We define {Hp(k) k = 0, 1, . . . Np} as the frequency response of the channel at pilot sub-carriers.
The estimate of the channel at pilot sub-carriers based on LS estimation is given by:
…………………..2.3
Yp(k) and Xp(k) are output and input at the k th pilot sub-carrier respectively[3].

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2.3. Channel Estimation Algorithm
Mainly two types of adaptive algorithms are used in channel estimation purpose. The
algorithms are Least-Mean Square (LMS) & Recursive Least-Squares (RLS).
2.3.1 Least-Mean Square (LMS) Algorithm
LMS algorithm uses the estimates of the gradient vector from the available data.
LMS incorporates an iterative procedure that makes successive corrections to the
weight vector in the direction of the negative of the gradient vector which eventually
leads to the minimum mean square error. Compared to other algorithms LMS
algorithm is relatively simple.
Input: A random process x(n);
FIR filter of weight: (w0, w1…wN-1);
Filter output:Y(n)=wT
x(n) ;
Error signal:d(n)-y(n) ;Where d(n) is the desired output.
From the method of steepest descent, the weight vector equation is given by:
W (n) = W (n) +1/ 2[-(E{e2
(n)}] ………………………..2.4
Where μ is the step-size parameter and controls the convergence characteristics of the LMS
algorithm[4]. In the method of steepest descent the biggest problem is the computation involved
in finding the values r and R matrices in real time. The LMS algorithm on the other hand
simplifies this by using the instantaneous values of covariance matrices r and R instead of their
actual values i.e.
…………………………………….2.5
……………………………………..2.6
Therefore the weight update can be given by the following equation:
w(n +1) = w(n) + x(n)[d *
(n) - xT
(n)w(n)] = w(n) + x(n)e*
(n) ………….2.7
R(n) = x(n)xT
(n)
r(n) = d *
(n)x(n)

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………………………….2.8
…………………………2.9
Equation number (2.6) & (2.9) are respectively known as weight update & filtering operation
equation.
2.3.2 Recursive Least Square (RLS) Algorithm
The Recursive least squares (RLS) adaptive filter is an algorithm which recursively finds the
filter coefficients that minimize a weighted linear least squares cost function relating to the input
signals. This is in contrast to other algorithms such as the least mean squares (LMS) that aim to
reduce the mean square error[8][9].
The RLS algorithm for a p-th order RLS filter can be summarized as, Parameters:
p = Filter order
= Forgetting factor
= Value of initialize P(0)
Initialization: wn = 0
P(0) =  -1
I Where I is the (p+1)-by-(p+1) identity matrix Computation: For n= 0,1,2,…….
Then the weight update can be given by the following equation:
w(n) = w(n -1) +(n)g(n) ……………………………..2.10
e(n) = d(n) - y(n) [n = 0 to final ]
Y (n) = wT
(n)x(n)

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Chapter-3
Communication Channel
3.1. Introduction
There are three basic types of channels considered for this work. The vehicle-to-vehicle
(V2V) channels estimation are compared with cellular channels. Performance of three channels,
viz., AWGN, Rayleigh Fading Channel, Rician Fading Channel in V2V communication
environment is evaluated through simulation.
Multipath fading is a significant problem in communications. In a fading channel, signals
experience fades (i.e., they fluctuate in their strength). When the signal power drops
significantly, the channel is said to be in a fade. This gives rise to high bit error rates (BER).
3.2. Channel Models
3.2.1. AWGN Channel:
An Additive white Gaussian noise (AWGN) channel adds white Gaussian noise to the
signal, when the signal passes through it. In this channel model the only impairment to
communication is a linear addition of wideband or white noise with a constant spectral density
and a Gaussian distribution of amplitude. Fading, frequency selectivity, interference, nonlinearity
or dispersion are not the part of AWGN model. It generates simple and tractable mathematical
models. Those models are useful for gaining insight into the underlying behavior of a system
before these other phenomena are considered.
In case for many satellite and deep space communication links, the AWGN model is very good.
This model is not useful for most terrestrial links because of multipath, terrain blocking,
interference, etc. However AWGN is used to simulate background noise of the channel under
study, in addition to multipath, terrain blocking, interference, ground clutter, etc[8][10].

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Figure-3.1: AWGN channels at least one existing LOS[8].
The AWGN channel is represented by a series of outputs Si at discrete time event index i. Si is
the sum of the input Ri and noise, Qi, where Qi is independent and identically-distributed and
drawn from a zero-mean normal distribution with variance n (the noise). The Qi are further
assumed to not be correlated with the Xi.
Qi ≈N(0.n) Si =Ri + Qi …………………………….3.1
The channel capacity C for the AWGN channel is given by:
C=1/2 log(1+𝑃/𝑛) ……………………………3.2
Where P = maximum channel power.
3.2.2 Rician Fading Channel:
Rician fading is a stochastic model. It is used for radio propagation anomaly caused by
partial cancellation of a radio signal by itself, the signal arrives at the receiver by several
different paths (hence exhibiting multipath interference), and at least one of the paths is changing
(lengthening or shortening). Rician fading model applicable where one dominant propagation
along a line of sight between the transmitter and receiver; typically a line of sight (LOS) signal is
much stronger than the others signal. In Rician fading, the amplitude gain is characterized by a
Rician distribution[8][11].

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K and Ω are the two parameters of Rician fading channel. K is the ratio between the
power in the direct path and the power in the other, scattered, paths. Ω is the power in the direct
path. The received signal amplitude (not the power of the received signal) R is then Rice
distributed.
……………………3.3
Figure-3.2: Rician Fading Channel with existing one LOS[8].
3.2.3. Rayleigh Fading Channel:
Rayleigh fading channel is a statistical model. It assumes the magnitude of a signal. This
model is used for the effect of a propagation environment on a radio signal, such as that used by
wireless devices.
When the signal has passed through such a transmission medium (communications channel) will
vary randomly, or fade, according to a Rayleigh distribution — the radial component of the sum
of two uncorrelated Gaussian random variables.

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Rayleigh fading is viewed as a sensible model for tropospheric and ionospheric signal
propagation and it is used for the effect of heavily built-up urban environments on radio signals.
If there is no dominant propagation along a line of sight between the transmitter and receiver,
there Rayleigh fading model is applicable. In case of one dominant line of sight, Rician fading
may be more applicable.
Rayleigh fading is a sensible model when there are many objects in the environment that scatter
the radio signal before it arrives at the receiver. If there is sufficiently much scatter, the channel
impulse response will be well-modeled as a Gaussian process irrespective of the distribution of
the individual components. Transmitted signal of Rayleigh fading model is affected by
multipath. If there is no dominant component to the scatter, then such a process will have zero
mean and phase evenly distributed between 0 and 2π radians. The envelope of the channel
response will therefore be Rayleigh distributed[8][12].
Calling this random variable R, it will have a probability density function:
PR(r) = r>=0 …………………………….3.4
Where 𝜴 = E(R2
).
Figure-3.3: Rayleigh Fading Channel with no existing LOS[8].

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3.3. Channel in Intelligent Transport Systems (ITS)
The development of the future V2V and Vehicle-to- Infrastructure (V2I) communications
systems imposes strong radio channel management challenges due to their decentralized nature
and the strict Quality of Service (QoS) requirements of traffic safety applications[8].
In ITS channel, scattering can occur around both the TX and the RX, on the other hand
base station is usually free of scatter.
The distance over which communications can take place is much smaller in ITS channels
(< 100 m) than in typical cellular scenarios (~ 1 km).
In cellular communication only Tx or Rx is moving, for ITS both are moving.
ITS operates most high carrier frequency (5.8- 5.9GHz), whereas Cellular
communication operates mostly 700-2400MHz.
The ITS ad-hoc communications are peer-to-peer communications, thus the transmitter
and receiver are at the same height and the same environment. On the other hand in
cellular communication the base station is high above the street level and the mobile
station is at the street level. Thus the dominant propagation mechanisms of the multipath
components are different.
3.4. Propagation Characteristics of Channels
For an ideal radio channel, the received signal would consist of only a single directpath
signal, which would be a perfect reconstruction of the transmitted signal.However in a real
channel, the signal is modified during transmission in the channel. The received signal consists
of a combination of attenuated, reflected, refracted, and diffracted replicas of the transmitted
signal. On top of all this, the channel adds noise to the signal and can cause a shift in the carrier
frequency if the transmitter or receiver is moving (Doppler effect). Understanding of these
effects on the signal is important because the performance of a radio system is dependent on the
radio channel characteristics[3].

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3.4.1. Attenuation
Attenuation is the drop in the signal power when transmitting from one point to the
another. It can be caused by the transmission path length, obstructions in the signal path and
multipath effects. Any objects, which obstruct the line of sight signal from the transmitter to the
receiver, can cause attenuation. Shadowing of the signal can occur whenever there is an
obstruction between the transmitter and receiver. It is generally caused by buildings and hills,
and is the most important environmental attenuation factor.
Figure-3.4: Attenuation of signal[3].
Shadowing is most severe in heavily built up areas, due to the shadowing from buildings.
However, hills can cause a large problem due to the large shadow they produce. Radio signals
diffract off the boundaries of obstructions, thus preventing total shadowing of the signals behind
hills and buildings. However, the amount of diffraction is dependent on the radio frequency used,
with low frequencies diffracting more than high frequency signals. Thus, high frequency signals,
especially, Ultra High Frequencies (UHF), and microwave signals require line of sight for
adequate signal strength. To overcome the problem of shadowing, transmitters are usually
elevated as high as possible to minimise the number of obstructions[3].

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3.4.2 Frequency Selective Fading
In any radio transmission, the channel spectral response is not flat. It has dips or fades in
the response due to reflections causing cancellation of certain frequencies at the receiver.
Reflections off near-by objects (e.g. ground, buildings, trees, etc) can lead to multipath signals of
similar signal power as the direct signal. This can result in deep nulls in the received signal
power due to destructive interference.For narrow bandwidth transmissions if the null in the
frequency response occurs at the transmission frequency then the entire signal can be lost. This
can be partly overcome in two ways. By transmitting a wide bandwidth signal or spread
spectrum as CDMA, any dips in the spectrum only result in a small loss of signal power, rather
than a complete loss. Another method is to split the transmission up into many small bandwidth
carriers, as is done in a COFDM/OFDM transmission. The original signal is spread over a wide
bandwidth and thus, any nulls in the spectrum are unlikely to occur at all of the carrier
frequencies. This will result in only some of the carriers being lost, rather than the entire signal.
The information in the lost carriers can be recovered provided enough forward error corrections
is sent[3].
3.4.3 Delay Spread
The received radio signal from a transmitter consists of typically a direct signal,plus
reflections of object such as buildings, mountings, and other structures. The reflected signals
arrive at a later time than the direct signal because of the extra path length, giving rise to a
slightly different arrival time of the transmitted pulse, thus spreading the received energy. Delay
spread is the time spread between the arrival of the first and last multipath signal seen by the
receiver. In a digital system, the delay spread can lead to inter-symbol interference. This is due to
the delayed multipath signal overlapping with the following symbols. This can cause significant
errors in high bit rate systems, especially when using time division multiplexing (TDMA). As the
transmitted bit rate is increased the amount of inter symbol interference also increases. The effect
starts to become very significant when the delay spread is greater than ~50% of the bit time[3].

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Figure-3.5: Delay spread[3].
3.4.4. Doppler Shift
When a wave source and a receiver are moving relative to one another the frequency of
the received signal will not be the same as the source. When they are moving toward each other
the frequency of the received signal is higher than the source, and when they are approaching
each other the frequency decreases. This is called the Doppler’s effect. An example of this is the
change of pitch in a car’s horn as it approaches then passes by. This effect becomes important
when developing mobile radio systems.
The amount the frequency changes due to the Doppler effect depends on the relative motion
between the source and receiver and on the speed of propagation of the wave. The Doppler shift
in frequency can be written
Δ f ≈ +- f 0 v/c ……………………………….3.5
where f is the change in frequency of the source seen at the receiver, fo is the frequency of the
source, v is the speed difference between the source and transmitter, and c is the speed of light.
Doppler shift can cause significant problems if the transmission technique is sensitive to carrier
frequency offsets or the relative speed is higher, which is the case for OFDM. If we consider
now a link between to cars moving in opposite directions, each one with a speed of 80 km/hr, the
Doppler shift will be double[3].

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Chapter-4
Modulation Techniques
4.1. Introduction
Modulation is a process of mixing a signal with a sinusoid to produce a new signal. In
electronics and telecommunications, modulation is the process of varying one or more properties
of a high-frequency periodic waveform, called the carrier signal, with a modulating signal which
typically contains information to be transmitted. This is done in a similar fashion to a musician
modulating a tone (a periodic waveform) from a musical instrument by varying its volume,
timing and pitch. The three key parameters of a periodic waveform are its amplitude ("volume"),
its phase ("timing") and its frequency ("pitch"). Any of these properties can be modified in
accordance with a low frequency signal to obtain the modulated signal. Typically a high-
frequency sinusoid waveform is used as carrier signal, but a square wave pulse train may also be
used This new signal, conceivably, will have certain benefits of an un-modulated signal,
especially during transmission.
If we look at a general function for a sinusoid:
……………………….4.1
we can see that this sinusoid has 3 parameters that can be altered, to affect the shape of the
graph. The first term, A, is called the magnitude, or amplitude of the sinusoid. The next term, is
known as the frequency, and the last term, is known as the phase angle. All 3 parameters can
be altered to transmit data.
The sinusoidal signal that is used in the modulation is known as the carrier signal, or simply
"the carrier". The signal that is used in modulating the carrier signal(or sinusoidal signal) is
known as the "data signal" or the "message signal". It is important to notice that a simple
sinusoidal carrier contains no information of its own.
In other words we can say that modulation is used because the some data signals are not always
suitable for direct transmission, but the modulated signal may be more suitable.
In telecommunications, modulation is the process of conveying a message signal, for example a
digital bit stream or an analog audio signal, inside another signal that can be physically

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transmitted. Modulation of a sine waveform is used to transform a baseband message signal into
a passband signal, for example low-frequency audio signal into a radio-frequency signal (RF
signal).
4.1.1. Why Need Modulation?
Clearly the concept of modulation can be a little tricky, especially for the people who
don't like trigonometry. Why then do we bother to use modulation at all? To answer this
question, let's consider a channel that essentially acts like a bandpass filter: The lowest frequency
components and the highest frequency components are attenuated or unusable, in some way. If
we can't send low-frequency signals, then we need to shift our signal up the frequency ladder.
Modulation allows us to send a signal over a bandpass frequency range. If every signal gets its
own frequency range, then we can transmit multiple signals simultaneously over a single
channel, all using different frequency ranges.
Another reason to modulate a signal is to allow the use of a smaller antenna. A baseband (low
frequency) signal would need a huge antenna because in order to be efficient, the antenna needs
to be about 1/10th the length of the wavelength. Modulation shifts the baseband signal up to a
much higher frequency, which has much smaller wavelengths and allows the use of a much
smaller antenna.
4.1.2. Aim of Using Modulation
The aim of digital modulation is to transfer a digital bit stream over an analog bandpass
channel, for example over the public switched telephone network (where a bandpass filter
limits the frequency range to between 300 and 3400 Hz), or over a limited radio
frequency band.
The aim of analog modulation is to transfer an analog baseband (or lowpass) signal, for
example an audio signal or TV signal, over an analog bandpass channel at a different
frequency, for example over a limited radio frequency band or a cable TV network
channel.

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Analog and digital modulation facilitate frequency division multiplexing (FDM), where
several low pass information signals are transferred simultaneously over the same shared
physical medium, using separate passband channels (several different carrier
frequencies).
The aim of digital baseband modulation methods, also known as line coding, is to transfer
a digital bit stream over a baseband channel, typically a non-filtered copper wire such as
a serial bus or a wired local area network.
The aim of pulse modulation methods is to transfer a narrowband analog signal, for
example a phone call over a wideband baseband channel or, in some of the schemes, as a
bit stream over another digital transmission system.
In music synthesizers, modulation may be used to synthesise waveforms with an
extensive overtone spectrum using a small number of oscillators. In this case the carrier
frequency is typically in the same order or much lower than the modulating waveform.
See for example frequency modulation synthesis or ring modulation synthesis.

23
4.2. Modulation Techniques
4.2.1. Binary Phase-Shift Keying (BPSK)
The simplest form of PSK is binary phase-shift keying (BPSK), where N = 1 and M = 2.
Therefore, with BPSK, two phases (21
= 2) are possible for the carrier.
One phase repre¬sents a logic 1, and the other phase represents a logic 0. As the input digital
signal changes state (i.e., from a 1 to a 0 or from a 0 to a 1), the phase of the output carrier shifts
between two angles that are separated by 180°.
Hence, other names for BPSK are phase reversal keying (PRK) and biphase modulation. BPSK
is a form of square-wave modulation of a continuous wave (CW) signal.
The balanced modulator acts as a phase reversing switch. Depending on the logic condition of
the digital input, the carrier is transferred to the output either in phase or 180° out of phase with
the reference carrier oscillator.
Figure 2-13 shows the schematic diagram of a balanced ring modulator.
The balanced modulator has two inputs: a carrier that is in phase with the reference oscillator and
the bi¬nary digital data.
For the balanced modulator to operate properly, the digital input voltage must be much greater
than the peak carrier voltage.
This ensures that the digital input con¬trols the on/off state of diodes D1 to D4. If the binary
input is a logic 1(positive voltage), diodes D 1 and D2 are forward biased and on, while diodes
D3 and D4 are reverse biased and off (Figure 2-13b). With the polarities shown, the carrier
voltage is developed across transformer T2 in phase with the carrier voltage across T 1.
Consequently, the output signal is in phase with the reference oscillator.

24
If the binary input is a logic 0 (negative voltage), diodes Dl and D2 are reverse biased and off,
while diodes D3 and D4 are forward biased and on (Figure 9-13c). As a result, the carrier voltage
is developed across transformer T2 180° out of phase with the carrier voltage across T 1.
Figure- 4.1 (a) Balanced ring modulator; (b) logic 1 input; (c) logic 0 input.

25
Figure-4.2: BPSK modulator: (a) truth table; (b) phasor diagram; (c) constellation
diagram.
In a BPSK modulator. the carrier input signal is multiplied by the binary data.
If + 1 V is assigned to a logic 1 and -1 V is assigned to a logic 0, the input carrier
(sin ωct) is multiplied by either a + or - 1 .
The output signal is either + 1 sin ωct or -1 sin ωct the first represents a signal that
is in phase with the reference oscillator, the latter a signal that is 180° out of phase
with the reference oscillator.

26
Each time the input logic condition changes, the output phase changes.
Mathematically, the output of a BPSK modulator is proportional to
BPSK output = [sin (2πfat)] x [sin (2πfct)] (2.20)
where
fa = maximum fundamental frequency of binary input (hertz)
fc = reference carrier frequency (hertz)
4.2.2. Quaternary Phase-Shift Keying (QPSK)
QPSK is an M-ary encoding scheme where N = 2 and M= 4 (hence, the name
"quaternary" meaning "4"). A QPSK modulator is a binary (base 2) signal, to produce four
different input combinations,: 00, 01, 10, and 11.
Therefore, with QPSK, the binary input data are combined into groups of two bits, called dibits.
In the modulator, each dibit code generates one of the four possible output phases (+45°,
+135°, -45°, and -135°).
A block diagram of a QPSK modulator is shown in Figure 4.3. Two bits (a dibit) are
clocked into the bit splitter. After both bits have been serially inputted, they are
simultaneously parallel outputted.
The I bit modulates a carrier that is in phase with the reference oscillator (hence the name
"I" for "in phase" channel), and the Q bit modulate, a carrier that is 90° out of phase.
For a logic 1 = + 1 V and a logic 0= - 1 V, two phases are possible at the output of the I
balanced modulator (+sin ωct and - sin ωct), and two phases are possible at the output of the
Q balanced modulator (+cos ωct), and (-cos ωct).

27
When the linear summer combines the two quadrature (90° out of phase) signals, there are
four possible resultant phasors given by these expressions: + sin ωct + cos ωct, + sin ωct -
cos ωct, -sin ωct + cos ωct, and -sin ωct - cos ωct.
Figure-4.3: QPSK modulator

28
Figure-4.4: QPSK modulator: (a) truth table; (b) phasor diagram; (c) constellation diagram
In Figures 4.4b and c, it can be seen that with QPSK each of the four possible output phasors
has exactly the same amplitude. Therefore, the binary information must be encoded entirely
in the phase of the output signal.
In Figure 4.4b, it can be seen that the angular separation between any two adjacent phasors in
QPSK is 90°.
Therefore, a QPSK signal can undergo almost a+45° or -45° shift in phase during
transmission and still retain the correct encoded information when demodulated at the
receiver.
With QPSK, because the input data are divided into two channels, the bit rate in either the I or
the Q channel is equal to one-half of the input data rate (fb/2) (one-half of fb/2 = fb/4).
the I or Q balanced modulator is an alternative 1/0 pattern, which occurs when the binary

29
input data have a 1100 repetitive pattern. One cycle of the fastest binary transition (a 1/0
sequence in the I or Q channel takes the same time as four input data bits).
Consequently, the highest fundamental frequency at the input and fastest rate of change at
the output of the balance.: modulators is equal to one-fourth of the binary input bit rate.
The output of the balanced modulators can be expressed mathematically as
(2.22)
where

30
Chapter-5
SIMULATION AND RESULTS
5.1. Introduction
In this chapter I show my simulation process for this work.
5.2. For BPSK Modulation
From this simulation of all the adaptive filters (LMS & RLS) comparison we observed that the
RLS exhibits minimum error. A random signal is taken as an input which is modulated by BPSK
modulator.
After modulation signal is sent through a channel. Here three different types of channel are used.
They are AWGN channel, Rayleigh fading channel & Rician fading channel.
5.2.1. AWGN Channel
The AWGN channel model is referred in v2v communication where no multipath or echo can
affect the transmitted signal. In this channel only some noise is added with the transmitted signal,
which is received by the receiver side vehicle. Then the received signal compared with the
desired signal and equalized it with respect to the desired signal. This process is done by
adaptive equalizer which may be LMS or RLS equalizer. The received signal equalized by RLS
equalizer then the scatter plot of the output signal looks like as Fig5.4.Similarly the LMS
equalizer output is shown in the Fig 5.5.
And by this way I simulate all the channels under BPSK modulation technique.

31
Figure-5.1: Block diagram of AWGN channel
Figure-5.2: Constellation diagram of transmitted signal.

32
Figure-5.3: Constellation diagram of received Signal(Noisy)
Figure-5.4: Constellation diagram of equalized signal(After RLS)

33
Figure-5.5: Constellation diagram of equalized signal(After LMS)
5.2.2.Rician Fading Channel
Figure-5.6: Block diagram of RICIAN Channel Model.

34
Figure-5.7: Constellation diagram of transmitted signal
Figure-5.8: Constellation diagram of received signal at the receiver end(Noisy signal)

35
Figure-5.9: Constellation diagram of equalized signal (RLS).
Figure-5.10: Constellation diagram of equalized signal (RLS).

36
5.3. For QPSK Modulation Technique
As like as BPSK techniques I simulate all the channels under QPSK modulation technique.
5.3.1. AWGN Channel
Figure-5.11: Block diagram of AWGN channel
Figure-5.12: Constellation diagram of transmitted signal

37
Figure-5.13: Constellation diagram of received signal(Noisy)
Figure-5.14: Constellation diagram of equalized signal(After RLS)

38
Figure-5.15: Constellation diagram of equalized signal(After LMS)

39
5.4. Result
5.4.1 For BPSK Modulation Scheme
From the simulation we got these tables.
Channel RLS LMS
AWGN 0.26497 0.26504
Relaygh Fading 0.28785 0.28789
Rician 0.28361 0.28396
Table-1: comparison of RLS and LMS eroors under BPSK modulation techniques.
5.4.2 For QPSK Modulation Scheme
Channel RLS LMS
AWGN 0.23555 0.2462
Relaygh Fading 0.28093 0.28193
Rician 0.28498 0.28468
Table-1:comparison of RLS and LMS eroors under QPSK modulation techniques.
.
From this simulation of all the adaptive filters (LMS & RLS) comparison I observed that the
RLS exhibits minimum error. A random signal is taken as an input which is modulated by PSK
modulator.
After modulation signal is sent through a channel. Here three different types of channel are used.
They are AWGN channel, Rayleigh fading channel & Rician fading channel.
From this comparison, we see that RLS algorithm generates less error than LMS algorithm. And
AWGN channel produces smallest amount of error value. The error value increases for changing
channel from AWGN Channel to Rician Fading Channel, & Rician Fading Channel to Rayleigh
Fading Channel. On the other hand mathematical computation is simple and straight forward for
LMS compared to RLS and hence implementation of LMS algorithm is easier.

40
Chapter-6
CONCLUSION
6.1. Summary
In this work I studied about the adaptive algorithms for channel estimation under
different channel scenario with different modulation techniques.This work mainly compare and
give the result about the relatively appropriate algorithm between the RLS and LMS
algorithms.This comparison is mainly based on the error rate of these two popular channel
estimation algorithms.
This comparison gives the result about the appropriate modulation techniques used for
the appropriate channels with perfect estimation algorithm for reliable communication.
6.2 Scope of Future work
In order to achieve a reliable communication system capable of meeting the demands of
the future, it is important to estimate the time-variant channel as accurately as possible, i.e. as
close to the true channel as possible. In order to estimate the channel accurately, the velocity of
the user must be estimated. It is necessary to estimate the velocity of the user for the Slepian
basis expansion because good performance of the Slepian depends on sequences of the Slepian
basis expansion complying with the velocity of the user. For high data rates and high capacity of
the system, many methods remain to be investigated. Future work to investigate the reduction in
the length of Slepian sequences in a slow fading channel (i.e., in a lower mobility and when the
Doppler frequency is small) is also important.

41
REFERENCES
[1].Haykin, S. (1996), ―‖Adaptive Filter Theory, 3/e‖, Prentice Hall.
[2].S.Dhar, R.Bera, R.B. Giri, S.Anand, D.Nath, S.Kumar (2011), ―‖An Overview of V2V
Communication Channel Modeling”, in proceedings of ISDMISC’11, 12-14 April, 2011,
Sikkim, India.
[3].Sarada Prasanna Dash, Bikash Kumar Dora –―Channel estimation in multicarrier
communication systems‖.
[4].Shetty k.k. ―LEAST MEAN SQUARE ALGORITHM.
[5].Rupul Safaya -A Multipath Channel Estimation Algorithm using the Kalman Filter.
[6].Jones et al. ―‖Adaptive Filtering: LMS Algorithm‖-02.06.2005 16:10 filterdesign
exercise in matlab.
[7].Markku Pukkila, Nokia Research –“CenterChannel Estimation Modeling”.
[8].Tirthankar Paul, Priyabrata karmakar, Sourav Dhar ―Comparative Study of Channel
Estimation Algorithms under Different Channel Scenario” ― International Journal of
Computer Applications (0975 – 8887) Volume 34– No.7, November 2011
[9].RLS online available at: http://en.wikipedia.org/wiki/ Recursive_least_squares_filter
[10]. Additive white Gaussian noise online available at:
http://en.wikipedia.org/wiki/Additive_white_Gaussian_noise
[11]. Rician fading online available at: http://en.wikipedia.org/ wiki/Rician_fading
[12]. Rayleigh fading online available at: http://en.wikipedia. org/wiki/Rayleigh_fading

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