OFDM Systems

for Wireless Communications

Synthesis Lectures on
Algorithms and Software in
Engineering
Editor
Andreas S. Spanias, Arizona State University

OFDM Systems for Wireless Communications

Adarsh B. Narasimhamurthy, Mahesh K. Banavar, and Cihan Tepedelenlioglu
2010

Algorithms and Software for Predictive Coding of Speech

Atti Venkatraman
2010

MATLAB Software for the Code Excited Linear Prediction Algorithm: The Federal
Standard1016
Karthikeyan N. Ramamurthy and Andreas S. Spanias
2010

Advances in Waveform-Agile Sensing for Tracking

Sandeep Prasad Sira, Antonia Papandreou-Suppappola, and Darryl Morrell
2008

Despeckle Filtering Algorithms and Software for Ultrasound Imaging

Christos P. Loizou and Constantinos S. Pattichis
2008

Copyright 2010 by Morgan & Claypool

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printed reviews, without the prior permission of the publisher.

OFDM Systems for Wireless Communications

Adarsh B. Narasimhamurthy, Mahesh K. Banavar, and Cihan Tepedelenlioglu
www.morganclaypool.com

ISBN: 9781598297010
ISBN: 9781598297027

paperback
ebook

DOI 10.2200/S00255ED1V01Y201002ASE005

A Publication in the Morgan & Claypool Publishers series

SYNTHESIS LECTURES ON ALGORITHMS AND SOFTWARE IN ENGINEERING
Lecture #5
Series Editor: Andreas S. Spanias, Arizona State University
Series ISSN
Synthesis Lectures on Algorithms and Software in Engineering
Print 1938-1727 Electronic 1938-1735

OFDM Systems
for Wireless Communications
Adarsh B. Narasimhamurthy, Mahesh K. Banavar, and Cihan Tepedelenlioglu
Arizona State University

SYNTHESIS LECTURES ON ALGORITHMS AND SOFTWARE IN

ENGINEERING #5

M
&C

Morgan

& cLaypool publishers

ABSTRACT
Orthogonal Frequency Division Multiplexing (OFDM) systems are widely used in the standards
for digital audio/video broadcasting, WiFi and WiMax. Being a frequency-domain approach to
communications, OFDM has important advantages in dealing with the frequency-selective nature
of high data rate wireless communication channels. As the needs for operating with higher data rates
become more pressing, OFDM systems have emerged as an effective physical-layer solution.
This short monograph is intended as a tutorial which highlights the deleterious aspects of the
wireless channel and presents why OFDM is a good choice as a modulation that can transmit at high
data rates.The system-level approach we shall pursue will also point out the disadvantages of OFDM
systems especially in the context of peak to average ratio, and carrier frequency synchronization.
Finally, simulation of OFDM systems will be given due prominence. Simple MATLAB programs
are provided for bit error rate simulation using a discrete-time OFDM representation. Software is
also provided to simulate the effects of inter-block-interference, inter-carrier-interference and signal
clipping on the error rate performance. Different components of the OFDM system are described,
and detailed implementation notes are provided for the programs. The program can be downloaded
from http://www.morganclaypool.com/page/ofdm

KEYWORDS
multi-carrier, orthogonal frequency division multiplexing (OFDM), frequency domain,
carrier frequency offset, peak-to-average power ratio, simulations

vii

Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Modeling Wireless Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

2.1

Basic Characteristics of Mobile Radio Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2

Microscopic or Small Scale Fading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Doppler Spread: Time Selective Fading

2.2.2 Delay spread: Frequency Selective Fading

2.3

Tapped Delay Line Model for Frequency Selective Fading Channels . . . . . . . . . . . . 10

Baseband OFDM System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1

Introduction to OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2

Discrete Baseband Block Transmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

3.3

Discrete-Time OFDM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Carrier Frequency Offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.1

Carrier Synchronization Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.2

Frequency Offset Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.2.1 Frequency Domain Autocorrelation
4.2.2 Maximum Likelihood Estimation

4.3

26
26

ICI Cancelation Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.3.1 Self-ICI Cancelation Scheme
4.3.2 Windowing

28

28

Peak to Average Power Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.1

Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.2

PAPR Mitigation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

viii

CONTENTS

5.2.1 Signal Distortion Techniques

5.2.2 Coding and Scrambling

32

34

Simulation of the Performance of OFDM Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6.1

Performance of an OFDM System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6.2

Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
6.2.1 The Basic OFDM System
6.2.2 Carrier Frequency Offset
6.2.3 PAPR Simulations

39
44

48

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Authors Biographies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Preface
Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier communication
scheme widely adopted in the wireless communications industry. In this book, we provide a brief
and comprehensive coverage of the OFDM system model, an overview of its advantages and disadvantages, along with MATLAB codes for simulation. This book is intended for practitioners or
students with some elementary knowledge of digital communications. The main focus of this book
is to aid readers in understanding the workings of a point to point baseband OFDM system and
understanding how to simulate performance under certain impairments. A unique feature of the
book is its emphasis on discrete-time representations which are used to simulate OFDM systems. In
order to make the book accessible to a wider audience, we present several simulations, which provide
a deeper insight into the subject. An extensive list of references is also included to support further
reading.
We begin by highlighting the benefits that OFDM offers over the conventional frequency
division multiplexing scheme in terms of bandwidth efficiency and implementation complexity.
Following this, we motivate the need for OFDM systems by providing a brief introduction to
wireless fading channels, with special emphasis on the time varying and frequency selective nature of
such channels. We demonstrate that complex equalization at the receiver, which would be required
for communication over frequency selective channels, are not needed in the case of OFDM systems,
further motivating its use. Different variations on the basic OFDM system are also presented to
illustrate its versatility. Drawbacks of OFDM such as high peak-to-average power ratio (PAPR)
at the transmitter, and carrier frequency offset (CFO) at the receiver are described, along with
their adverse effects on system performance. Techniques to mitigate their effects are also presented.
All these concepts are supported with simulations. The programs used for these simulations, with
detailed comments, are also provided.
We would like to thank Professor Andreas Spanias, for providing us with the opportunity
to author this book, and Morgan & Claypool publishers for working with us in producing this
manuscript.

Adarsh B. Narasimhamurthy, Mahesh K. Banavar, and Cihan Tepedelenlioglu

February 2010

CHAPTER

Introduction
Next-generation wireless communication systems mandate data rate intensive applications like multimedia services, data transfer, audio, streaming video, leading to future wireless terminals being
capable of connecting to various networks to support services like switched traffic, IP data packets and broadband streaming services. Additionally, with the growth of Internet applications and
wireless users, many wireless local area network (WLAN) standards, including IEEE802.11, permit mobile connectivity to the Internet. With a surging demand for wireless Internet connectivity,
new WLAN standards have been developed including IEEE802.11b, popularly known as Wi-Fi,
that provides up to 11 Mb/s raw data rate, and more recently IEEE802.11g that provides wireless
connectivity with speeds up to 54 Mb/s. High data rates are a requirement for not only wireless
networks but also in broadcasting standards like Digital Audio Broadcast (DAB) [1], Digital Video
Broadcasting-Terrestrial (DVB-T) [2] and the HiperLAN-2 standards in Europe, the Integrated
Services Digital Broadcasting (ISDB) in Japan and the Korean Digital Multimedia BroadcastingTerrestrial (DMB-T) standard [3]. As a solution to their requirements for high data rates, all these
standards use multicarrier communications, and in most cases, Orthogonal Frequency Division
Multiplexing (OFDM).
Multicarrier communication was first implemented in Frequency Division Multiplexing
(FDM) in the early 1900s. In FDM, multiple low rate signals were transmitted using separate
carrier frequencies for each signal. The various carrier frequencies had to be spaced sufficiently apart
to avoid overlap of spectra and to be able to be efficiently separated at the receiver by using low cost
filters. The empty spectral regions between the carrier frequencies led to very low spectral efficiency,
but by breaking up the wide-band channel into several parallel narrower sub-channels, the effect of
inter-symbol-interference (ISI) caused due to the frequency selective nature of the channel is greatly
mitigated compared to the single channel wideband communication scheme. In time domain, the
same can be explained as a method of achieving high immunity against multipath dispersion since
the symbol duration on each sub-channel will be much larger than the channel time dispersion.
Hence, the effects of ISI will be minimized. This gets rid of the need for expensive and complex
equalization techniques. Also, due to the much narrower bandwidth of each sub-channel, effects
of impulsive noise are also reduced. But to implement FDM, which yields the above mentioned
benefits, a dedicated set of filters and oscillators are needed for each sub-channel, which makes the
system expensive and complex to implement.
The Kineplex system developed by Collins Radio Co. [4] was one of the first algorithms to
address the bandwidth efficiency problem of multicarrier transmission for data transmission over a
high frequency radio channel subject to severe multi-path fading. Twenty tones spaced at frequency

1. INTRODUCTION

intervals almost equal to the signalling rate were used. The tones are selected in such a way that they
can be separated at the receiver. A subsequent multi-tone system [5] was proposed using 9-point
QAM constellations on each carrier, with correlation detection employed at the receiver.
The above techniques provide the orthogonality needed to separate multi-tone signals, but due
to the infinite range of the spectrum of each component, the aggregate overlap of a large number
of sub-channel spectra is pronounced. Also, spectrum spillage outside the allotted bandwidth is
significant. With this in mind, it is desirable for each of the signal components to be bandlimited.
There will still be overlap but with only the immediately adjacent sub-carriers, while still remaining
orthogonal to them.
The first OFDM scheme was proposed by Chang in 1966 [6] for dispersive fading channels.
Since then OFDM systems have been extensively employed [7, 8, 9, 10]. Saltzberg [11] studied a
multi-carrier system employing orthogonal time-staggered QAM for the carriers. Use of DFT to
replace the banks of sinusoidal generators and demodulators was suggested by Weinstein and Ebert
[12] in 1971, which significantly reduced the implementation complexity of OFDM systems. In
1980, Hirosaki [13] introduced the DFT-based implementation of Saltzbergs O-QAM OFDM
system.
The simplicity of OFDM has been recognized as an advantage to aid in its implementation [14,
15, 16].The incoming data stream is converted from serial to N parallel data streams and each parallel
data stream is then modulated onto separate carriers using fast Fourier transforms (FFT), ensuring
orthogonality. Due to the advancement in digital circuitry, the hardware to implement FFT is
fast and inexpensive, making this scheme very attractive. Further, by using N parallel data streams
modulated by separate carriers instead of a single high rate stream modulated by a single carrier,
the wide bandwidth of the channel is now broken down into N narrow bandwidth channels which
only experience flat fading. This avoids the need for equalizers at the receiver even over dispersive
channels. To summarize, OFDM provides the following advantages over traditional FDM methods:
High spectral efficiency due to the absence of guard bands
Simple and efficient hardware realization by implementing the FFT operation
Avoids inter-symbol-interference and thereby leads to low complexity receivers due to the
avoidance of equalizers
Each sub-carrier can have a different modulation/coding scheme leading to the design of
highly robust adaptive transmission schemes
Enables frequency diversity by spreading the subcarriers across the usable spectrum
Provides good resistance against co-channel interference and impulsive noise
Though OFDM offers the above advantages, it has some disadvantages:
High sensitivity to Doppler shifts, requiring accurate frequency and time synchronization

High Peak-to-Average Power Ratio due to the overlap of a large number of modulated subcarrier signals which requires the transmit power amplifier to be linear across the whole signal
range, or otherwise leads to clipping of peaks causing distortions. If the transmit power amplifier is not linear across the whole range, the out of band power leakage is significant which
causes inter-carrier interference
Loss in spectral efficiency due to the use of guard interval/cyclic prefix
With the substantial advancements in digital signal processing technology and drop in hardware costs, the presence of OFDM in telecommunication standards is rapidly growing. OFDM is
used in broadcast standards such as Digital Video Broadcasting Terrestrial (DVB-T) for international television with 1705 or 6817 subcarrier OFDM, Digital Multimedia Broadcasting (DMB)
for use in multimedia data transfer for mobile devices in Korea, and Integrated Services Digital
Broadcasting (ISDB) for digital television in Japan with Band Segmented Transmission (BST)OFDM. Wireless network standards such as IEEE 802.11a, wireless local area networks (WLAN),
metropolitan area networks (MAN), wireless personal area networks (WPAN) and HiperLAN/2
are based on OFDM transmissions. The IEEE P1901 draft standard for broadband over power line
networks includes OFDM in its specifications.
The rest of the book is organized as follows. In Chapter 2, wireless communication channels
are first introduced. Following this a baseband OFDM system is defined in Chapter 3. In Chapter 4
and Chapter 5, the two main pathologies of OFDM communication, namely carrier frequency offset
(CFO) and high peak to average power ratio (PAPR) are presented along with techniques to mitigate
their effects on the performance of OFDM systems. In Chapter 6, we provide code to simulate the
error rate performance of a simple OFDM system. Following this, we also illustrate the effects of
CFO and PAPR on the error rate performance of an OFDM system. All programs are written in
MATLAB .

CHAPTER

Modeling Wireless Channels

In this chapter, some of the basic characteristics of the wireless channel are reviewed. In the first part
of the chapter, characteristics of frequency-flat fading channels are introduced. In the second part of
the chapter, frequency selective channels are introduced. A major reason to use OFDM is to mitigate
frequency selective channels effectively. As will be shown in subsequent channels, OFDM converts
one frequency selective channel into several frequency-flat fading channels, motivating the need
for understanding the nature of frequency-flat fading channels, which is addressed in Section 2.1
and Section 2.2. The effect of frequency selectivity is addressed in Section 2.3, since OFDM is a
frequency domain modulation scheme.

2.1

BASIC CHARACTERISTICS OF MOBILE RADIO

CHANNELS

In mobile radio communication, the emitted electromagnetic waves may not reach the receiving
antenna directly due to the obstacles blocking the line-of-sight path. The received waves are a
superposition of waves coming from different directions due to reflection, diffraction, and scattering
caused by buildings, trees, and other obstacles. This effect is known as multipath propagation.
In mobile communication the signal power drops off at the receiver due to (i) mean path
loss, (ii) macroscopic fading, also called shadowing, and (iii) microscopic fading, also referred to as
small scale fading. Mean path loss arises from inverse square law of power loss and depends on the
distance of the traveling wave. Macroscopic fading or shadowing results from a blocking effect by
obstacles such as buildings, large trees and mountains. Microscopic or small scale fading arises due to
the multipath propagation where the received signal consists of an infinite sum of attenuated, delayed
and phase-shifted replicas, caused due to the scattering of the transmitted signal by obstructions.
Multipath propagation and the mobility of the receiver result in the spreading of the signal in different
dimensions. These are mainly delay spread due to the presence of resolvable multipath components
in time and Doppler spread in frequency due to the mobility of the terminal. We now describe the
statistics of small scale fading along with the time and frequency spread that the channel introduces.

2.2

MICROSCOPIC OR SMALL SCALE FADING

Small scale fading refers to the rapid fluctuations of the received signal in space, time and frequency [17]. Since fading is caused by the superposition of a large number of independent scattered
components, the in-phase and quadrature components of the received signal can be assumed to be

2. MODELING WIRELESS CHANNELS

independent zero mean Gaussian processes. Therefore, if no line-of-sight (LOS) path exists, the
received signal consists only of sum the independent scattered components. The envelope, |h|, of
the received signal has a Rayleigh density function given by
 
2u
u2
f|h| (u) = 2 exp
, u 0,
(2.1)
h
h2
where h2 := E[|h|2 ]. If there exists a line-of-sight (LOS) path between the transmitter and the
receiver, the signal envelope is no longer Rayleigh distributed, but has a Ricean distribution. The
Ricean distribution is defined in terms of the Ricean factor, K, which is the ratio of the power in the
mean component of the channel to the power in the scattered (diffused) component. The Ricean
probability distribution function (PDF) of the envelope of the received signal is given by

 

(u2 + 02 )
2u
2u0
f|h| (u) = 2 exp
, u 0,
(2.2)
I0
2
h
h2
where h2 = E[|h 0 |2 ] is the average power of non-line-of-sight component and 02 is the average
power of the LOS component, the Ricean factor K = 02 /h2 , and I0 is the modified Bessel function
of the first kind defined as

1
exp (x cos ) d.
(2.3)
I0 (x) =
0
In the absence of a direct path, i.e., with K = 0, the Ricean PDF in (2.2) reduces to the Rayleigh
PDF in (2.1) with I0 (0) = 1. There are other fading models, such as Nakagami fading or Weibull
fading [18], which will not be considered.
An example of a multipath channel is shown in Figure 2.1. Out of several possible paths
emanating from the transmitter, four are shown. There is one LOS path directly from the transmitter
to the receiver. Three other paths shown from the transmitter, first encounter obstacles. Two of them
reflect and reach the receiver, while the third reflects off an obstacle, but away from the receiver.
These four paths are examples of actual paths. The received signal will consist of many such paths
combining non-coherently at the receiver.

2.2.1

DOPPLER SPREAD: TIME SELECTIVE FADING

Due to relative motion between the transmitter and the receiver, the Doppler effect causes an
apparent frequency shift of the received electromagnetic waves. If the angle of arrival of the n-th
incident wave is n , the Doppler frequency shift of this component is given by fn := fmax cos n ,
where fmax = (v/c)f0 is the maximum Doppler frequency, speed of the mobile unit is v, c is the
speed of light and the carrier frequency is f0 . Due to the Doppler effect, the spectrum of the
transmitted signal undergoes a frequency expansion known as frequency dispersion. In time domain,
the Doppler effect implies that the impulse response of the channel becomes time-variant. The
scattering function, S(, f ), can be used to capture the time-variant nature of the channel caused

2.2. MICROSCOPIC OR SMALL SCALE FADING

Figure 2.1: Illustration of a multipath channel. Time-delayed reflections of the same signal combine at
the receiver.

by the Doppler effect [19]. The scattering function shows the Doppler power spectrum for paths
with different delays and Doppler frequency f , and it is a complete characterization of the second
order statistics of wireless channels [20]. Figure 2.2 illustrates a scattering function with respect to
Doppler frequency f and delay . When averaged over the delay, , the scattering function yields
the Doppler spectrum, S(f ), which is the average power of the channel output as a function of the
Doppler frequency:

S(f ) =

S(, f )d.

(2.4)

The root mean square (RMS) bandwidth of S(f ) is called the Doppler spread, frms , and is given by



 Rf (f favg )2 S(f )df

,
(2.5)
frms =
Rf S(f )df
where Rf is the region where f0 fmax f f0 + fmax and favg is the average frequency of the
Doppler spectrum given by

Rf f S(f )df
favg =

.
(2.6)
Rf S(f )df
In the presence of direct path, the Doppler spectrum, S(f ), is modified by an additional
discrete frequency component corresponding to the relative velocity between the base-station and
the terminal. Fading introduced by the Doppler effect can be characterized by the coherence time, Tc ,
of the channel and is typically defined as the time lag at which the signal autocorrelation coefficient

2. MODELING WIRELESS CHANNELS

Figure 2.2: Plot of the scattering function, S(, f ).

reduces to 0.7. The coherence time can also be approximated as the reciprocal of the Doppler spread,
i.e., Tc 1/frms . Thus, the coherence time serves as a measure of how fast the channel changes in
time, i.e., the larger the coherence time, the slower the channel fluctuation.
The coherence time and the Doppler effect play an important role in the functioning of
multicarrier systems. In a multicarrier system, a frequency selective channel with large bandwidth
is divided into several narrow-band subcarriers. If the number of subcarriers increases for a given
bandwidth, the bandwidth assigned to each channel reduces. This implies that the pulse width of
the symbols in time increases. Therefore, the system has to designed carefully for the symbol pulse
width to not exceed the coherence time of the channel. Doppler also causes loss of orthogonality
of the subcarriers in frequency which leads to inter-carrier interference, and this will be covered in
Chapter 4.

2.2.2

DELAY SPREAD: FREQUENCY SELECTIVE FADING

In multipath propagation, depending on the incident phase of the waves from each of the multiple
paths, their superposition can be constructive or destructive. Moreover, there may exist multiple
resolvable components depending on the transmission rate. Thus, the presence of more than one
resolvable multipath component causes time dispersion of the transmitted pulse and often several

2.2. MICROSCOPIC OR SMALL SCALE FADING

individually distinguishable pulses occur at the receiver. This time, dispersion of the pulses manifests
as frequency distortion in the frequency domain due to the non-flat frequency response of the
channel. The distortion caused by multipath propagation is usually modeled as linear and often
compensated by an equalizer in single carrier communication. In multicarrier communications,
however, several narrow band parallel subcarriers are transmitted where each subcarrier is designed
to observe frequency-flat fading.
The delay separation between paths increases with path delay [21]. The span of path delays
between the first and the last replicas of the received signal is called the delay spread. The RMS delay
spread of the channel, rms , is defined as


max
( avg )2 A( )d
0

max
rms =
,
(2.7)
A( )d
0
where the multipath intensity profile or power delay profile, A( ), is the average power of the channel
output as a function of delay , max is the maximum path delay and avg is the average delay spread
given by

max
A( )d
avg =
0 max
.
(2.8)
A( )d
0
The multipath intensity profile is related to the spectrum S(f ) as

S(, f )df.
A( ) =

(2.9)

Therefore, to avoid inter-symbol interference (ISI) in linearly modulated systems, the symbol duration, T  rms should be satisfied. In the OFDM scenario, the symbols being transmitted are
separated by a specialized guard band called the cyclic prefix. The length of the cyclic prefix should
be at least as long as the maximum delay spread. The cyclic prefix, and its role in OFDM systems, is
explained in more detail in Chapter 3. In the presence of delay spread, the channel can be modeled
as a tapped delay line filter and, consequently, frequency-selective fading is experienced. Frequencyselective fading can be characterized in terms of its coherence bandwidth, Bc , which is the frequency
difference for which the channels autocorrelation coefficient reduces to a prescribed value (example,
0.7 in [22]). The coherence bandwidth is a measure of the channels frequency selectivity and is the
reciprocal of the RMS delay spread, i.e., Bc 1/rms . The power delay profile is often modeled as
one-side exponential distribution:
A( ) =

1
avg



exp /avg , 0.

(2.10)

Using (2.7), it can be shown that for the exponential delay profile given in (2.10), rms = avg .
Typically, delay spread, rms , increases with distance from the terminal. This is due to the fact
that at larger distances, multipaths with large delays have strengths comparable to the direct path

2. MODELING WIRELESS CHANNELS

Impulse Response
5

2
h[l]

10

2
1

2
l

Figure 2.3: Impulse response of channel in (2.12).

which ultimately increases rms . In flat rural areas, rms is less than 0.05 s, in urban areas rms
is approximately 0.2s and in hilly terrains rms is around 2-3 s [23]. In a multicarrier system,
a frequency selective channel is divided into several narrow-band subcarriers. The subcarriers are
chosen such that each of them is a frequency-flat fading channel.The values of the RMS delay spread
and the coherence bandwidth play an important role in determining the number of subcarriers to
be used. For instance, consider a system with a total bandwidth of BW = 2MHz. The system is
deployed in an environment that has an RMS delay spread of 25s or a coherence bandwidth of
Bc = 40kHz. For channels to be frequency-flat fading, the required coherence bandwidth on each
of N subcarriers is given by BN := BW/N  Bc . If BN = 0.1Bc , at least N = 500 subcarriers have
to be used. In OFDM, as will be shown later, it is preferred that the number of subcarriers be a
power of 2, in which case, N = 512 can be used.

2.3

TAPPED DELAY LINE MODEL FOR FREQUENCY

SELECTIVE FADING CHANNELS

In this section, we consider frequency selective channels and briefly discuss some methods to mitigate
the effects of frequency selective channels. The drawbacks of these schemes are presented in order
to motivate the need for multicarrier systems such as OFDM.
Frequency selective channels are commonly represented using the tapped delay line model.
In a tapped delay line model, a data line is tapped at different time delays, weighted with different
values, and then summed together to provide an output. Such a model efficiently represents data
received via multiple paths for a signal from the same source, making it a good fit for frequency
selective channels. For a frequency selective channel represented using L taps, if the transmitted

2.3. TAPPED DELAY LINE MODEL FOR FREQUENCY SELECTIVE FADING CHANNELS

11

Frequency Response

Magnitude (dB)

20

10

10

20

0.1

0.2

0.3
0.4
0.5
0.6
0.7
Normalized Frequency ( rad/sample)

0.8

0.9

0.1

0.2

0.3
0.4
0.5
0.6
0.7
Normalized Frequency ( rad/sample)

0.8

0.9

Phase (degrees)

200

400

600

800

Figure 2.4: Frequency response of channel in (2.12).

data is u[n], the output at the receiver, r[n], is represented as [20]

r[n] =

L1


h[n; l]u[n l],

(2.11)

l=0

where h[n; l], l = 0, . . . , L 1 represent the L taps of the frequency selective channel at time n.
This convolutional channel can also be interpreted as an FIR filter of order L 1. For a frequency
selective fading channel, the channel coefficients are modeled as random 1 .
As an example, consider a channel h[n; l] at a fixed instant of time. Assuming that the
channel is time invariant, we drop the time index. Consider, as an example, the channel whose
impulse response can be represented as
h[l] = 3[l] [l 1] + [l 2] + 4[l 4].

(2.12)

For this channel, the impulse response and frequency response are shown in Figure 2.3 and Figure
2.4, respectively. We can see from Figure 2.3 that the channel represents a multipath channel, and
from Figure 2.4, we can see that the response of the channel is not the same at each frequency,
making it a frequency selective channel. In multicarrier systems such as FDM and OFDM, the
frequency spectrum is divided into several narrow-band channels called subcarriers. If the channel
bandwidths are small, each can be considered to be a frequency-flat fading channel. While this is a
1 It should be noted that in some cases, especially in wired ISI channels, such as telephone lines, the channel taps are modeled as

deterministic [24].

12

2. MODELING WIRELESS CHANNELS

good approach to mitigate the effects of a frequency selective channel, a subcarrier that occurs at a
trough on the frequency response of the channel will result in a channel with very poor performance.
Strategies such as error-control coding across subcarriers [25] are used to improve performance in
such situations.
As shown in (2.11), transmission over a frequency selective channel can be considered as a
convolution in time between the data and the tapped delay line representation of the channel. The
Viterbi algorithm considers the channel as a state-machine and can be used to decode the data, and
it is shown to provide the maximum-likelihood solution [24]. However, the Viterbi algorithm grows
exponentially in the number of channel taps.
Alternatively at the receiver, the convolution in (2.11) can be inverted in order to estimate
the transmitted data, in a process called equalization. Several suboptimal techniques can be used for
equalization. Linear equalization uses an FIR filter, g[l], to estimate the value of the transmitted
symbol, u[n], to yield the estimate:

u[n] = y[n] g[n].

(2.13)

The filter, g[l], has to be selected so that the estimate, 

u[n], is close to the transmitted signal, u[n].
In the absence of channel noise, g[l] is selected such that
h[l] g[l] = [l],

(2.14)

so that 
u[n] = u[n]. Since the convolution of two FIR filters will never yield [l], selecting g[l] to
satisfy (2.14) is not possible with an FIR equalizer. Instead, the optimum coefficients of g[l] are
chosen in a way to minimize a performance index, such as the mean square error (MSE) between
the transmitted symbol, u[n], and the estimate of the symbol, 
u[n], at the receiver as follows:


g opt [l] = argmin E |u[n] 
u[n]|2 .
(2.15)
{g[l]}

More information about these and other more complex equalizers such as the decision-feedback
equalizer (DFE) and iterative solutions to (2.15) can be found in [24].
Mitigation of the effects of the frequency selective channel requires estimation of the taps for
both equalization and the Viterbi algorithms. Due to the convolutional nature of the channel, channel
estimation cannot be performed by transmitting a pilot tone. A white noise sequence is transmitted
and cross-correlated with the received signal in order to estimate the channel [26]. In contrast,
with frequency-domain schemes such as FDM or OFDM, the frequency-flat fading channel on
each subcarrier can be estimated individually by transmitting a pilot tone at each subcarrier. If the
entire channel estimate is required with a few pilots, interpolation of the channel estimates in the
frequency domain will yield the required result. The structure of an OFDM system, which allows
such estimation, is discussed in Chapter 3.

13

CHAPTER

Baseband OFDM System

In this chapter, the basic model of the OFDM system is introduced. First, an analog interpretation of
the OFDM system is presented. Following this, the discrete symbol-rate sampled OFDM transmission scheme is developed. Block transmissions built on a matrix-vector framework are introduced,
which subsume transmission schemes that use zero padding (ZP), OFDM using cyclic prefix, and
pre-coded transmissions. We will also discuss OFDM as a block transmission scheme effective in
mitigating ISI in large delay spread environments.

3.1

INTRODUCTION TO OFDM

As discussed in Chapter 2, a frequency selective channel has a convolutional effect on transmitted

data, and methods such as the Viterbi algorithm or equalization are used to mitigate the effects of the
frequency selective channel. Orthogonal Frequency Division Multiplexing (OFDM) is a technique
that can also be used to mitigate frequency selective channels.

Figure 3.1: An example of FDM transmissions.

14

3. BASEBAND OFDM SYSTEM

In a simple frequency division multiplexing (FDM) system, the entire channel bandwidth is
divided into several narrow bandwidth channels, referred to as subcarriers. If the bandwidth of the
subcarrier is suitably small, it can be considered to be a flat fading channel. In an FDM system,
the subcarriers need to be assigned in such a way that they do not interfere with each other. Such
a system is shown in the top half of Figure 3.1 where the allotted bandwidth is partitioned into
subcarriers. To make allowances for bandwidths that are not restricted in frequency, and for filters,
the subcarriers are spaced sufficiently apart from each other. The restriction stops us from utilizing
a partitioning system as shown in the bottom half of Figure 3.1.

Figure 3.2: OFDM symbols represented using sinc functions.

In contrast, in an OFDM system, in addition to dividing the frequency spectrum into separate
parts, they are shaped as well, as shown in Figure 3.2. Due to this shaping, when a subcarrier is
sampled at its peak, all other subcarriers have zero-crossings at that point, and they do not interfere
with the subcarrier being sampled. In case this sampling is off-peak, there could be interference
from adjacent subcarriers. Furthermore, not truncating the spectrum of each subcarrier reduces the
demands on filters, and it allows the symbols to be restricted in time. In a typical OFDM system,
data symbols are transmitted over each subcarrier and received without interference.
To implement such a system, the symbols are first considered in frequency. By taking the
IFFT of the data symbols, time-domain representations are obtained. A cyclic prefix is added to this
representation in time. An interval of the time-representation of the symbols is copied and added to
the front, comprising the cyclic prefix. This data, after the addition of the cyclic prefix, is transmitted

3.1. INTRODUCTION TO OFDM

15

over the frequency selective channel. At the receiver, the cyclic prefix is dropped, and the FFT of
the rest provides the symbols at the receiver [27].
In this process, the length of the cyclic process plays an important role. The length of the
cyclic prefix is chosen such that it is larger than the maximum delay spread of the wireless channel.
Figure 3.3 helps understand the significance of the cyclic prefix. It shows three subcarriers in the

Figure 3.3: Importance of Cyclic Prefix.

time domain after passing through a two-ray channel environment (L = 2 in (2.11)). The solid
curves represent the subcarriers that have reached the receiver without any delay, and the dotted
ones represent those that have reached after a certain delay. Of course, what we see at the receiver
is a sum of the signals. Figure 3.3 also shows the phase transitions that might occur at symbol
interval boundaries. Since the choice of the cyclic prefix interval is larger than the delay spread, the
delayed replicas of the subcarriers show phase transitions within the guard interval. At the receiver,

16

3. BASEBAND OFDM SYSTEM

since FFT is taken after discarding the guard interval part of the received signal, the orthogonality
between any subcarrier and delayed version of any other subcarrier is still preserved [28].
The analog method described provides good intuition into the working of an OFDM system.
However, in the case of digital systems, the continuous-time methods described cannot be used.
Digitization and the use of block transmissions are required. These digitization techniques are used
to formally introduce the concepts of OFDM later on in the chapter. Additionally, the discrete model
is more suited for simulation using computer programs.

3.2

DISCRETE BASEBAND BLOCK TRANSMISSIONS

The purpose of this section is to establish a convenient discrete-time framework encompassing wellknown block transmission techniques like OFDM with a Cyclic Prefix (CP-OFDM), zero-padded
(ZP) transmissions, and block pre-coded transmissions that process information symbols in blocks.
We will also show that block-transmissions are an effective way to mitigate channel induced ISI [29].
This unifying model is useful in holding a signals-and-systems view of the entire transmission process
and is also used in describing the OFDM transmission technique later in this chapter.

Figure 3.4: Serial Transmissions (above) and Block Transmissions (below).

We first begin with a linearly modulated transmission system over a frequency selective channel. In Figure 3.4, u[n], n Z are pulsed shaped by a filter with response p(t) and then sent over a
wireless channel with an impulse response h(t), and additive noise, v(t). The received signal is then
passed through a filter with response p(t), matched to the transmit pulse-shaping filter p(t). The
equivalent received discrete-time sequence is given by
r[n] =

L1


u[n l]h[l] + v[n],

(3.1)

l=0

where h[n] := h(nTs ), r[n] := r(nTs ), v[n] := v(nTs ), Ts is the sampling period, and h(t), r(t)
and v(t) are the analog-time representations of the channel, received symbols, and additive channel

3.2. DISCRETE BASEBAND BLOCK TRANSMISSIONS

17

noise, respectively. Let us now link this serial transmission setup with a block transmission setup. In
block transmissions, blocks of length P are obtained from the symbols u[n] such that P  L. Let
u[i] denote the i th transmitted block1 which is equal to [u[(i 1)P ], u[(i 1)P + 1], . . . , u[(i
1)P + P 1]]T . Using (3.1), it can be shown that
r[i] = H0 u[i] + H1 u[i 1] + v[i],

(3.2)

where r[i] = [r[(i 1)P ], . . . , r[(i 1)P + P 1]]T and v[i] = [v[(i 1)P ], . . . , v[(i
1)P + P 1]]T are the received and noise vectors, respectively, in the i th block interval and because
P  L, the P P channel matrices H0 and H1 are given by

h[0]
0
0
...
0

..

.
h[0]
0
...
0

..

.
,
(3.3)
H0 = h[L 1] . . .
...
0

.
.
.
..
..
..

0
...

..
. h[0]
0
. . . h[L 1]
P P

and

0 . . . h[L 1] . . .
h[1]
.. . .
..
.
.
.
.
.
0
.

.
..
H1 =
. . . h[L 1]
0 ...
..
.
.
..
..
..
..
.
.
.

..
0 ...
0
.
0

(3.4)

P P

and shown in the lower block diagram in Figure 3.4. It may be noted that similar to ISI in the
serialized transmission shown in (3.1), there is inter-block interference (IBI) for the block u[i], but
only from the immediately preceding block u[i 1], due to causality. It is easy to see that this
is a consequence of choosing P  L. The matrix-vector framework given in (3.2) unifies many
well-known transmission schemes in the following way:
Block-Precoded Transmissions Linear precoded transmissions, such as those that have
been proposed for OFDM in [30] are instances of block transmission. They involve linearly
coding the information block s[i] by multiplying with a precoding matrix Th. With linear
precoding, u[i] is derived from Th s[i] after either zero-padding or after taking FFT and
appending a cyclic prefix and is transmitted over the channel, which will be discussed next.
Zero-Padded Transmissions (ZP) Let us recollect from (3.2) that there is IBI in the
received block at the i th time instant because of the last L 1 symbols in u[i 1]. It is easy
1The term symbol is used interchangeably with block in block transmissions.

18

3. BASEBAND OFDM SYSTEM

to see that this IBI can be made equal to 0 by making the last L 1 or more of the symbols in
u[i 1] equal to 0. This ZP transmission scheme [31] can be mathematically represented as
y[i] = H0 Tzp u[i] + v[i],

(3.5)

where u[i] = Tzp s[i] is obtained from an N 1 block of symbols by appending L > L zeros
T
through a matrix operator Tzp := [ITN 0LN
]T which appends L zeros to s[i]. This period of

silence at the end of the i th block prevents IBI in the (i + 1)th block, since H1 Tzp = 0.

Cyclic-Prefixed OFDM (CP-OFDM) Cyclic-prefixed OFDM [29, 32, 33, 34], which is
a very popular Multicarrier (MC) modulation scheme, is also a block transmission scheme
and fits very well into the data model developed in (3.2). In OFDM, blocks of data, s[i], of
length N, are obtained from a serial stream of input symbols, s[n]. In each block interval i,
the N elements in s[i] are modulated onto N subcarriers. This is achieved through discrete
IFFT at the transmitter. In order to prevent IBI, guard intervals between blocks of symbols
are introduced. But instead of not transmitting anything in the guard interval duration like the
zero-padded transmission scheme described in (3.5), the last L data-points in the tail portion
of the OFDM symbol are transmitted and termed cyclic-prefix. At the receiver, the cyclic
prefix portion of the received signal is discarded, and FFT is taken on the remainder. At this
stage, the relation between the input block s[i] and the output of the FFT appears as follows:
y[i] = Hs[i] + v[i],

(3.6)

where v[i] is the noise vector. Addition of the cyclic prefix makes Rcp H0 Tcp into a circulant
H being diagonal. Therefore,
matrix, which results in the channel matrix, H = FN Rcp H0 Tcp FN
there is no ISI between the elements of y[i] in (3.6). Later in the chapter, we shall see in detail
all the transmitter and receiver operations that result in the data model in (3.6) for OFDM.
We will also address the explicit derivation of (3.6) and the relationship between h[l] and H.

3.3

DISCRETE-TIME OFDM MODEL

Figure 3.5 shows a block-diagram representation of the discrete-time implementation of OFDM.

The transmissions occur on a wireless ISI channel, h(t), which is modeled as a tap-delay-line filter
with L taps, a maximum delay spread of max , average delay spread of avg , and RMS delay spread of
rms . All the transmitter operations like serial-to-parallel conversion of the input data, taking IFFT
on it, cyclic-prefix insertion, pulse-shaping and transmitting on the channel shown in Figure 3.5 are
for the i th block interval and are explained in detail below:
Blocking It has been shown in Section 3.2 that OFDM fits into the general class of block
transmissions. The OFDM modulator parses a continuous stream of input data into blocks
of length N , as shown in Figure 3.5. Later, as we shall see, this blocking of input data and
further processing helps in countering the channel induced ISI. Figure 3.5 shows the signal

3.3. DISCRETE-TIME OFDM MODEL

19

Figure 3.5: Discrete-Time Implementation of OFDM.

processing that takes place in the i th OFDM symbol interval during which the N uncoded
data elements, s[k], k = (i 1)N, (i 1)N + 1, . . . , (i 1)N + N 1, are grouped into
an OFDM symbol, s[i], of length N . The OFDM symbol s[i] is then subjected to further
processing.
Subcarrier Modulation In its discrete-time implementation, the modulation of subcarriers
by the data is achieved through an IFFT operation. In Figure 3.5, we see that the N data
elements, s[i] are subjected to an IFFT operation.
Cyclic-prefix insertion An important operation that helps in preserving the orthogonality
of the subcarriers is the insertion of cyclic-prefix between OFDM symbols. The number
of symbols in the cyclic prefix is at least as many as the number of taps in the FIR filter
representation of the frequency selective channel.

Figure 3.6: Cyclic prefix added to a block of data.

The output, after taking IFFT and inserting cyclic-prefix, is u[i] = [u[(i 1)P ], u[(i
1)P + 1], . . . , u[(i 1)P + P 1]]T , where
1
u[n] =
N

(i1)N
+N 1

k=(i1)N

s[k] exp (j 2 nk/N ) ,

(3.7)

20

3. BASEBAND OFDM SYSTEM

Alternatively, the
for n = (i 1)P , (i 1)P + 1, . . . , (i 1)P + P 1 and P = N + L.
generation of u[i] from s[i] can also be described as


where Tcp := ITcp ITN

T

H
u[i] = Tcp FN
s[i],

(3.8)

is a cyclic prefix inserting matrix with Icp being the last L rows of

H is an N N IDFT
the N N identity matrix IN , FN is an N N DFT matrix, and FN
matrix obtained by taking the Hermitian of FN . An OFDM symbol with the CP added to it
is shown in Figure 3.6.

Pulse-Shaping Samples u[n], n = (i 1)P , (i 1)P + 1, . . . , (i 1)P + P 1 of the

P 1 OFDM symbol u[i] (also containing the cyclic-prefix) that we see in Figure 3.5 are pulse
shaped with a transmit filter, p(t), and transmitted over the channel, h(t). Practical issues like
out-of-band energy emissions, inter-channel interference and peak-to-average power dictate
the choice of the exact pulse-shaping scheme to be used. OFDM systems can be categorized
into two classes depending on the pulse shaping filter used: (i) the class of OFDM systems
that use time-limited pulses [33, 35], typically rectangular pulses that overlap in the spectral
domain, but are orthogonal and (ii) the class of OFDM systems designed with infinitely long
pulses, but they are realized with their truncated versions. Orthogonality conditions for the
second category were presented in [6] and an application with offset QAM was presented in
[11].
Even though the data samples, u[n], are those of an OFDM system, the channel, h[n], affects
them the same way it does other single-carrier transmission schemes. Therefore, the continuoustime signal that is received is the same as in (3.1). At this stage, the data model for the i th block at
the receiver, r[i], is exactly the same as shown in (3.2). After this, the receiver simply eliminates the
IBI due to H1 by discarding the first L samples received during the guard interval and performs an
FFT on the remainder. All the aforementioned operations on r[i], can be mathematically described
as below:
H
y[i] = FN Rcp r[i] = FN Rcp H0 Tcp FN
s[i] + FN Rcp v[i],
(3.9)
where Rcp := [0NL IN ] removes the initial cyclic prefix part of the received symbol. Note that the
IBI term because of H1 is also made zero since Rcp removes its first L rows.The advantage of employH
ing IFFT and FFT at the transmitter and receiver, respectively, is that the factor FN Rcp H0 Tcp FN
simplifies to a diagonal matrix H which can easily be inverted (provided the inverse exists) [29].
Since H is a diagonal matrix, the input-output relation at any subcarrier k is a simple one without
any ISI and is as follows:
y[k] = H [k]s[k] + v[k],
(3.10)
for k = (i 1)N, (i 1)N + 1, . . . , (i 1)N + N 1, where
v[k] := N 1/2

(i1)N
+N1

(i1)N

v[n] exp (j 2 kn/N) ,

(3.11)

3.3. DISCRETE-TIME OFDM MODEL

and H [k] representing the channel gain of the

subcarrier is the
diagonal of H is given by


L1

2 kn
H [k] =
.
h[n] exp j
N
k th

k th

21

element on the principal

(3.12)

n=0

Looking at the input-output relation in (3.10), it is clear that through IFFT and cyclic-prefix insertion at the transmitter and with matching operations at the receiver, OFDM has turned an ISI
channel requiring potentially complex equalization at the receiver into a set of flat-fading channels.
This is the single-most important advantage of OFDM: robustness to large delay spread environments obviating the need for complex equalization at the receiver. One drawback of this method is
that when the gain of a subcarrier is low, equalization amplifies the additive noise. This problem is
exacerbated when a subcarrier lies on a channel null, and the data transmitted over that subcarrier
is completely lost. To mitigate this problem, error-control coding is used to code symbols across
subcarriers [25].

Figure 3.7: A frequency selective fading channel divided into orthogonal subcarriers. Alternate subcarriers are shaded for clarity.

This representation of OFDM as shown in (3.10) can be also interpreted as shown in Figure 3.7. It shows a frequency selective channel divided into subcarriers, with no overlap. Each
subcarrier then behaves like a flat fading channel with no interference from other subcarriers. Data
can be transmitted over each of these subcarriers independently. In this scenario, for channel equalization, it is only necessary to compensate for the effect of each subcarrier indivdually. In order to
do this, the subcarriers gains have to be estimated. For OFDM, each subcarrier gain can be simply
estimated with individual pilots since each subcarrier is now equivalent to a flat fading channel with
no interference from other subcarriers [36]. In case the entire channel needs to be estimated, and
not just the subcarriers, a simple interpolation will yield the required information.
OFDM is used in the DAB [1], the DVB-T [2], the DMB [3] and the IEEE 802.11a [37]
standards. Typical values of parameters such as bandwidth, number of subcarriers, spacing of subcarriers, modulation schemes and bit rates used in these standards are shown in Table 3.1. Some of
these standards work in multiple modes and bands. For instance, although the DAB standard can

22

3. BASEBAND OFDM SYSTEM

Table 3.1: Typical Values of OFDM parameters as used in Common Standards

Standards
Name
DAB
DVB-T
DMB
IEEE 802.11a
174-240
174-240
470-862
4912-5825
Bandwidth (MHz)
1,452-1,492
470-862
Mode I: 1,536
Mode II: 384
2K mode: 1705 3,7802
52
Number of Subcarriers
Mode III: 192
8K mode: 6,817
Mode IV: 768
Mode I: 1000
Mode II: 4000
2K mode: 4,464 2,000
312.5K
Subcarrier Spacing (Hz)
Mode III: 8000 8K mode: 1,116
Mode IV: 2000
Modulation Scheme
/4 DQPSK
QPSK
QAM
BPSK
Bit Rate (Mbits/s)
0.576-1.152
24
4.81-32.49 6-54
operate above 30 MHz, it has spectra allocated for it in Band III (high-band VHF; 174240 MHz)
and L-band (14521492 MHz). DAB has a number of country specific transmission modes (I, II,
III and IV). For worldwide operation, a receiver must support all 4 modes: (i) Mode I for Band III,
Earth; (ii) Mode II for L-Band, Earth and satellite; (iii) Mode III for frequencies below 3 GHz,
Earth and satellite; and (iv) Mode IV for L-Band, Earth and satellite. In the case of DVB-T, there
are two choices for the number of carriers known as 2K-mode or 8K-mode. In the 2K mode, 1,705
(approximately 2000) subcarriers are used that are spaced approximately 4kHz apart. In the 8K mode,
6,817 carriers, approximately 1 kHz apart, are used. The DVB-T has been allocated frequencies in
Band III and Band IV. For these standards that work in multiple modes and bands, receivers are
generally marketed so that they can be set up to work with all the different systems.

23

CHAPTER

Carrier Frequency Offset

Before an OFDM symbol can be successfully demodulated, the receiver has to synchronize to both
the transmitted frame timing and carrier frequency. First, the receiver has to know where exactly it
has to sample the incoming OFDM symbol prior to the FFT process. Secondly, the receiver has
to estimate and correct for any carrier frequency offset because offset can result in inter-carrierinterference (ICI). In fact, the sensitivity to timing and carrier offset errors is higher in OFDM
systems than in single carrier systems [34]. Transmitted signals are provided with timing, frequency,
and phase reference parameters to assist with synchronization at the receiver. Proper detection at the
receiver requires knowledge of these parameters. The first task of the receiver is to estimate symbol
boundaries. If the receiver cannot clearly identify the symbol lengths, then ISI occurs. A preamble
consisting of a sequence of known symbols is used for the receiver. Once the presence of symbol is
detected, the next task is to estimate the frequency offset. Frequency offset occurs due to unmatched
frequencies on the received signal and the local oscillator at the receiver. Therefore, subcarriers could
be shifted from their original positions resulting in a non-orthogonal signal at the receiver resulting
in ICI after the FFT due to the FFT output containing interfering energy from all other subcarriers.
Other problems such as out-of-band radiation [38, 39] can also occur with OFDM transmissions.

4.1

CARRIER SYNCHRONIZATION ERROR

Frequency offsets are typically introduced by a (small) frequency mismatch in the local oscillators
of the transmitter and the receiver. Doppler shifts can also induce a slight frequency change of the
carrier frequency [40] and hence, lead to frequency mismatch.
The impact of a frequency error can be seen as an error where the received signal is sampled
during demodulation. Figure 4.1 depicts this twofold effect.
Since the subcarriers (SC) are orthogonal, when viewed in time domain, the peak of any sinc
is aligned with the zeros of all other sincs. Ideally, each SC is sampled at its peak, and there is no
contribution from the other SCs. However, when there is a frequency offset, sampling may not occur
at the peaks but at an offset point. The amplitude of the desired SC is reduced, and ICI arises from
the adjacent SCs.
Here, we would like to recall that after parallel to serial conversion the output of the IFFT
can be represented as


N 1
1 
j 2 kn
x[n] =
s[k] exp
.
(4.1)
N
N
k=0

24

4. CARRIER FREQUENCY OFFSET

Figure 4.1: Sampling mismatch due to CFO.

We now consider the case where there exists a mismatch in the frequencies of the received signal
and the local oscillator at the receiver. Ignoring the effects of the additive noise, the received signal
after removal of CP can be written as

z[n] =



N 1
1 
j 2 n(k + f )
,
s[k]H [k] exp
N
N

(4.2)

k=0

where f represents the relative frequency offset defined as the ratio of the actual frequency offset
to the intercarrier spacing, and H [k] is the transfer function of the channel at the frequency of the
k th subcarrier. z[n] here also represents the input to the FFT at the receiver. Therefore, the output
of the FFT can be expressed as

y[k] =



N 1
j 2 kn
1 
z[n] exp
.
N
N
n=0

(4.3)

4.2. FREQUENCY OFFSET ESTIMATION

25

Substituting for z[n] from (4.2) into (4.3) and after some algebraic manipulations, the output of the
FFT is given by [41]


 
N1
1 
sin ( (m k + f ))
N 1

 exp j
y[k] =
s[m]H [m]
(m k + f ) ,
N
N
sin (mk+f )

(4.4)





N 1
1
sin ( f )
j (N 1)f
1 
s[k]H [k]
exp
+
s[m]H [m]mk ,
N
sin ( f/N )
N
N

(4.5)

m=0

m=0
m=k

where the complex coefficient

mk =

 


N 1
sin ( (m k + f ))

 exp j
(m k + f ) .
)
N
sin (mk+f
N

Here, it can be seen that when m = k we have





j (N 1)f
sin ( f )
exp
0 =
,
sin ( f/N)
N

(4.6)

(4.7)

which is identical to the scaling factor on the k th subcarrier in (4.5). This implies that in case of
frequency offset, each output symbol estimate now depends on all the input values, i.e., ICI occurs
due to the influence of data on the other subcarriers. Further, it can be seen from (4.5) that if f = 0
then the received signal is s[k]H [k]/N. Since the scaling of k th component is independent of k, it is
evident that all subcarriers experience the same degree of attenuation along with ICI. It is important
to note here that carrier frequency offset does not affect the amplitudes of any of the signals, and,
consequently, it does not change the total power in the received signal.Therefore, the total ICI power
changes little with N. Some techniques for offset estimation and offset cancelation are provided in
the following section. More details can be found in [41, 42, 43].

4.2

FREQUENCY OFFSET ESTIMATION

By estimating the frequency offset at the receiver, the loss in performance due to a frequency mismatch
of the received signal and the receive oscillator can be significantly reduced. The frequency offset
estimation techniques can be broadly classified into pilot-aided schemes and non-pilot aided or blind
estimation schemes. Pilot assisted methods use well defined pilot symbols to aid in the estimation
of CFO. Since this method is capable of achieving very quick and reliable estimates, it is a popular
technique though there is a loss in data rate and spectrum efficiency of the system. Blind or non pilot
assisted methods exploit the structural and statistical properties of the transmitted OFDM signals.
Though these techniques preserve the data rate, they lead to processing the received data multiple
times, which causes delay in decoding. After normalizing the CFO by the subcarrier spacing, the
integer part and the fractional part of the CFO can be estimated separately. Estimation of the integer

26

4. CARRIER FREQUENCY OFFSET

part of the CFO can be termed as coarse CFO estimation while the estimation of the fractional part
of the CFO can be termed as fine estimation of the CFO. Next, we describe briefly simple methods
to estimate the integer part and the fractional part of the CFO.

4.2.1

FREQUENCY DOMAIN AUTOCORRELATION

For this method, pilot symbols are transmitted on a selected set of subcarriers. Out of N subcarriers
in an OFDM symbol, J are selected to be pilots. These J subcarriers are not necessarily contiguous.
Since the integer part of the CFO causes frequency shift of the received signals in the frequency
domain, this method yields good estimates of the CFO. Recall from Chapter 3 that an OFDM block
consists of several OFDM symbols, and each OFDM symbol contains N subcarriers, so that the
data point y[i, j ] represents the symbol transmitted on the j th subcarrier of the i th OFDM symbol.
For the frequency domain auto-correlation scheme, two consecutively received OFDM symbols on
a set of subcarriers are correlated [44], as shown in Figure 4.2, to yield
f[g] =

J
1

y[i, j + g]y [i 1, j + g],

(4.8)

j =0

where g = 0, 1, 2, 3, . . . are the possible integer-valued subcarrier shifts, and 1 , 2 , . . . , J

are the J pilot subcarriers. Since the pilot symbols are not random but known at the receiver, (4.8)
will contain the average magnitude of the squared pilot symbols. The integer portion of the CFO
can be estimated by finding the value of g which results in the largest |f[g]|, i.e.,

g = argmax |f[g]|.
g

(4.9)

Pilot symbols have to be transmitted over several consecutive OFDM symbols to obtain a good
estimate and minimize the error in estimation that maybe caused by channel fluctuations. Following
this, to obtain an accurate estimate of the CFO, we describe the maximum likelihood method of
estimating the fractional portion of CFO.

4.2.2

MAXIMUM LIKELIHOOD ESTIMATION

Though the cyclic-prefix can be used for timing and frequency synchronization, generally, in OFDM
transmissions, there will be an additional preamble transmitted after the CP, and before the data is
transmitted [37]. The preamble is designed to contain multiple repetitive symbols with a symbol
time much less than that of the transmitted data symbol. Such a preamble can be used to estimate
the fractional part of the CFO. Defining Q as the repetition interval length in time samples and B
as the time samples separation between two adjacent repetitions, the maximum likelihood estimator
can be expressed as

Q1

1
ff rac =
arg
z[n q]z [n q B] ,
(4.10)
2 BTs
q=0

4.2. FREQUENCY OFFSET ESTIMATION

27

Figure 4.2: Block Diagram of Carrier Frequency Offset Estimation Process by using the Frequency
Domain Approach.

where arg() represents the argument of a complex number. Given that the phase can be
uniquely resolved in the interval [, ], the CFO can be estimated only within the interval
[1/(2LTs ), 1/(2LTs )]. Adding this to the result obtained by the estimation of the integer part of
CFO, a more accurate estimate is obtained.
Several other algorithms can be used for CFO estimation. In [41], the authors propose a
correlation based technique for estimation. In this method, two consecutive identical pilot symbols
are required to estimate CFO. The restricting assumption made is that the maximum CFO has to be
less than half the subcarrier spacing. In [42], the authors used two identical half-period symbols to
estimate the fractional part of the CFO and a second full period symbol that has a special correlation
relation with the first pilot symbol to estimate the integer part of CFO. The important assumption
the authors made in this work is that the constellation of symbols transmitted on each subcarrier has
points that are equally spaced in phase. A similar method exploiting only two identical half period
symbols to estimate both the integer and the fractional part of the CFO was proposed by [45]. While
the above cited works depend on the correlation of the two half-period identical blocks for estimation,
in [46, 47], the pilot symbol consists of multiple repetitive fractional parts. The differential phase of
the correlation between different pairs of adjacent fractional blocks in a symbol are used to form an
improved estimates.
In the blind estimation methods [37, 48, 49], elements of the transmitted OFDM symbol
such as the cyclic prefix, virtual subcarriers or constant modulus transmission are used. Practical
OFDM systems in general do not have data transmitted on all available subcarriers to help avoid
aliasing errors. Some of the subcarriers at the edges of the OFDM symbol are left empty; these
subcarriers are called virtual subcarriers. The number of subcarriers in a symbol is a system design
parameter (generally about 10% of the total number of subcarriers N). The authors in [50] propose
a blind estimation method that is only suitable to recover CFO values that are multiples of the
subcarrier spacing. In [51, 48], the presence of virtual subcarriers is exploited and techniques such
as MUSIC and ESPIRIT [26] are used to estimate the CFO. This scheme requires usually multiple
OFDM symbols to achieve desirable performance thereby leading to additional delay at the receiver
to estimate the CFO and decode the received symbols.
In a typical communication system, offset estimation is done in the presence of channel noise
corrupting the received signals. Therefore, the estimates obtained are always noisy. When these

28

4. CARRIER FREQUENCY OFFSET

estimates are used to reverse the effects of the frequency offset, there is a residual offset that is small,
but random. This results in deterioration of performance, in spite of compensating for carrier offset
using the estimation process. Therefore, it is preferable that carrier offset be canceled automatically,
rather than be estimated and then removed. In Section 4.3, some algorithms for ICI cancelation are
presented.

4.3

ICI CANCELATION SCHEMES

4.3.1

SELF-ICI CANCELATION SCHEME

There have been several schemes proposed to avoid ICI in the OFDM communication scheme. The
first scheme we consider is called Self ICI Cancelation, proposed by Zhao and Haggman [52, 53]. In
this scheme, instead of independent data being mapped on to the subcarriers, data is mapped onto adjacent pairs of subcarriers. For example, s[0] = s[1], s[2] = s[3], . . ., s[ N 2] = s[ N 1]. This
mapping has been shown to result in cancelation of most of the ICI in the values y[0], . . . , y[N 1].
So, it is evident that the ICI for this scheme depends on the difference between the adjacent weighting
coefficients rather than on the coefficients themselves. As the difference between adjacent subcarriers is small this results in substantial reduction in ICI. If adjacent coefficients are identical, then ICI
is completely canceled. The ICI cancelation in this scheme depends only on the coefficients being
slowly varying functions of offset, and it does not depend on the absolute value of the coefficients
themselves. However, due to the redundancy introduced by mapping the same symbol onto two
subcarriers, the data rate is halved.

4.3.2

WINDOWING

Windowing is another technique proposed to help reduce sensitivity to frequency offsets in an

OFDM system [54, 55, 56, 57]. This process involves cyclically extending the time domain signal
associated with each symbol by v samples.The resulting signal is then shaped with a window function.
The transmitter uses an N/2 point IDFT process while the receiver uses an N point DFT
process. If the time domain signal is extended by v = N/2 samples, then N point received signal can
be used as inputs to the DFT process at the receiver. If v < N/2 then zero padding can be employed
to obtain a sequence of length N. At the output of the DFT process, the even numbered outputs are
used to estimate the transmitted symbols while the odd numbered outputs are discarded. Here it is
important to note that since all the received power is not being used in generating data estimates, this
method has a reduced overall SNR compared with OFDM without windowing. Different windows
can be used in this scheme. The authors in [54] consider a Hanning window, while in [55] the
general class of windows satisfying the Nyquist criteria are studied, and the Kaiser window is studied
in [55]. Details about these and other windowing techniques can be seen in [58].
Though the self ICI cancelation method is simple to implement, each data symbol is carried
on two sub-carriers. Therefore, the data rate of the system and the frequency efficiency of the system
is reduced by half. In the windowing method, the functions used to cancel ICI have non-zero side

4.3. ICI CANCELATION SCHEMES

29

bands, leading to the addition of spurious bits and causing a loss in SNR. Thus, the choice of
canceling scheme leads to a trade-off between data rate and SNR, which is dictated by the system
design [43].

31

CHAPTER

Peak to Average Power Ratio

While the carrier frequency offset is a phenomenon that occurs due to frequency mismatch at the
receiver, high peak-to-average power ratio occurs at the transmitter due to summation of multiple
sinusoids. Occasionally, these sinusoids can add coherently to yield a very high amplitude compared to
the average amplitude, resulting in a large peak-to-average power ratio (PAPR). To ensure that these
peaks are transmitted without distortion, the power amplifier at the transmitter should be capable
of remaining linear over a wide range of input amplitudes. This presents a significant challenge in
terms of design, cost and power consumption. In this chapter, we describe this occurrence in further
detail and present schemes to minimize the effects of a high PAPR.

5.1

PROBLEM FORMULATION

An OFDM signal consists of a number of independently modulated SCs, which can result in a large
PAPR when added up coherently. The different carriers may align in phase at some instant in time,
and, therefore, they produce an amplitude peak equal to the sum of the amplitudes of the individual
carriers. This occurs with extremely low probability for large N.
The peak power is defined as the power of a sine wave with an amplitude equal to the maximum
envelope value. Hence, an unmodulated carrier has a PAPR of 0 dB. An alternative measure of the
envelope variation of a signal is the crest factor, which is defined as the maximum signal value divided
by the RMS signal value. For an unmodulated sinusoidal carrier, the crest factor is 3 dB. This 3 dB
difference between the PAPR and crest factor also holds for other non-sinusoidal carriers, provided
that the center frequency is large in comparison with the signal bandwidth. A large PAPR has
disadvantages like a requirement of increased complexity of analog-to-digital (A/D) and digital-toanalog (D/A) converters, and reduced efficiency of the RF power amplifier.
The output of the IFFT at the transmitter can be represented as


N 1
1 
j 2 kn
x[n] =
.
s[k] exp
N
N

(5.1)

k=0

Using this, the peak power of transmission can be expressed as



max |x[n]|2
n



1
1 N
N

j
2(k

k
)n
1
1
2
,
s[k1 ]s [k2 ] exp
= 2 max

N
N n
k1 =0 k2 =0

(5.2)

32

5. PEAK TO AVERAGE POWER RATIO

where |x[n]|2 = x[n]x [n]. Similarly, the average power can be expressed as



1
N
1 N




j 2(k1 k2 )n
1
.
s[k1 ]s [k2 ] exp
E |x[n]|2 = 2 E
N
N

(5.3)

k1 =0 k2 =0

The PAPR, using (5.2), and (5.3), can be expressed as



max |x[n]|2
n

 .
PAPR =
E |x[n]|2

(5.4)

As an example, consider the case when BPSK modulation

is' used, i.e., s[k] {1, 1}. For this case,
&
from (5.2), the peak transmit power is one, and E |x[n]|2 = 1/N, thereby leading to a PAPR of
N . For example, for an OFDM system employing 1024 subcarriers per transmitted symbol, the
PAPR= 1024 30dB, which is an extremely large range for the transmit power amplifier to vary
over.
The techniques proposed for PAPR reduction can be divided into three categories: signal
distortion techniques, coding techniques, and scrambling techniques. In signal distortion techniques,
nonlinear distortion is introduced in the OFDM signal at or around the peaks. Examples of distortion
techniques include clipping, peak windowing, and peak cancelation. Coding techniques use forward
error correcting codes that exclude OFDM symbols with a large PAPR. In scrambling techniques,
each OFDM symbol is scrambled with a different scrambling sequence, which is selected to yield
the smallest PAPR.
An example is shown in Figure 5.1 where several OFDM subcarriers are added together, as
transmitted. This leads to some peaks forming that are high, and consequently, a high PAPR. In the
example shown, it is assumed that an amplitude greater than a predefined threshold causes a PAPR
that is unacceptable. In the time section shown, there is one such peak.

5.2

PAPR MITIGATION METHODS

5.2.1

SIGNAL DISTORTION TECHNIQUES

Since large PAPR occurs rarely, the peaks can be removed at the cost of a slight amount of selfinterference. The simplest way to remove the peaks is by clipping the signal such that the peak
amplitude becomes limited to some predefined maximum level. By defining the highest accepted peak
value as the clipping threshold, any peak above this value will be clipped appropriately. Since clipping
can be viewed as a rectangular windowing operation in time, non-linear distortion introduced by
clipping, called self-interference, causes deterioration of the error rate performance of the system
and also significantly increases the out-of-band radiation levels. Due to the slow roll-off of the
spectrum of the rectangular window and the large side-lobes, the out-of-band radiation levels are
high. Different window shapes, other than rectangular, have been considered to minimize the outof-band radiation level, including the Gaussian, raised cosine, Kaiser and Hamming windows. To

5.2. PAPR MITIGATION METHODS

33

Amplitude - Sum of subcarriers

8

0
0

50

100

150

200

Time Index
250

Figure 5.1: Amplitude of transmitted OFDM symbol.

Figure 5.2: PAPR reduction by Peak Cancelation.

minimize the out-of-band interference, ideally, the window should be narrow in frequency and have
a fast roll-off with small side-lobes.
Peak cancelation can also be performed digitally. A comparator is used to check if the peak
amplitude of the digital OFDM symbol is above a predefined threshold, and if it is above the
threshold, the peak and the side lobes are scaled appropriately to maintain the PAPR to a predefined
value. Figure 5.2 shows the block diagram of an OFDM transmitter implementing peak cancelation.
As shown in Figure 5.2, the peak cancelation procedure is performed after the addition of the CP.

34

5. PEAK TO AVERAGE POWER RATIO

Peak cancelation can also be performed on a symbol-by-symbol basis immediately after the IDFT,
before adding the cyclic prefix and windowing.There is no change needed in the receiver architecture
for the digital peak cancelation technique.

5.2.2

CODING AND SCRAMBLING

Though peak cancelation offers a simple yet powerful technique to control the PAPR of an OFDM
system, an important drawback of this technique is that symbols with a large PAPR suffer more
degradation, so they are more vulnerable to errors. Given that the PAPR is high only once in
several OFDM symbols, another technique to minimize the effects of PAPR is error control coding.
By using codes with low rates, i.e., with high redundancy, errors caused by symbols with a large
degradation can be corrected by the surrounding symbols. The authors in [59], by exhaustively
searching all possible QPSK code words, have shown that for eight channels, a rate 3/4 convolution
code exists that provides a maximum PAPR of 3 dB. Also, in [59], it is illustrated that many of the
codes developed for PAPR reduction are Golay complementary sequences. Golay complementary
sequences are sequence pairs for which the sum of autocorrelation functions is zero for all delay shifts
not equal to zero [60, 61, 62]. In [63], the author presents a specific subset of Golay codes, together
with decoding techniques that combine PAPR reduction with good error correcting capabilities. But,
if the received signal is suffering from burst errors, then the initial transmission and the retransmission
might both have a large number of errors even with coding. To deal with this, scrambling techniques
are used to ensure that the transmitted data between initial transmission and retransmissions are
uncorrelated.
Symbol scrambling techniques to reduce the PAPR of a transmitted OFDM signal can be
seen as a special type of a PAPR reduction code. Symbol scrambling does not, however, try to
combine error correcting coding and PAPR reduction such as is done by complementary codes. The
basic idea of symbol scrambling is that for each OFDM symbol, the input sequence is permuted
by a set of scrambling sequences and the output signal with the smallest PAPR is transmitted. For
uncorrelated scrambling sequences, the resulting OFDM signals and corresponding PAPRs will be
uncorrelated, so if the PAPR for one OFDM symbol has a probability p of exceeding a certain level
without scrambling, the probability is decreased to p K by using K scrambling codes. Hence, symbol
scrambling does not guarantee a PAPR below some low level; rather, it decreases the probability
that high PAPRs will occur. Scrambling techniques were first proposed in [64] under the names
selected mapping and partial transmit sequences. The difference between the two is that the first
applies independent scrambling permutations to all SCs, while the latter only scrambles groups of
SCs.
Though all three of the above mentioned methods help reduce the PAPR of an OFDM
system [28], they each have drawbacks. While the coding method introduces redundancy and thereby
a loss in transmission data rate, clipping of the peak amplitude introduces non-linear distortion
into the system, and the scrambling method increases the complexity of the system and also the
transmission overhead due to the need to transmit the scrambling sequence resulting in the lower

5.2. PAPR MITIGATION METHODS

35

PAPR. Therefore, system design requirements are used to decide which of these schemes is used to
overcome the effects of high PAPR on the system.

37

CHAPTER

Simulation of the Performance

of OFDM Systems
6.1

PERFORMANCE OF AN OFDM SYSTEM

In the performance analysis of an OFDM system, we assume that the channel remains constant
for a certain length of time and then randomly changes to an independent value. This behavior
is termed as quasi-static fading. Assuming quasi-static channel and perfect synchronization at the
receiver leads to the received signals on the various subcarriers (SC) to be independent of each other.
Therefore, the channel on each SC can be equivalently represented as a flat fading channel with
additive white Gaussian noise (AWGN). The instantaneous signal to noise ratio (SNR) on each
subcarrier, within a block of quasi-static fading, can now be represented as
k = |H [k]|2 ,

(6.1)

where represents the average SNR on the subcarrier and H [k] represents the channel on the k th
subcarrier as given in (3.12). Note here that we have assumed that all SCs have the same average
SNR = Eb /No , where Eb represents the average energy per bit and No represents the height of
the noise spectral density expressed in the units of Watts per Hertz.
Further, given that the subcarriers are i.i.d., and assuming perfect channel knowledge at the
receiver, the probability of error, Pe , of the OFDM system can be expressed as the mean of the
probability of error of individual subcarriers, i.e.,
N
1 
Pe =
Pe [k],
N

(6.2)

k=1

where Pe [k] is the channel dependent instantaneous probability of error on the k th subcarrier. Pe [k]
depends on the modulation scheme chosen. For binary phase shift keying (BPSK) modulation [65,
Chap. 3], we have
( 
Pe [k] = Q
2k .
(6.3)
If instead, quadrature phase shift keying (QPSK) modulation is used, we have


k Q 2 k .
Pe [k] = 2Q

(6.4)

For other modulation schemes, instantaneous probability of error expressions can be found in [19].

38

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

To average the probability of error over time, the instantaneous value has to be averaged across
all possible values of the random variable H [k], i.e.,
'
&
Pe [k] = EH Pe [k] ,

(6.5)

where EH [] denotes the expectation operator with respect to H [k]. Therefore, using (6.5), the
average probability of error of a OFDM system can be expressed as
N
'
&
1 
Pe =
EH Pe [k] .
N

(6.6)

k=1

At the receiver, a maximum likelihood (ML) decoder is implemented. An ML decoder, as

the name implies, maximizes the likelihood of receiving a signal, y, conditioned on the transmitted
signal, s, and channel, H, i.e.,
s = argmin p(y|s, H) = argmax
{sk }

N
)

p(yk |sk , hk ).

(6.7)

k=1

For all simulations considered, we assume Rayleigh fading channels and additive white Gaussian noise (AWGN), thereby leading to an ML decoder of the form [19],
s = argmax y Hs2 .
s

(6.8)

For the case when frequency and timing synchronization is perfect at the receiver, ML decoder can
be expressed as
s [k] = argmin |y[k] H [k]s[k]|2 .

(6.9)

s[k]

Note here that the ML decoder can be represented as in (6.9) because there is no channel coding. Instead, if channel coding was employed then sequence detection algorithms like the Viterbi
algorithm [17] needs be used.
In the following section, we implement the Monte-Carlo method to calculate the average
probability of error of an OFDM system. The Monte-Carlo method is a numerical method to
estimate the ensemble average with respect to a random variable. Further information on how this
technique can be used to approximate the expected value can be found in [66, 67, 68, 69, 70].

6.2

SIMULATIONS

In what follows, MATLAB simulations are used to demonstrate the working of OFDM, based on
the theoretical development of OFDM has been presented previously.

6.2. SIMULATIONS

39

Figure 6.1: Basic OFDM model.

6.2.1

THE BASIC OFDM SYSTEM

In this section, the basic OFDM system is simulated. As discussed in the preceding chapters, a
basic OFDM system is constructed as shown in Figure 6.1. In this section, MATLAB code will be
provided for each of the blocks, with an explanation. The full program for the basic OFDM system
is then provided, including flexibility to vary several parameters and visualization options.
Data Generation and Modulation

Data is first generated to be transmitted over the OFDM system. Data of length N is randomly
generated and modulated as shown:
am = [-1,1];
M = 2;
dat_ind = ceil(M*rand(1,N));
data = am(dat_ind);
These lines of code generate a baseband representation of BPSK signals (1). The data is generated
from a source that generates the symbols with equal probability. It should be noted here that BPSK
is not the only mode of modulation, and any other modulation scheme can be used at this stage. For
example, if QPSK is used, the code can be modified as follows:
am = [1,1i,-1,-1i];
M = 4;
dat_ind = ceil(M*rand(1,N));
data = am(dat_ind);
IFFT
The first operation performed on the data is a N-point IFFT. In MATLAB, the IFFT function can
be used for this operation as follows:

data_t = ifft(data);
Add Cyclic Prefix

After the IFFT, the last few data-points are repeated at the beginning. The repeated data is called
the cyclic prefix.
data_cp = [data_t(end-CP_length+1:end), data_t];
Cyclic prefix of length CP_length is added to the beginning of the data block.

40

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

Channel

Transmission occurs over frequency selective fading channels.The channels are modeled as FIR filters
of order L. The channel, h, is assumed to have channel taps drawn from a Rayleigh distribution.
The power per tap is normalized, and the channel can be simulated as
h = complex(randn(L+1,1), randn(L+1,1))*sqrt(0.5/(L+1));
Noise is generated to be added to the transmission over the channel.The noise is zero-mean complex
Gaussian. The code below shows how to generate the noise:
noise = complex(randn(1,Total_length), randn(1,Total_length))*sqrt(0.5/N);
Since the channel is modeled as an FIR filter, the output of the channel is computed by filtering the
input signal with the channel, and noise is then added on to it. To adjust the SNR, the transmission
is scaled by a power value as shown:
rho = SNR;
rec = sqrt(rho)*(filter(h,1,data_cp))+noise
Remove Cyclic Prefix

The cyclic prefix is removed from the received data. The first CP_length symbols are discarded
from the received data:
rec_sans_cp = rec(CP_length+1:end)
FFT and Demodulation

The data extracted by discarding the cyclic prefix is transformed into the frequency domain.The data
in the frequency domain is then equalized to account for the channel, and to yield the final received
signal. It should be noted here that the FFT used here to convert the channel into the frequency
domain is normalized by the number of channel taps. The received signal is then demodulated
to obtain an estimate of the transmitted signals. In the following code, it is assumed that BPSK
modulation has been used at the transmitter:
rec_f = fft(rec_sans_cp); % FFT
h_f = sqrt(rho)*fft(h,N); % Equivalent channel on each subcarrier
det1 = abs(rec_f+h_f).2; % Calc the Euclidean dist
% assuming -1 was transmitted
det2 = abs(rec_f-h_f).2; % Calc the Euclidean dist
% assuming +1 was transmitted
det = [det1, det2];
% Concatenating the two vectors
% Find the symbol the received signal is closest to
[min_val, ind] = min(det, [], 2);

6.2. SIMULATIONS

dec = 2*((ind-1)>0.5)-1;

41

% BPSK decoding

For QPSK decoding:

rec_f = fft(rec_sans_cp); % FFT
% Equivalent channel on each subcarrier
h_f = sqrt(rho)*fft(h,N);
% Calc the Euclidean dist assuming 1 was transmitted
det1 = abs(rec_f-am(1)*h_f).2;
% Calc the Euclidean dist assuming +1i was transmitted
det2 = abs(rec_f-am(2)*h_f).2;
% Calc the Euclidean dist assuming -1 was transmitted
det3 = abs(rec_f-am(3)*h_f).2;
% Calc the Euclidean dist assuming -1i was transmitted
det4 = abs(rec_f-am(4)*h_f).2;
% Concatenating the vectors
det = [det1, det2, det3, det4];
% Find the symbol the received signal is closest to
[min_val, ind] = min(det, [], 2);
% Generating the decoded symbols
dec = am(ind);
The received symbols are decoded using maximum likelihood (ML) estimation.
6.2.1.1 OFDM Simulation

A Monte-Carlo simulation is performed to estimate the probability of error of the received signals
when transmission occurs using OFDM. The SNR of the channels is varied and the probability
of error is estimated for each case of SNR. The program for the full simulation model is provided.
In the code provided, it is assumed that data is transmitted in frames, and each frame consists of B
OFDM symbols. This allows us to evaluate the effects of inter-block-interference when the length
of the cyclic prefix is less than the number of channel taps. The simulation results are presented for
different parameters, which can be changed within the code.

42

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

clear all
clc
N = 16;
% Number of subcarriers in each OFDM symbol
L = 3;
% Channel order
CP_length = 4;
% Cyclic prefix length
B = 10;
% Number of OFDM symbols per transmitted frame
mc_N = 5000;
% Number of iterations to achieve sufficient errors
SNR_db = 0:2:20;
%SNR in dB
SNR = 10.(SNR_db/10);
% SNR values
Pe = zeros(size(SNR_db));
% Initializing the error vector
Total_length = (CP_length+N)*B;
% Total length of each frame
am = [-1,1];
% For BPSK
M = 2;
% For BPSK
for SNR_loop = 1:length(SNR_db)
rho = SNR(SNR_loop);
err = 0;
for mc_loop = 1:mc_N
dat_ind = ceil(M*rand(B,N));
data = am(dat_ind);
% Reshaping the data into a BxN matrix,...
%...used later for error detection
data_reshape = reshape(data, 1, B*N);
tx_data = data;
for b = 1:B
% Taking the IFFT
data_t(b,:) = ifft(tx_data(b,:));
end
% Adding Cyclic prefix
data_cp = [data_t(:,end-CP_length+1:end), data_t];
% Reshape the BxN matrix to obtain the frame (1xTotal_length)
data_tx =reshape(data_cp.,1,Total_length);

6.2. SIMULATIONS

h = complex(randn(L+1,1), randn(L+1,1))*sqrt(0.5/(L+1));
%Noise
noise = complex(randn(1,Total_length), ...
randn(1,Total_length)) * sqrt(0.5/N);
% Received signal
rec = sqrt(rho)*(filter(h,1,data_tx))+noise;
% Reshape the recd signal into CP_length+N x B array
rec_reshaped = (reshape(rec, CP_length+N, B)).;
% Remove CP
rec_sans_cp = rec_reshaped(:,CP_length+1:end);
for bb = 1:B
% Taking the FFT
rec_f(bb,:) = fft(rec_sans_cp(bb,:));
end
% Calculating the equivalent channel on each subcarrier
h_f = sqrt(rho)*fft(h,N);
for b2 = 1:B
% Extracting the OFDM symbol from the "rec_f" matrix
rec_symbol = transpose(rec_f(b2,:));
% Calc Euclidean dist assuming -1
det1 = abs(rec_symbol+h_f).2;
% Calc Euclidean dist assuming +1
det2 = abs(rec_symbol-h_f).2;
% Concatenate the two vectors
det = [det1, det2];
% Find symbol the recd signal is closest to
[min_val, ind] = min(det, [], 2);

43

44

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

% Generate the decoded symbols

dec(b2,:) = 2*((ind-1)>0.5)-1;
end
% Reshape the decoded symbols to calc error
dec_reshape = reshape(dec, 1, B*N);
% Comparing dec_reshape against...
%...data_reshape to calculate errors
err = err + sum(dec_reshape=data_reshape);
end
% Calculate the probability of error
Pe(SNR_loop) = err/(mc_N*B*N);
end
% Semilog plot of Pe vs. SNR_db
semilogy(SNR_db,Pe)
Figure 6.2 shows the output of the code for two modulation schemes, BPSK and QPSK. It is
shown that the performance of the system remains the same irrespective of the number of subcarriers,
as long as the number of symbols in the cyclic prefix is at least as many as the number of channel
taps. Furthermore, as expected from digital communications, the symbol error rate of the QPSK
system is worse than the BPSK case. In Figure 6.3, the system is simulated for QPSK modulation.
Three curves are plotted, each for a different length of cyclic prefix. It can be seen that as the length
of the cyclic prefix reduces, inter-block-interference (IBI) causes deterioration in performance.

6.2.2

CARRIER FREQUENCY OFFSET

Carrier frequency offset and its drawbacks were discussed in Chapter 4. In this Section, we will
provide simple code to simulate the effect of frequency offset and demonstrate its effect on the BER
performance of the OFDM system. More detailed treatment can be found in [71, 72].
6.2.2.1 Simulation

The frequency offset is provided as a complex exponential multiplier for each subcarrier. This simulates the effect of having the frequency of each subcarrier being offset at the receiver by a small
amount. In the code that follows, the frequency offset is varied, and the BER is calculated for each
of those values, to demonstrate the effect of CFO on performance of OFDM systems. Similar to
the previous cade, the code is flexible and different parameters such as modulating techniques, levels
of offset and number of Monte-Carlo iterations can be changed.

6.2. SIMULATIONS

45

L = 3, CP_length = 4, D = 256
L = 16, CP_length = 17, D = 256
L = 16, CP_length = 17, D = 1024

QPSK
Symbol Error Rate

10

BPSK
2

10

10

5
SNR in dB

10

15

20

Figure 6.2: Simulation of the basic OFDM model. Plot shows effect of SNR on the probability of error.
QPSK
L = 16, CP_length = 16, N = 256
L = 16, CP_length = 9, N = 256
L = 16, CP_length = 4, N = 256
L = 16, CP_length = 9, N = 1024
L = 16, CP_length = 4, N = 1024
1

Symbol Error Rate

10

10

10

15

20

SNR in dB

Figure 6.3: Simulation of the basic OFDM model, showing the need for cyclic prefix.

46

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

clear all
clc
N = 16;
L = 3;
CP_length = 4;
B = 1;
mc_N = 5000;
SNR_db = 5;

%
%
%
%
%
%

Number of subcarriers in each OFDM symbol

Channel order
Cyclic prefix length
Number of OFDM symbols per transmitted frame
Number of iterations to achieve sufficient errors
SNR in dB

SNR = 10.(SNR_db/10); % SNR

Pe = zeros(size(SNR_db));
Total_length = (CP_length+N)*B; % Total length of each frame
am = [-1,1];
% For BPSK
M = 2;
% For BPSK
freq_offset = [-0.5:0.01:0.5];
for off_loop = 1:length(freq_offset)
rho = SNR;
err = 0;
for mc_loop = 1:mc_N
dat_ind = ceil(M*rand(B,N));
data = am(dat_ind);
% Reshaping the data into a BxN matrix,...
%...used later for error detection
data_reshape = reshape(data, 1, B*N);
tx_data = data;
for b = 1:B
% Taking the IFFT
data_t(b,:) = ifft(tx_data(b,:));
end

6.2. SIMULATIONS

% Adding Cyclic prefix

data_cp = [data_t(:,end-CP_length+1:end), data_t];
% Reshape the BxN matrix to...
%...obtain the frame (1xTotal_length)
data_tx =reshape(data_cp.,1,Total_length);
h = complex(randn(L+1,1), randn(L+1,1))*sqrt(0.5/(L+1));
noise = complex(randn(1,Total_length), ...
randn(1,Total_length)) *sqrt(0.5/N); %Noise
rec = sqrt(rho)*(filter(h,1,data_tx))...
* exp(-1i*2*pi*freq_offset(off_loop))+noise;
% Reshape the recd signal...
%...into CP_length+N x B array
rec_reshaped = (reshape(rec, CP_length+N, B)).;
% Remove CP
rec_sans_cp = rec_reshaped(:,CP_length+1:end);
for bb = 1:B
% Taking the FFT
rec_f(bb,:) = fft(rec_sans_cp(bb,:));
end
% Calculating the equivalent channel on each subcarrier
h_f = sqrt(rho)*fft(h,N);
for b2 = 1:B
% Extracting the OFDM symbol from...
%...the "rec_f" matrix
rec_symbol = transpose(rec_f(b2,:));
% Calc Euclidean dist assuming -1
det1 = abs(rec_symbol+h_f).2;
% Calc Euclidean dist assuming +1
det2 = abs(rec_symbol-h_f).2;

47

48

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

% Concatenate the two vectors

det = [det1, det2];
% Find symbol the recd signal is closest to
[min_val, ind] = min(det, [], 2);
% Generate the decoded symbols
dec(b2,:) = 2*((ind-1)>0.5)-1;
end
% Reshape the decoded symbols to calc error
dec_reshape = reshape(dec, 1, B*N);
% Compare dec_reshape against...
%...data_reshape to calc errors
err = err + sum(dec_reshape=data_reshape);
end
% Calculating the probability of error
Pe(off_loop) = err/(mc_N*B*N)
end
%Semilog plot of Pe vs. offset
semilogy(freq_offset,Pe)
The code provided yields a value for the probability of error at each value of frequency offset.
The value of probability of error is plotted for each value of offset. As expected, the best performance
is obtained for a zero offset. As the offset increases in either direction, the performance deteriorates,
and the performance is symmetric in the offset about the zero offset point. This plot for a channel
SNR of 5dB is shown in Figure 6.4.

6.2.3

PAPR SIMULATIONS

The effect of high peak-to-average-power ratio (PAPR) on the performance of OFDM systems was
seen in Chapter 4. In this simulation, the transmitted signal is simulated to provide an example of
the transmit power over a few time samples and to demonstrate the occurrence of high PAPR. In
this simulation, the base-band equivalent model of an OFDM system is considered. At each time
instant, data is BPSK modulated and transmitted using OFDM. The instantaneous peak transmit
power and the average transmit power are calculated and plotted in Figure 6.5. The number of
subcarriers used is 16. It can be verified from (5.3) that the average power of the system must be

6.2. SIMULATIONS

49

QPSK, N = 256

0.1

10

0.2

Symbol Error Rate

10

0.3

10

0.4

10

0.5

10

0.6

10

0.7

10

0.5

0.4

0.3

0.2 0.1
0
0.1
0.2
Frequency Oset - f (rads)

0.3

0.4

0.5

Figure 6.4: Bit error rate vs. phase offset

2
Average Power
Peak Power
4

Power (dB)

10

12

14

20

40

60

80

100

Time index

Figure 6.5: Peak power due to addition of in-phase sinusoids, compared with the average power of the
transmissions.

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

10
9
8
7
PAPR (dB)

50

6
5
4
3
2

20

40

60

80

100

Time index

Figure 6.6: Peak to average power ratio compared against a threshold.

-12dB as can be seen from the figure. The PAPR is also calculated and plotted in Figure 6.6. As can
be seen from (5.4), the maximum possible value of PAPR is 12dB, and in the case of this simulation,
the system does not exceed this value. In fact, the largest PAPR in this case approaches 10dB. A
predetermined threshold is also shown, and the PAPR exceeds this threshold at one point.
clear all
N = 16; % Length of data
SNR_db = 5;
SNR = 10.(SNR_db/10);
Pe = zeros(size(SNR_db));
noise_var = 1/SNR;
time_samples = 100;
avg_pow = zeros(1,time_samples);
mx_pow = zeros(1,time_samples);
papr = zeros(1,time_samples);
for time_loop = 1:time_samples
data = 2*(randn(N,1)>0)-1;
data_t = ifft(fftshift(data));

6.2. SIMULATIONS

51

avg_pow(time_loop) = (norm(data_t))2/N;
mx_pow(time_loop) = max(data_t.*conj(data_t));
papr(time_loop) = mx_pow(time_loop)/avg_pow(time_loop);
end
figure(1)
plot(10*log10(avg_pow))
hold all
plot(10*log10(mx_pow))
figure(2)
plot(10*log10(papr))
One of the ways of limiting PAPR at the transmitter is by clipping signals that exceed a
certain level. In the code provided, an OFDM system is considered with such a clipping systems.
Different clipping thresholds are considered to show the effect of clipping level on performance. In
this example, the system considered has no additive noise, and the channel is frequency flat with
gain one.
clear all
clc
N = 16;
% Number of subcarriers in each OFDM symbol
L = 3;
% Channel order
CP_length = 4;
% Cyclic prefix length
B = 10;
% Number of OFDM symbols per transmitted frame
mc_N = 50;
% Number of iterations to achieve sufficient errors
th_var = 0:.1:1; % Clipping thresholds
Pe = zeros(size(th_var));
% Initializing the error vector
Total_length = (CP_length+N)*B; % Total length of each frame
am = [1,1i,-1,-1i];
% For QPSK
M = 4;
% For QPSK
for th_loop = 1:length(th_var)
th = th_var(th_loop);
err = 0;
for mc_loop = 1:mc_N
dat_ind = ceil(M*rand(B,N));
data = am(dat_ind);
% Reshape the data into a BxN matrix,...

52

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

%...used later for error detection

data_reshape = reshape(data, 1, B*N);
tx_data = data;
for b = 1:B
% Taking the IFFT
data_t(b,:) = ifft(tx_data(b,:));
end
% Adding Cyclic prefix
data_cp = [data_t(:,end-CP_length+1:end), data_t];
% Reshaping the BxN matrix to...
%...obtain the frame (1xTotal_length)
data_tx =reshape(data_cp.,1,Total_length);
thu = abs(th);
thl = -abs(th);
data_clip = data_tx;
pt_high = find(data_clip>thu);
data_clip(pt_high) = thu;
pt_low = find(data_clip<thl);
data_clip(pt_low) = thl;
rec = data_clip;
% Reshape the recd signal...
%...into CP_length+N x B array
rec_reshaped = (reshape(rec, CP_length+N, B)).;
% Remove CP
rec_sans_cp = rec_reshaped(:,CP_length+1:end);
for bb = 1:B
%Taking the FFT
rec_f(bb,:) = fft(rec_sans_cp(bb,:));
end

6.2. SIMULATIONS

for b2 = 1:B
% Extracting the OFDM symbol...
%...from the "rec_f" matrix
rec_symbol = transpose(rec_f(b2,:));
% Calc the Euclidean dist assuming 1
det1 = abs(rec_symbol-am(1)).2;
% Calc the Euclidean dist assuming +1i
det2 = abs(rec_symbol-am(2)).2;
% Calc the Euclidean dist assuming -1
det3 = abs(rec_symbol-am(3)).2;
% Calc the Euclidean dist assuming -1i
det4 = abs(rec_symbol-am(4)).2;
% Concatenating the vectors
det = [det1, det2, det3, det4];
% Find symbol the recd signal is closest to
[min_val, ind] = min(det, [], 2);
% Generating the decoded symbols
dec(b2,:) = am(ind);
end
% Reshape decoded symbols to calc error
dec_reshape = reshape(dec, 1, B*N);
% Compare dec_reshape against data_reshape to calculate errors
err = err + sum(dec_reshape=data_reshape);
end
% Calculate the probability of error
Pe(th_loop) = err/(mc_N*B*N);
end
% Semilog plot of Pe vs. clipping threshold
semilogy(th_var,Pe)

53

6. SIMULATION OF THE PERFORMANCE OF OFDM SYSTEMS

Figure 6.7 shows the effect of clipping on the performance of an OFDM system. As expected,
as the clipping threshold becomes smaller, the performance of the system deteriorates even though
it is a noiseless, unit-gain frequency-flat channel. This is the trade-off between performance and
power consumption. In order to keep the power consumption of the amplifier at the transmitter
low, the clipping threshold is lowered, and this leads to poor performance. On the other hand, if the
clipping threshold increases, the BER is lowered, but the power consumption increases.
BP SK Modul ati on - N = 16, CP l e ngth = 4

10

10

10
BER

54

10

10

10

0.05

0.1

0.15
0.2
0.25
Clipping threshold

Figure 6.7: Effect of clipping on the BER of an OFDM system.

0.3

0.35

0.4

55

CHAPTER

Conclusions
This book examined and analyzed various aspects of Orthogonal Frequency Division Multiplexing
(OFDM), including the use of OFDM in various current standards that demand high data rates
and very low error rates when transmitting over wireless multipath channels. Challenges to high
data rate and low error rate transmissions are analyzed by examining the characteristics of wireless
communication channels. It is known that a frequency selective fading model best fits a wireless
multipath channel. Equalization of the multipath channel in the frequency domain instead of the
time domain is presented, and it is exploited in the design of multicarrier systems. A simple frequency domain multiplexing scheme is first described as a possible solution for communication over
frequency selective channels. The drawbacks of this method are used to motivate the development
of OFDM, which is then described in detail.
Using the FFT as a means for transformation from the time domain to the frequency domain,
a discrete time OFDM baseband system can be easily developed. Since implementation of FFT is
inexpensive in terms of simplicity and cost, and efficient in terms of computation speed, OFDM has
become a popular choice for communication over frequency selective fading channels. An OFDM
system is designed to have orthogonal subcarriers, and each subcarrier sees a flat fading channel.
While this simplicity is an obvious advantage, OFDM does have weaknesses. Two main pathologies
of the OFDM communication scheme, carrier frequency offset (CFO) and high peak to average
power ratio (PAPR), are presented. When there is an offset in frequency between the carrier and
the local oscillator at the receiver, the subcarriers do not remain orthogonal, leading to ICI. A high
PAPR of the signal at the transmitter makes the design of efficient RF amplifiers difficult. Both these
pathologies cause deterioration of the system performance. Techniques to alleviate their effect on
performance are described. Several standards that employ OFDM are provided, along with typical
operational values that these systems use.
Finally, a system employing OFDM to transmit data modulated using binary phase shift
keying (BPSK) and quadrature phase shift keying (QPSK) modulation is simulated. Monte-Carlo
type simulations are employed to evaluate the performance of these systems in terms of the probability
of error. Additionally, the effect of CFO on the error rate performance is illustrated. Lastly, the PAPR
of an OFDM system is computed and shown to illustrate the fluctuations in transmit power. All of
these simulations are performed in MATLAB. Programs used for these simulations are also provided.

57

APPENDIX

Abbreviations
A/D
AWGN
BER
BPSK
CFO
CP
D/A
DAB
DFT
DMB-T
DVB-T
FDM
FFT
FIR
HiperLAN
IBI
IEEE
IFFT
ISDB
ISI
LAN
LOS
MAN
MC
OFDM
PAPR

Analog to Digital
Additive White Gaussian Noise
Bit Error Rate
Binary Phase Shift Keying
Carrier Frequency Offset
Cyclic Prefix
Digital to Analog
Digital Audio Broadcast
Discrete Fourier Transform
Digital Multimedia Broadcasting-Terrestrial
Digital Video Broadcasting-Terrestrial
Frequency Division Multiplexing
Fast Fourier Transform
Finite Impulse Response
High Performance LAN
Inter-Block-Interference
Institute of Electrical and Electronics Engineers
Inverse Fast Fourier Transform
Integrated Services Digital Broadcasting
Inter-Symbol-Interference
Local Area Network
Line of Sight
Metropolitan Area Network
Multi-Carrier
Orthogonal Frequency Division Multiplexing
Peak to Average Power Ratio

58

A. ABBREVIATIONS

PDF
QAM
QPSK
RMS
SC
SNR
WLAN
Wi-Fi
ZP

Probability Distribution Function

Quadrature Amplitude Modulation
Quadrature Phase Shift Keying
Root Mean Square
Subcarrier
Signal to Noise Ratio
Wireless Local Area Network
Wireless Fidelity Alliance
Zero Padded

59

APPENDIX

Notations



:=
E[]
In ()
|x|

[]T
[]H
FN
Rcp
Tcp
f
[]
Tzp
IN
0MN
max
avg
rms
S(, f )

Greater than or equal to

Less than or equal to
Much greater than
Much smaller than
Is defined as
Expectation operator
nth order Modified Bessel function
Absolute value
Linear Convolution
Transpose operator
Hermitian or the conjugate transpose operator
N N DFT matrix
Matrix used for removing cyclic prefix
Matrix used for adding cyclic prefix
Frequency offset
Kronecker delta function
Matrix to zero pad
Identity matrix of dimension N N
Zero Matrix of dimension M N
Maximum path delay
Average path delay
RMS path delay
Scattering function with delay and frequency f as parameters

61

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Authors Biographies
ADARSH NARASIMHAMURTHY
Adarsh Narasimhamurthy is a Ph.D. candidate at the School of Electrical, Computer and Energy
Engineering in Arizona State University, Tempe. He obtained his B.E. with distinction in 2005
from the Bangalore University, Karnataka, India and the M.S. degree in Electrical Engineering
from Arizona State University, Tempe in 2007. Currently, he is a part of the Signal Processing for
Wireless Communications Lab headed by Dr. Cihan Tepedelenlioglu and also a member of the
SenSIP consortium.
Mr. Narasimhamurthy was awarded a Research Assistantship in the year 2006 and the subsequent year a Graduate Teaching Associate position from the Department of Electrical Engineering.
His research interests include MIMO systems, OFDM systems, reduced complexity diversity combining techniques and multiuser communication. Mr. Narasimhamurthy is also a member of the Eta
Kappa Nu honor society and an IEEE student member.

MAHESH K. BANAVAR
Mahesh K. Banavar received the B.E. degree in telecommunications engineering from Visvesvaraya
Technological University, Karnataka, India, in 2005 and the M.S. degree in electrical engineering
from Arizona State University, Tempe, in 2008. He is pursuing the Ph.D. degree with Arizona State
University, specializing in Signal Processing and Communications, and doing research in wireless
communications and sensor networks.
Mr. Banavar is a member of the Eta Kappa Nu electrical and computer engineering honor
society.

CIHAN TEPEDELENLIOGLU
CihanTepedelenlioglu was born in Ankara,Turkey in 1973. He received his B.S. degree with highest
honors from Florida Institute of Technology in 1995, and his M.S. degree from the University of
Virginia in 1998, both in Electrical Engineering. From January 1999 to May 2001 he was a research
assistant at the University of Minnesota, where he completed his Ph.D. degree in Electrical and
Computer Engineering. He is currently an Associate Professor of Electrical Engineering at Arizona
State University. He was awarded the NSF (early) Career grant in 2001, and has served as an
Associate Editor for several IEEE Transactions including IEEE Transactions on Communications,
and IEEE Signals Processing Letters.

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AUTHORS BIOGRAPHIES

His research interests include statistical signal processing, system identification, wireless communications, estimation and equalization algorithms for wireless systems, multi-antenna communications, filterbanks and multirate systems, OFDM, ultra-wideband systems, distributed detection
and estimation.