Peak to Average Power Ratio (Papr) Reduction in Ofdm Based Radio Systems_3

PEAK TO AVERAGE POWER RATIO (PAPR) REDUCTION IN

OFDM BASED RADIO SYSTEMS

Mohammad Zavid Parvez

Md. Abdullah Al Baki

This thesis is presented as part of Degree of Master of Science in Electrical Engineering

Blekinge Institute of Technology

May 2010

Blekinge Institute of Technology School of Engineering Department of Signal Processing Supervisor : Professor Abbas Mohammed Examiner : Professor Abbas Mohammed

MEE10:12

Blekinge Institute of Technology (BTH), Sweden Page 2

Dedication To

Our Parents And

Teachers


Abstract

High data rate wireless access is demanded by many applications. Usually,

more bandwidth is required for higher data rate transmission in any of the

system. With promising technology and ever-increasing wireless devices, the

spectrum is becoming scarcer day by day. In this case, using Orthogonal

Frequency Division Multiplexing (OFDM) and Cognitive Radio (CR) for

spectrally efficient transmission are an alternative solution.

OFDM is a bandwidth efficient multicarrier modulation where the available

spectrum is divided into subcarriers, with each subcarrier containing a low rate

data stream. OFDM has gained a tremendous interest in recent years because

of its robustness in the presence of severe multipath channel conditions with

simple equalization, robustness against Inter-symbol Interference (ISI),

multipath fading, in addition to its high spectral efficiency. However, the Peak-

to-Average Power Ratio (PAPR) is a major drawback of multicarrier

transmission system such as OFDM.

CR can be defined as an intelligent wireless system that is always alert about its

surrounding environment through sensing and should have ability to

dynamically adjust its radio operation parameters. The CR demands that the

physical layer (PHY) needs to be adaptable and flexible.

For flexibility and adaptability, the OFDM is an attractive candidate for CR

systems. This dissertation proposes a novel non-contiguous OFDM (NC-

OFDM) technique, where the implementation achieves high data rates of non-

contiguous subcarriers while simultaneously avoiding any interference to the

transmissions.

In this dissertation we apply different modulation techniques to reduce high

PAPR for non-contiguous bands spectrum of OFDM based CR. The simulation

results for PAPR reduction shows that higher modulation techniques are better

compared to lower modulation techniques.


Acknowledgement

All praises to almighty ALLAH who give us strength and abilities to complete

this thesis work successfully.

We would like to give our sincere gratitude to our honourable supervisor Prof.

Abbas Mohammed for his assistance and good guidance time after time which

made our thesis work become more precise and attractive. He supervises us and

gives his spare time to discuss more about the problem of thesis work.

We are grateful to our beloved parents for their love and continuous support

during the thesis work at BTH until finish.

Finally, we would like to thanks again to almighty ALLAH, for keeping us good

understanding and relationship in between us through this thesis work that is

more improved and constructive to do better.


Table of Contents

Abstract ................................................................................................................................................... 3

Acknowledgement .................................................................................................................................. 4

Table of Contents .................................................................................................................................... 5

List of Figures .......................................................................................................................................... 8

List of Tables ........................................................................................................................................... 9

List of Abbreviations ............................................................................................................................. 10

Chapter One: Introduction .................................................................................................................... 14

1.1 Motivation ................................................................................................................................... 14

1.2 Thesis out Lines .......................................................................................................................... 15

Chapter Two: Orthogonal Frequency Division Multiplexing (OFDM) ................................................... 16

2.1 Introduction ................................................................................................................................. 16

2.2 Advantages and Disadvantages of OFDM System ..................................................................... 17

2.2.1 Advantages of OFDM .......................................................................................................... 17

2.2.2 Disadvantages of OFDM ..................................................................................................... 18

2.3 OFDM System Model ................................................................................................................. 18

2.4 Mathematical Definition of OFDM Signal ................................................................................. 20

2.5 NC-OFDM System Model .......................................................................................................... 21

2.6 Why PAPR reduction in OFDM system ..................................................................................... 22

2.7 Mathematical Definition of PAPR .............................................................................................. 23

2.8 PAPR Techniques ....................................................................................................................... 23

2.8.1 Signal Scrambling Techniques ............................................................................................. 24

2.8.1.1 Block Coding Techniques ............................................................................................. 24

2.8.1.2 Block Coding Scheme with Error Correction ............................................................... 24

2.8.1.3 Selected Mapping (SLM) .............................................................................................. 25

2.8.1.4 Partial Transmit Sequence (PTS) .................................................................................. 25

2.8.1.5 Interleaving Technique ................................................................................................. 25

2.8.1.6 Tone Reservation (TR) .................................................................................................. 26

2.8.1.7 Tone Injection (TI) ........................................................................................................ 26

2.8.2 Signal Distortion Techniques ............................................................................................... 27

2.8.2.1 Peak Windowing ........................................................................................................... 27


2.8.2.2 Envelope Scaling........................................................................................................... 27

2.8.2.3 Peak Reduction Carrier ................................................................................................. 27

2.8.2.4 Clipping and Filtering ................................................................................................... 28

2.9 Overall Analysis of Different Techniques .................................................................................. 29

2.10 Conclusion ................................................................................................................................ 30

Chapter Three: Cognitive Radio ............................................................................................................ 31

3.1 Introduction ................................................................................................................................. 31

3.2 The Features of CR ..................................................................................................................... 32

3.3 The Evolution of Radio Technology ........................................................................................... 33

3.4 Software Defined Radio (SDR) .................................................................................................. 34

3.4.1 Benefits of SDR ................................................................................................................... 34

3.4.2 Relationship between SDR and CR ..................................................................................... 35

3.4.3 Ideal SDR Architecture for CRs .......................................................................................... 36

3.5 Spectrum Sensing ........................................................................................................................ 37

3.5.1 Different Techniques of Spectrum Sensing in CR ............................................................... 37

3.5.1.1 Primary Transmitter Detection ...................................................................................... 38

3.5.1.1.1 Matched Filter Detection ....................................................................................... 39

3.5.1.1.2 Energy Detection .................................................................................................... 39

3.5.1.1.3 Cyclostationary Detection ...................................................................................... 40

3.5.1.2 Cooperative Detection ................................................................................................... 42

3.5.1.3 Primary Receiver Detection .......................................................................................... 42

3.5.1.4 Interference Temperature Management ........................................................................ 43

Chapter Four: OFDM Based Cognitive Radio ........................................................................................ 44

4.1 Merits and Challenges for OFDM Based CR ............................................................................. 44

4.2 OFDM Based CR Architecture ................................................................................................... 44

4.3 Proposed System ......................................................................................................................... 47

Chapter Five: Channel Models .............................................................................................................. 49

5.1 Introduction ................................................................................................................................. 49

5.2 Additive White Gaussian Noise (AWGN) Channel .................................................................... 49

5.3 Rayleigh fading channel.............................................................................................................. 50

5.3.1 Mathematical Expression ..................................................................................................... 51

Chapter Six: Simulation and Results ..................................................................................................... 53

6.1 Introduction ................................................................................................................................. 53

6.2 Mathematical Definitions ............................................................................................................ 53


6.3 PAPR Reduction ......................................................................................................................... 54

6.4 BER calculation for AWGN channel .......................................................................................... 57

6.5 BER calculation for Rayleigh fading channel ............................................................................. 59

6.6 BER calculation for Different channels ...................................................................................... 61

Chapter Seven: Conclusion ................................................................................................................... 63

References ............................................................................................................................................ 64


List of Figures

Figure 2. 1-OFDM subcarriers in frequency domain ......................................................... 17

Figure 2. 2-A basic diagram of OFDM Transceiver ........................................................... 19

Figure 2. 3 NC-OFDM Transceiver ..................................................................................... 22

Figure 3. 1-SDR intensity ...................................................................................................... 33

Figure 3. 2 -The relationship of SDR and Cognitive Radio ............................................... 35

Figure 3. 3 -Ideal software defined radio architecture ....................................................... 36

Figure 3. 4 -Primary transmitter detection ......................................................................... 38

Figure 3. 5 -Implementation of an energy detector using Welch Periodgram averaging40

Figure 3. 6 -Implementation of a cyclostationary feature detector ................................... 41

Figure 4. 1- Research challenges in Cognitive Radio and OFDM [10] ............................. 45

Figure 4. 2- OFDM base Cognitive Radio system block diagram [10] ............................. 46

Figure 4. 3 -Proposed block diagram of NC-OFDM based Cognitive Radio ................... 47

Figure 5. 1- AWGN channel .................................................................................................. 50

Figure 6. 1- PAPR reduction using BPSK with clipping and filtering .............................. 54

Figure 6. 2- PAPR reduction using QPSK with clipping and filtering ............................. 55

Figure 6. 3- PAPR reduction using QAM16 with clipping and filtering .......................... 56

Figure 6. 4- BER Vs SNR using BPSK through AWGN channel ...................................... 57

Figure 6. 5- BER Vs SNR using QPSK through AWGN channel ..................................... 58

Figure 6. 6- BER Vs SNR using BPSK through Rayleigh fading channel ....................... 59

Figure 6. 7- BER Vs SNR using QPSK through Rayleigh fading channel ....................... 60

Figure 6. 8- BER Vs SNR using BPSK through two different channels ........................... 61

Figure 6. 9- BER Vs SNR using QPSK through two different channels .......................... 62


List of Tables

Table 2. 1-Comparison of PAPR Reduction Techniques ................................................... 29

Table 3. 1- Comparison different techniques of spectrum sensing ................................... 42

Table 4. 1- OFDM Based Wireless Standards ..................................................................... 46


List of Abbreviations

A

AWGN Additive White Gaussian Noise ATSC Advanced Television Systems Committee ADSL Asymmetric Digital Subscriber Line AI Adaptive Interleave ADC Analog to Digital Converter DAC Digital to Analog Converter

B

BPSK Binary Phase Shift Keying BER Bit Error Rate BSC Binary Symmetric Channel BO Back Off

C

CR Cognitive Radio COBSC Combination Optimized Sub-Block Coding Scheme CDMA Code Division Multiple Access CP Cyclic Prefix CDF Cumulative Distribution Function

D

DSP Digital Signal Processing DVB Digital Video Broadcasting DFS Dynamic Frequency Selection DARPA Defense Advanced Research Project Agency DFT Discrete Fourier Transform DTV Digital Television

E


EDGE Enhance Data GSM Environment

F

FFT Fast Fourier Transform FDM Frequency Division Multiplexing FDMA Frequency division Multiple Access FCC Federal Communication Commission FPGA Field Programmable Gate Arrays FEC Forward Error Correction

G

GPP General Purpose Processors GSM Global System for Mobile Communication

H

HiperLAN High Performance Radio LAN

I

ISI Inter Symbol Interference ICI Inter Carrier Interference IEEE Institute of Electrical and Electronic Engineers IFFT Inverse Fast Fourier Transform IDFT Inverse Discrete Fourier Transform

L

LTE Long Term Evolution LAN Local Area Network LO Local Oscillator LOS Line Of Sight


M

MIMO Multiple Input Multiple Output MISO Multiple Input Single Output MAC Media Access Control

N

NPRM Notice of Proposed Rule Making

O

OFDM Orthogonal Frequency Division Multiplexing OFDMA Orthogonal Frequency Division Multiple Access

P

PHY Physical Layer PTS Partial Transmit Sequences PSK Phase Shift Keying PEPs Peak Envelope Powers PA Power Amplifier LNA Low Noise Amplifiers PAPR Peak to Average Power Ratio

Q

QPSK Quadrature Phase Shift Keying QAM Quadrature Amplitude Modulation

R RBLO-SBC Redundant Bit Location Optimized Sub-Block Coding RF Radio Frequency


S

SDR Software Defined Radio SNR Signal to Noise Ratio SLM Selective Level Mapping SoC System on Chip SISO Single Input Single Output SIMO Single Input Multiple Output

T

TR Tone Reservation TI Tone Injection TDMA Time Division Multiple Access

U

UWB Ultra Wide Band U-NII Unlicensed National Information Infrastructure

V

VLSI Very Large Scale Integration

W

WiMAX Worldwide Interoperability for Microwave Access Wi-Fi Wireless Fidelity WLAN Wide Area Network WRAN Wireless Regional Area Network WMAN Wireless Metropolitan Area Network WLAN Wireless Local Area Network WCDMA Wide Code Division Multiple Access WSS Wide Sense Stationary WLAN Wireless Local Area Network


Chapter One: Introduction

1.1 Motivation

With the advent of new high data rate wireless applications, demand of the spectrum is

rapidly increasing. Communications governmental and regulatory agencies impose

regulations on spectrum usage, such as control of allocations and priorities, as well as its

features. At this time, most of the prime spectrum has been assigned and it is difficult to find

spectrum for the new wireless applications. It can be made available for either expand

existing infrastructures or invent new services. Even though much of the spectrum has been

allocated and preliminary measurement is that the spectrum is unutilized by primary users.

There exist a lot of spectrums holes, which can be easily used by secondary users. FCC is

currently working on the concept of dynamic spectrum access, where secondary users can

borrow un-used portions of the spectrum from primary users. Cognitive Radio (CR) is

employing on proper spectrum utilization because of their rapid adaptability and flexibility.

Orthogonal Frequency Division Multiplexing (OFDM) is promising candidate for flexible

spectrum pooling in communication systems [31].

CR is an emerging technology, which intelligently detects a particular segment of the radio

spectrum currently in use and selects unused spectrum without interfering to licensed users.

One of the challenges of the OFDM is high peak-to-average power ratio (PAPR). A high

PAPR brings disadvantages like an increased complexity of the A/D and D/A converters and

reduced efficiency of radio frequency (RF) power amplifier [53]. OFDM signal consists of a

number of independent modulated subcarriers that leads to the problem of PAPR. If all

subcarriers come with same phase, the peak power is N times the average power of the signal

where N is the total number of symbols in an OFDM signal. Thus, it is not possible to send

this high peak amplitude signals to the transmitter without reducing peaks. Because power

amplifier used for the transmission has non-linear nature which causing inter-modulation and

out-of-band radiation. The high peak of OFDM signal can be reduced in several ways.

The focus of this dissertation is on OFDM based CR, which can handle the apparent spectrum

scarcity and enable high data rate communications. The proposed system exhibits high PAPR

reduction for non-contiguous bands spectrum of OFDM based CR.


1.2 Thesis out Lines

This thesis is organized as follows:

Chapter 2: Presents an introduction of OFDM and describe its principles, advantages and

disadvantages, the basic OFDM transceiver model, different techniques of PAPR reduction

and finally the comparison of PAPR reduction techniques in theoretical aspect.

Chapter 3: Presents the introduction of Cognitive Radio (CR), background, spectrum

sensing, licensed and unlicensed spectrum in CR. It also presents an introduction to software

defined radio (SDR), background, benefits, architectures, and the relationship between SDR

and CR.

Chapter 4: Presents the merits and challenge architectures of OFDM based CR and analysis

of its system block diagram.

Chapter 5: Presents in the different kinds of channel model; including AWGN, Rayleigh

fading channel.

Chapter 6: Presents the simulation results by using MATLAB simulator that is implemented

in different channels with BPSK, QPSK, and QAM16 modulation schemes and also BER

calculation.

Chapter 7: Presents the main objective of thesis with concluding remarks and proposes the

future work of this thesis for advance research.


Chapter Two: Orthogonal Frequency

Division Multiplexing (OFDM)

2.1 Introduction

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique

that divides the available spectrum into subcarriers, with each subcarrier containing a low rate

data stream. The subcarriers have proper spacing and pass-band filter shape to satisfy

orthogonality as shown in Figure 2.1. OFDM will play an important role in realizing

Cognitive Radio (CR) concept by providing a proven, scalable, adaptive technology for

wireless communications [10]. Inter-symbol interference (ISI) is reduced completely by using

a guard band in every OFDM symbol. In OFDM, using guard band is cyclically extended in

order to avoid inter-carrier interference (ICI). The advantage of OFDM system is robustness

to channel fading in wireless communication environment. Frequency selective fading is

reduced by increasing the number of subcarriers. By choosing the coherence bandwidth is

greater than the subcarrier spacing of the channel, each subcarrier is going to be affected by a

flat channel and thus no or simple channel equalizer is needed.

OFDM is used in many wireless applications today. Already it is used in different WLAN

standards (e.g. HIPERLAN-2, IEEE 802.11a), Wireless Metropolitan Area Networks

(WMAN), Digital Video Broadcasting (DVB), 3GPP-LTE, Asymmetric Digital Subscriber

Line (ADSL) and power line communications.

Despite of OFDM advantages, it has a major potential drawback in the form of high Peak-to-

Average Power Ratio (PAPR). The high PAPR has nonlinear nature in the transmitter and it

degrades the power efficiency of the system.


+∆f +∆f

f

Figure 2. 1-OFDM subcarriers in frequency domain

2.2 Advantages and Disadvantages of OFDM System

There are some advantages and disadvantages of OFDM are summarized below:

2.2.1 Advantages of OFDM

Some of the advantages of an OFDM system are as follows:-

OFDM is computationally efficient to employ the modulation and demodulation

techniques by using FFT.

The OFDM signal is robustness in multipath propagation environment and more

tolerant of delay spread.

1 Subcarrier

(f) ∆f=1/Ts

(f)

(f)

()

8 Subcarriers


OFDM is more resistant to frequency selective fading than single carrier transmission

systems.

OFDM system gives good protection against co-channel interference and impulsive

parasitic noise.

Pilot subcarriers are used in OFDM system to prevent frequency and phase shift

errors.

It is possible to use maximum likelihood detection with reasonable complexity.

OFDM is a good candidate for CR because of its flexibility and adaptability [10].

The orthogonality preservation procedures in OFDM are much simpler compared to

CDMA/TDMA technique in multipath conditions [8].

2.2.2 Disadvantages of OFDM

Some of the disadvantages of an OFDM system are as follows:-

The OFDM signal suffers high peak to average power ratios (PAPR) of transmitted

signal.

OFDM is very sensitive to carrier frequency offset.

It is difficult to synchronize when subcarriers are shared among different transmitters.

2.3 OFDM System Model

A Basic OFDM system is described in Figure 2.2. Here an input data symbols are supplied

into a channel encoder that data are mapped onto BPSK/QPSK/QAM constellation.

The data symbols are converted from serial to parallel and using Inverse Fast Fourier

Transform (IFFT) to achieve the time domain OFDM symbols. Time domain symbols can be

represented as:

(2.1)

where,

is the transmitted symbol on the subcarriers

N is the number of subcarriers


Time domain signal is cyclically extended to prevent Inter Symbol Interference (ISI) from the

former OFDM symbol using cyclic prefix (CP).

Figure 2. 2-A basic diagram of OFDM Transceiver

The Digital to Analog Converter (DAC) is performed to convert the baseband digital signal

into analog signal. This operation is executed in DAC block of diagram. Then, the analog

signal is proceeded to the Radio Frequency (RF) frontend. The RF frontend performs

operations after receiving the analog signal. The signal is up converted to RF frequencies

using mixer and amplified by using Power Amplifier (PAs) and then transmitted through

antennas. At the receiver side, the received signal is down converted to base band signal by

RF frontend.

The analog signal is digitized and re-sampled by the Analog to Digital Converter (ADC). The

ADC is used to digitize the analog signal and re-samples it. In the figure, frequency and time

synchronization block are not shown because of simplicity. Cyclic prefix is removed from the

signal in frequency domain. This step is done by the Fast Fourier Transform (FFT) block.

The received symbols in the frequency domain can be represented as:

Y (k) = H (k) Xm(k) +W (k) (2.2)

where, Y (k) is the received symbol on the subcarrier, H (k) is the frequency response of

the channel on the same subcarrier and W (k) is the additive noise added to , subcarrier

which is generally assumed to be Gaussian random variable with zero mean and variance of

. Thus, simple one tap frequency domain equalizers can be employed to get the transmitted

symbols. After FFT signals are de-interleaved and decoded to recover the original signal.

Input

Output

Transmitter

Receiver

IFFT S

/

P

RF frontend

DAC ADD CP Mod

P

/

S

ADC RF

frontend

Remove

CP

Demod

S

/

P FFT

P

/

S


2.4 Mathematical Definition of OFDM Signal

OFDM consists of multiple carriers. Each carrier can be presented as a complex waveform

like:

,

(2.3)

where,

is the amplitude of the signal

(t) is the phase of the signal

The complex signal can be described by

, (2.4)

This is a continuous signal. Each component of the signal over one symbol period can take

fixed values of the variables like:

,

,

where,

n is the number of OFDM block.

T is a time interval and the signal is sampled by 1/T then it can be represented by:

, (2.5)

Let ω0=0 then the signal becomes:

, (2.6)

The signal is compared with general Inverse Fourier Transform (IFT):

(2.7)

Here, is time frequency domain.


Both are equivalent if

∆f = = =

where,

τ is symbol duration period (2.8)

The OFDM signal can be defined by Fourier Transform. The Fast Fourier Transform (FFT)

can obtained frequency domain OFDM symbols and Inverse Fast Fourier Transform (IFFT)

can obtain time domain symbols. They can be written as:

Fast Fourier Transform

(2.9)

Inverse Fast Fourier Transform

(2.10)

where,

2.5 NC-OFDM System Model

In OFDM system, the achievement of large number of non-contiguous subcarriers by

collective usage for the high data rate transmission is referred as Non-Contiguous OFDM

(NC-OFDM) [31]. NC-OFDM can provide the necessary agile spectrum usage for the target

licensed spectrum if spectrum can be occupied by primary and secondary users. The spectrum

sensing measurements are deactivated during the subcarriers corresponding to the spectrum

occupied by primary user. Moreover, dynamic spectrum sensing can be determined when the

active subcarriers are located in the unoccupied spectrum bands.

Fundamentals of the NC-OFDM signal transmission and reception are quite similar to that of

the OFDM signal explained in Section 2.7.1. However, NC-OFDM techniques offer very

important advantage for growing scarcity of the large contiguous frequency spectrum, i.e. it

can support dynamic spectrum pooling for high data rate transmissions.


Figure 2. 3 NC-OFDM Transceiver [31]

2.6 Why PAPR reduction in OFDM system

The OFDM technique divides the total bandwidth into many narrow sub-channels and sends

data in parallel. It has various advantages, such as high spectral efficiency, immunity to

impulse interference and, frequency selective fading without having powerful channel

equalizer.

But one of the major drawbacks of the OFDM system is high PAPR. OFDM signal consists

of lot of independent modulated subcarriers, which are created the problem of PAPR. It is

impossible to send this high peak amplitude signals to the transmitter without reducing peaks.

So we have to reduce high peak amplitude of the signals before transmitting.

Input

Output

Null subcarrier

selection

Spectrum sensing measurement

Subcarrier ON/OFF information

Transmitter

Receiver


P

/

S IFFT

S

/

P

RF

frontend

DAC ADD CP Mod

ADC RF

frontend

Remove

CP

Demod

S

/

P FFT

P

/

S


2.7 Mathematical Definition of PAPR

The PAPR of the OFDM signal can be written as:

PAPR{ , τ} = (2.11)

where,

s(t) is the original signal

τ is the time interval

is the peak signal power

is the average signal power

2.8 PAPR Techniques

There have been many new approaches developed during the last few years. Several PAPR

reduction techniques have been proposed in the literature. These techniques are divided into

two groups. These are signal scrambling techniques and signal distortion techniques. The

signal scrambling techniques are:

Block coding

Selective Level Mapping (SLM)

Partial Transmit Sequences (PTS)

Signal scrambling techniques work with side information which minimized the effective

throughput since they commence redundancy. Signal distortion techniques introduce band

interference and system complexity also. Signal distortion techniques minimize high peak

dramatically by distorting signal before amplification.

The signal distortion techniques are:

Clipping

Peak windowing

Peak cancellation

Peak power suppression

Weighted multicarrier transmission


2.8.1 Signal Scrambling Techniques

2.8.1.1 Block Coding Techniques

Coding techniques can be applied for signal scrambling, M sequences, Golay complementary

sequences, Shapiro-Rudin sequences codes can be used to reduce the PAPR efficiently.

This Block coding technique has been proposed by Wilkinson and Jones in 1965 for the

minimization of the peak to mean envelope power ratio of multicarrier communication

system [1]. The key object in this paper is that PAPR can be minimized by block coding the

data. The block coding techniques have three stages for the development. The first stage

works with the collection of appropriate sets of code words for any number of carriers, any

M-ary phase modulation method, and any coding rate. The second stage works with the

collection of the sets of code words which enable proficient implementation of the

encoding/decoding. The third stage offers error deduction and correction potential.

There different methods for the collection of the sets of code words. The mainly insignificant

method, order to search the peak envelope power (PEP) for all possible code words for a

certain length of given number of carriers. This technique is simple and accurate for short

codes because it needs extreme computation. Natural algorithms are mainly sophisticated

searching techniques. It can be used for the collection of longer code words. A selection of

code words select from searches for encoding and decoding can be performed with a look up

table or using combinatorial logic exploiting the mathematical structure of the codes

minimization when the frame sixe is bigger.

Large PAPR reduction can be achieved if the long information sequence is separated into

different sub blocks, and all sub block encoded with System on a Programmable Chip

(SOPC). There are many likely spaces, where the odd parity checking bits can be put into

each frame to minimize PAPR. For further minimization of PAPR, redundant bit location

optimized sub-block coding (RBLO-SBC) optimizes these locations redundant Combination

optimized sub-block coding scheme (COSBC) optimizes the combination of the coded sub-

blocks, where two coding schemes instead of one is used to encode the same information

source.

2.8.1.2 Block Coding Scheme with Error Correction

This Block coding scheme with Error Correction has been proposed by Ahn and et.al in [12]

to introduce a new block coding proposal for minimization of peak to average power ratio

(PAPR) of an Orthogonal Frequency Division Multiplexing (OFDM) system. Block coding

has error correction capability. In block coding method, the OFDM symbol can be reduced by

selecting only those code words with lower PAPR. In this paper, the key object of the method

is proposed that properly designed block codes can not only minimize the PAPR, but also

give error correction capability. A k bit data block (e.g. 4-bit data) is encoded by a (n, k)

block code with a generator matrix ‘G’ in the transmitter of the system. Followed by the

phase rotator vector b to produce the encoded output x=a.G+b(mod 2).


To achieve the accurate generator matrix and phase rotator vector that make sure the

minimum PAPR for the OFDM system, check all the 2n codes and choose only 2k codes that

obtain the minimum PAPR. After that generator matrix ‘G’ and the phase rotator vector ‘b’

are produced; which are used mapping between these symbols combination and input data

vector ‘a’. The converse functions of the transmitter are executed in the receiver system. The

parity check matrix ‘H’ is achieved from the generator matrix ‘G’, with an exception that the

effect of the phase rotator vector b is removed before calculations of syndromes.

Contrasting the method in [1], which only presents error detection; this method can improve

the overall system performance and provides error correction capability.

2.8.1.3 Selected Mapping (SLM)

Selective Mapping (SLM) approaches have been proposed by Bauml in 1965 [13]. This

method is used for minimization of peak to average transmit power of multicarrier

transmission system with selected mapping. A complete set of candidate signal is generated

signifying the same information in selected mapping, and then concerning the most favorable

signal is selected as consider to PAPR and transmitted. In the SLM, the input data structure is

multiplied by random series and resultant series with the lowest PAPR is chosen for

transmission. To allow the receiver to recover the original data to the multiplying sequence

can be sent as ‘side information’.

One of the preliminary probabilistic methods is SLM method for reducing the PAPR

problem. The good side of selected mapping method is that it doesn’t eliminate the peaks,

and can handle any number of subcarriers. The drawback of this method is the overhead of

side information that requires to be transmitted to the receiver of the system in order to

recover information.

2.8.1.4 Partial Transmit Sequence (PTS)

Partial Transmit Sequence (PTS) technique has been proposed by Muller and Hubber in 1997

[14]. This proposed method is based on the phase shifting of sub-blocks of data and

multiplication of data structure by random vectors. This method is flexible and effective for

OFDM system. The main purpose behind this method is that the input data frame is divided

into non-overlapping sub blocks and each sub block is phase shifted by a constant factor to

reduce PAPR.

PTS is probabilistic method for reducing the PAPR problem. It can be said that PTS method

is a modified method of SLM. PTS method works better than SLM method. The main

advantage of this scheme is that there is no need to send any side information to the receiver

of the system, when differential modulation is applied in all sub blocks.

2.8.1.5 Interleaving Technique

Interleaving technique has been proposed by Jayalath and Tellambura [2], for reduction peak

to average power ratio of an OFDM transmission. A data randomization technique has

proposed for the minimization of the PAPR in this paper.


The notion that highly correlated data structures have large PAPR can be reduced, if long

correlation pattern is broken down. Also, this paper proposes an additive method to minimize

the complexity.

The basic idea in adaptive interleaving is to set up an initial terminating threshold. PAPR

value goes below the threshold rather than seeking each interleaved sequences. The minimal

threshold will compel the adaptive interleaving (AL) to look for all the interleaved sequences.

The main important of the scheme is that it is less complex than the PTS technique but

obtains comparable result. This method does not give the assurance result for PAPR

reduction. In this circumstance, higher order error correction method could be used in

addition to this method.

2.8.1.6 Tone Reservation (TR)

Tone Reservation (TR) method is proposed for PAPR reduction [15]. The main idea of this

method is to keep a small set of tones for PAPR reduction. This can be originated as a convex

problem and this problem can be solved accurately. The amount of PAPR reduction depends

on some factors such as number of reserved tones, location of the reserved tones, amount of

complexity and allowed power on reserved tones.

This method explains an additive scheme for minimizing PAPR in the multicarrier

communication system. It shows that reserving a small fraction of tones leads to large

minimization in PAPR ever using with simple algorithm at the transmitter of the system

without any additional complexity at the receiver end. Here, N is the small number of tones,

reserving tones for PAPR reduction may present a non–negligible fraction of the available

bandwidth and resulting in a reduction in data rate. The advantage of TR method is that it is

less complex, no side information and also no additional operation is required at the receiver

of the system. Tone reservation method is based on adding a data block and time domain

signal. A data block is dependent time domain signal to the original multicarrier signal to

minimize the high peak. This time domain signal can be calculated simply at the transmitter

of system and stripped off at the receiver.

2.8.1.7 Tone Injection (TI)

Tone Injection (TI) method has been recommended by Muller, S.H., and Huber, J.B. [14].

This technique is based on general additive method for PAR reduction. Using an additive

method achieves PAPR reduction of multicarrier signal without any data rate loss. Note that

Tone injection (TI) uses a set of equivalent constellation points for an original constellation

points to reduce PAPR. The main idea behind this method is to increase the constellation

size. Then, each point in the original basic constellation can be mapped into several

equivalent points in the extended constellation, since all information elements can be mapped

into several equivalent constellation points. These additional amounts of freedom can be

utilized for PAPR reduction. This method is called Tone Injection method because of

replacing the points in the basic constellation for the new points in the larger constellation

which corresponds to injecting a tone of the proper phase and frequency in the multi-carrier

symbol. The drawbacks of this method are; need to side information for decoding signal at

the receiver side, and cause extra IFFT operation which is more complex.


2.8.2 Signal Distortion Techniques

2.8.2.1 Peak Windowing

The peak windowing method has been suggested by Van Nee and Wild [17]. This method,

proposes that it is possible to remove large peaks at the cost of a slight amount of self

interference when large peaks arise infrequently. Peak windowing reduces PAPRs at the cost

of increasing the BER and out-of-band radiation. Clipping is a one kind of simple introduces

PAPR reduction technique which is self interference. The technique of peak windowing

offers better PAPR reduction with better spectral properties. (Peak Windowing technique

provides better PAPR reduction with better spectral properties than clipping).

In peak windowing method we multiply large signal peak with a specific window, for

example; Gaussian shaped window, cosine, Kaiser and Hamming window. In view of the fact

that the OFDM signal is multiplied with several of these windows, consequential spectrum is

a convolution of the original OFDM spectrum with the spectrum of the applied window.

Thus, the window should be as narrow band as possible, conversely the window should not

be too long in the time domain because various signal samples are affected, which results an

increase in bit error rate (BER). Windowing method, PAPRs can be obtained to 4dB which

from the number of independent subcarriers. The loss in signal-to-noise ratio (SNR) due to

the signal distortion is limited to about 0.3dB. A back off relative to maximum output power

of about 5.5dB is needed in spectra distortion at least 30dB below the in-band spectral

density.

2.8.2.2 Envelope Scaling

The Envelope Scaling technique has been proposed by Foomooljareon and Fernando in [18].

They proposed a new algorithm to reduce PAPR by scaling the input envelope for some

subcarriers before they are sent to IFFT. In this paper, they used 256 subcarriers with QPSK

modulation technique, so that envelopes of all the subcarriers are equal. The key idea of this

scheme is that the input envelope in some sub carrier is scaled to achieve the smallest amount

of PAPR at the output of the IFFT. Thus, the receiver of the system doesn’t need any side

information for decoding the receiver sequence.

This scheme is appropriate for QPSK modulation; the envelopes of all subcarriers are equal.

Results show that PAPR can be reduced significantly at around 4 dB. Finally the system of

single scaling factor and number of clusters equal to number of sub carriers is recommended.

2.8.2.3 Peak Reduction Carrier

Peak Reduction Carrier has been proposed by Tan and Wassell to use of the data bearing

peak reduction carriers (PRCs) to reduce the effective PAPR in the OFDM system [19].

This scheme includes the use of a higher order modulation scheme to represent a lower order

modulation symbol. This permits the amplitude and phase of the PRC to be positioned within

the constellation region symbolizing the data symbol to be transmitted.


For example, to use a PRC that employs a 16-PSK constellation to carry QPSK data symbol,

the 16-phases of the 16-PSK constellations are divided into four regions to represent the four

different values of the QPSK symbol.

This scheme is appropriate for PSK modulation; where the envelopes of all subcarriers are

equal. When the QAM modulation scheme will be implemented in the OFDM system, the

carrier envelope scaling will result in the serious BER degradation. To limit the bit error rate

(BER) degradation, amount of the side information would also be excessive when the number

of subcarriers is large.

2.8.2.4 Clipping and Filtering

High PAPR is one of the most common problems in OFDM. A high PAPR brings

disadvantages like increased complexity of the ADC and DAC and also reduced efficiency of

radio frequency (RF) power amplifier.

One of the simple and effective PAPR reduction techniques is clipping, which cancels the

signal components that exceed some unchanging amplitude called clip level. However,

clipping yields distortion power, which called clipping noise, and expands the transmitted

signal spectrum, which causes interfering [20]. Clipping is nonlinear process and causes in-

band noise distortion, which causes degradation in the performance of bit error rate (BER)

and out-of-band noise, which decreases the spectral efficiency [21].

Clipping and filtering technique is effective in removing components of the expanded

spectrum. Although filtering can decrease the spectrum growth, filtering after clipping can

reduce the out-of-band radiation, but may also cause some peak re-growth, which the peak

signal exceeds in the clip level [22]. The technique of iterative clipping and filtering reduces

the PAPR without spectrum expansion. However, the iterative signal takes long time and it

will increase the computational complexity of an OFDM transmitter [20].

But without performing interpolation before clipping causes it out-of-band. To avoid out-of-

band, signal should be clipped after interpolation. However, this causes significant peak re-

growth. So, it can use iterative clipping and frequency domain filtering to avoid peak re-

growth.

In the system used, serial to parallel converter converts serial input data having different

frequency component which are base band modulated symbols and apply interpolation to

these symbols by zero padding in the middle of input data. Then clipping operation is

performed to cut high peak amplitudes and frequency domain filtering is used to reduce the

out of band signal, but caused peak re-growth [22]. This consists of two FFT operations.

Forward FFT transforms the clipped signal back to discrete frequency domain. The in-band

discrete components are passed unchanged to inputs of second IFFT while out of band

components are null. The clipping and filtering process is performed iteratively until the

amplitude is set to the threshold value level to avoid the peak out-of band and peak re-

growth.


2.9 Overall Analysis of Different Techniques

There are several techniques has been proposed in literature. Thus, it is possible to reduce the

large PAPR by using the different techniques. Note that the PAPR reduction technique should

be chosen with awareness according to various system requirements.

Table 2. 1-Comparison of PAPR Reduction Techniques

Name of Schemes Name of parameters

Distortion

less

Power increases Data rate loss

Clipping and Filtering No No No

Coding Yes No Yes

Partial

Transmit Sequence(PTS)

Yes No

Yes

Selective Mapping (SLM) Yes No Yes

Interleaving Yes No Yes

Tone Reservation (TR) Yes Yes Yes

Tone Injection(TI) Yes Yes No

There are many issues to be considered before using the PAPR reduction techniques in a

digital communication system. These issues include PAPR reduction capacity, power

increase in transmit signal, BER increase at the receiver, loss in data rate, computational

complexity increase and so on. Simultaneously most of the techniques are not proficient to

obtain a large reduction in PAPR with low coding overhead, with low complexity, without

performance degradation and without transmitter and receiver symbol handshake.


2.10 Conclusion

OFDM is a promising technique for wireless communication systems although it has some

drawbacks which are given below:

High PAPR

Frequency offset

High PAPR is one of the major problems of OFDM system. There are several techniques to

reduce the PAPR in OFDM transmission system. All PAPR reduction techniques have some

advantages and disadvantages. These PAPR reduction techniques should be chosen carefully

for getting the desirable minimum PAPR. All PAPR reduction techniques are based on

particular situation of system. This section describes and summarizes several techniques of

PAPR and proposes repeated clipping and frequency domain filtering technique which is the

best solution for PAPR.


Chapter Three: Cognitive Radio

3.1 Introduction

Cognitive Radio (CR) is an intelligent wireless communication system that is self-aware of

its surrounding environment and identifies unused portion of radio spectrum on the basis of

observed spectrum usage and able to make decision itself and efficiently uses spectrum in a

dynamically adaptive way.

The demand of spectrum is increasing day by day. A CR is an emerging technology for the

efficient use of the spectrum. It can discover unused spectrum by spectrum sensing and can

adjust its transmission setting accordingly without causing interference to licensed users. The

inconsistency between allocation and use of spectrum leads to need for the development of

intelligent radios. The regulation and more flexible spectrum management techniques are

required to increase the efficient use of our natural spectrum resources.

The Federal Communication Commission (FCC) has started considering dynamic approaches

for spectrum sharing. The IEEE 802.22 standards have launched the process to use TV band

spectrum holes for enabling wide area Internet service. The IEEE 802.22 working group is

developing a standard for cognitive wireless regional area networks (WRAN) for use by

license-exempt devices on a non-interfering basis in spectrum that is allocated to the TV

Broadcast Service [24].

The CR has the ability to dynamically adjust its certain operating parameters (e.g. transmit

power, carrier frequency, and modulation strategy) in real-time, with two primary objects in

mind: to provide highly reliable communications and efficient utilization of the radio

spectrum.


3.2 The Features of CR

CRs enable a more efficient and flexible usage of spectrum. The Federal Communication

Commission FCC has been identified in [27] the following features of CRs as given below:

Frequency Agility: The radio is capable of modifying its operating frequency in

adapting to the environment.

Dynamic Frequency Selection (DFS): The Radio can sense signals from

neighboring transmitter to select a most desirable operation environment.

Adaptive Modulation: The transmission waveforms and characteristics can

automatic modulation adjustment to utilize for the usage of spectrum.

Transmit Power Control: It is adapted to full power limits when required on the one

side and to lower levels on the other hand to permit greater sharing of spectrum.

Location Awareness: The radio is capable to identify in its location. In the same

location, other devices operate using the same spectrum to make as effective use of

transmission parameters.

Negotiated Use: The CR can have algorithms for allowing the sharing of spectrum

usage. So, they must have some terms and conditions between a licensed and a third

party or an ad hoc in real time.


L

ow

m

ediu

m

H

igh

(matu

re)

(

Sta

te o

f th

e sh

elf)

(S

tate

of

the

art

)

3.3 The Evolution of Radio Technology

A CR is an extension of modern Software Defined Radio (SDR). The evolution of radio

technology is shown in Figure 3.1.

Software Software Software Aware Adaptive Cognitive

Capable Programmable Defined Radio Radio Radio

Radio Radio Radio

A SDR is a Communications device whose attributes and capabilities are developed and or

implemented in Software (Based on the FCC’s notice of Proposed Rule Making (NPRM)

8/12/2000).

Suo-SAS CSEL

MBMMR MBITR DMR

MIDDS Falcon PSC-5 ICNIA

Jaguar JIT NTDR

Leprechaun EPLRS PRC-117

LST-5 SINCGARS ASIP

VRC-99 Racal 25

ARC-220 ARC-210

ARC-164

PLRS

SINGARS WSC-3

Software-Capable Software Programmable Software Defined Cognitive Radio

Figure 3. 1-SDR intensity [28]

Increasing Technology/Software Maturity

Cognitive

Radio

JTRS


3.4 Software Defined Radio (SDR)

A Radio in which some or all of the physical layer functions are software defined is known as

Software Radio or Software Defined Radio (SDR). The term SDR was first proposed in 1991

by Joseph Mitola. SDR is considered as a wireless device that is fully controlled or

reconfigured by software [33].The first software radio technical paper was published at the

IEEE National Tele Systems Conference in the united state in 1992.The European

Commission sponsored the first International Workshop on Software Radio. The SDR

Forum, working in collaboration with IEEE group, has provided definition of SDR [54].

A radio is a one kind of electronic device that transmits or receives signal through the radio

frequency (RF).A radio frequency is a part of electromagnetic spectrum. Radio exits in many

items for instance; television, cell phones, vehicles, car door openers and computer.

Traditionally, Radio is hardware based radio device. It can only be possible to modify radio

through the physical intervention. On the other hand, A SDR permits multi-band, multi-mode

and multi-functional wireless devices that can perform in an efficient way and comparatively

inexpensive.

A set of hardware and software technologies where all of the radio’s operating functions are

implemented through modifiable software or firmware operating on programmable

processing technologies. These sort of devices include field general purpose processors

(GPP), programmable System on Chip (SoC), programmable gate arrays (FPGA), digital

signal processors (DSP), or other application specific programmable processors. These

technologies provide a degree of freedom that allows new wireless features and capabilities

and can be enhanced using software upgrades to existing radio systems without requiring new

hardware devices.

All transmit or receive frequencies parameters are fixed in hardware based radio where

hardware design can’t be changed.

Example: A 800 MHz cellular phone can’t work in 1900 MHz PCS band without adding

additional hardware devices.

Example: A dual mode phone can operate in both the GSM and PCS worlds over both the US

and the EU frequency bands.

3.4.1 Benefits of SDR

An SDR is an enabling technology in a wide range within wireless communication systems.

To replace as many analog components and digital VLSI devices of the transceiver as

possible with programmable devices is the main goal of SDR. Benefits of SDR are given

below [33]:

Flexibility and Adaptability.

Subscriber: easy international roaming, more flexible and improved services.

Mobile network operator: provided adds value services.


SDR

Handset and base station manufacturers: increase production flexibility and new scale

of economies.

Flexibility means able to change modulation and to switch the channel depending on its

surrounding environment. Adaptability means having capable of adaptation. SDR can adapt

itself according to the parameters information.

3.4.2 Relationship between SDR and CR

The most popular definition of CR and SDR that is aware of its environment, internal state

and location, and autonomously adjust its operation to achieved designed objectives. There is

one simple conceptual method that represents the relation between SDR and CR as shown in

Figure 3.2.

Cognitive Radio

Figure 3. 2 -The relationship of SDR and CR [10]

Cognitive engine is responsible for controlling the SDR based on parameters learned from

environment. Cognitive engine is always alert to the radios’ hardware resources and

capabilities.

Cognitive

Engine

Internal

and

External

Sensing

Upper Layer

Functionalities


Compared to hardware radio where radio can operate single or limited radio functionality.

The SDR creates software based digital signal processing with software tuneable Radio

Frequency (RF) components. Thus, SDR is capable of operating with different bandwidths

over wide range of frequencies and using many different modulations. As a consequence,

SDR can support multiple standards e.g. WCDMA, EDGE, GSM, CDMA2000, WiMAX,

Wi-Fi and multiple access technologies such as Code Division Multiple Access (CDMA),

Time Division Multiplexing (TDMA), Space Division Multiple Access (SDMA) and

Orthogonal Frequency Division Multiple Access (OFDMA). Sensing device needs to sense

the spectrum; Sensing is most important part of CR. Sensing devices can sense the spectrum

which can be implanted into SDR internally or externally. A SDR may have spectrum

analyzer which can provide spectrum information to cognitive engine [10].

3.4.3 Ideal SDR Architecture for CRs

The ideal architecture of SDR shown in Figure 3.3 consists of three main units which are

reconfigurable such as digital radio, software tunable radio along with fixed impedance

synthesizer, and software tunable antenna systems [36]. The reconfigurable digital radio

function is responsible to perform digital functionalities such as various waveforms

generation, antenna units and optimization algorithms for software tunable radio, and also

controls all units. Software tunable analog front-end system can’t perform digitally because

those are restricted such as RF filters, combiners/splitters, data converter, power amplifier

(PA) and Low Noise Amplifiers (LNA). Impedance synthesizer is a sub-system of this

scheme. Impedance synthesizer is a vital section to optimize the performance of software

tunable analog radio system. For example, the software tunable antenna system for a random

frequency plan is specified by cognitive engine. Depending on system requirements,

reconfigurable digital radio system monitors and controls the software tunable radio system

[10].

Figure 3. 3 -Ideal SDR architecture [10]

Antenna Control Signal

Feedback information

Radio Control Signal

Reconfigurable

Digital Radio

Software Tunable

Analog Radio

Impedance

Synthesizer


The key relationship between the main units of SDR is explained as follows. The cognitive

engine is crucial unit that transmits radio configuration parameters to reconfigurable digital

radio unit. As a result it can easily reconfigure the entire radio. The reconfigurable digital

radio unit can calculate some parameters such as location information of a specific user for

cognitive engine. The unit of reconfigurable digital radio can configure itself with antenna

systems and software tunable radio elements to optimize these unit performances. There is

feedback option which is utilized by reconfigurable digital radio unit from software tunable

radio (mainly impedance). The reconfigurable digital radio unit can adjust the parameters of

software tunable radio.

3.5 Spectrum Sensing

Spectrum Sensing is used to detect the under-utilized portion of spectrum. Spectrum sensing

is key object that makes possible to opportunistic spectrum access in CR networks. However,

spectrum sensing can give error result in the form of false alarm and misdetection.

The key object is to offer more spectrum opportunities to Secondary Users (SUs) without

causing any interference to the Primary Users (PUs). CR hardware should be able to detect

the vacant portion of the spectrum. The accurate sensing of the wireless spectrum is a key

challenge in realizing CR technology. The individual cognitive devices can sense the

environment and sent the sensing information to the base station. The base station has to

adapt a common set of channels that do not conflict with PU throughout its coverage area.

The necessity supports from the physical layer (PHY) of the CR architectures as well as

intelligent algorithms that are implemented in software. Various sensing techniques exist in

the literature are described in Section 3.5.1.1.

3.5.1 Different Techniques of Spectrum Sensing in CR

A CR be conscious about changes of the environment in which spectrum sensing plays

important role by identifying unused spectrum portions called spectrum ‘holes’ without

causing interference to the PU. Spectrum sensing plays important role in future wireless

communication systems to provide high performance services. The aims of CR are to enforce

the efficient use of resources as including spectrum, frequency, transmitted energy, and time.

Spectrum sensing has two important roles:

Firstly, sensing should provide guaranty of interference free communication for PU.

Secondly, it can indentify spectrum opportunistic for increasing capacity of cognitive

networks.

The detection should be very short time that can avoid weak signals in a noisy environment,

as result there will be very small probability of miss detection.


Usually, Spectrum sensing techniques can be classified into four groups:

Primary Transmitter detection

Cooperative detection

Primary receiver detection

Interference temperature management

3.5.1.1 Primary Transmitter Detection

Generally, Cognitive users do not have any real time interaction with primary transmitter and

receiver. They do not have prior information about PUs. So, the transmitter is to find out the

used and unused spectrum bands of CR users. CR users should have the capability to detect

the signal from the primary transmitter on the basis of local observation of CR users in shown

Figure 3.4. The basic hypothesis scheme of transmitter detection can be presents as follows:

, (3.1)

where, x(t) signal is received by the CR user. s(t) signal is the transmitted signal of primary

user, n(t) is a zero-mean Additive White Gaussian Noise (AWGN) and b is the amplitude

gain of the ideal channel. is a null hypothesis that represents no licensed user in a specific

spectrum. Conversely, is an alternative hypothesis that denotes there exits some signals

of PUs. These proposed systems are generally used for the transmitter detection: matched

filter, energy detection, and features detection.

Figure 3. 4 -Primary transmitter detection

No intersection

between CR user and

Primary Tx/Rx

Primary Transmitter

Primary

Transmitter

CR user must rely on locally sensed

signals from the Primary transmitter to

infer user activity to infer primary user

activity.


3.5.1.1.1 Matched Filter Detection

Matched filter is a filter that is obtained by correlating a known signal with an unknown

signal to detect the presence of AWGN. A known signal X(t)has an impulse response equal to

a conjugated time-reversed version of X(t).This filter can provide a maximum Signal to Noise

Ratio (SNR) output when a signal X(t) is AWGN. This matched filter is appropriate for

random signal when they have some periodically repeated elements. There are some

applications of matched filter to spectrum sensing in CR which includes the known elements

of ATSC, GSM, DTV, IS-54/136, 802.11a/g, OFDM, etc. The filter can be matched in GSM

system to 26-bit midamble (code) in the centre of each 156-bit traffic time slot. In EEE

802.11a/g standard, OFDM can be matched to the 127-bit repeated pilot subcarrier

synchronization sequence. The matched filter has some merits include simplicity, optimally

computational for AWGN. This filter has one serious demerit; it provides poor performance

in non-AWGN channels and sensitivity to deficient synchronization.

The matched filtering is an optimal way for detection of PU [36].A matched filter needs

demodulation of PUs signal that means a CR has a pre-knowledge of PU at both PHY and

MAC layers such as bandwidth, modulation, order, packet format, and pulse shape. This type

of information might be pre-stored in memory of CR. The most of PUs have preambles,

pilots, spreading codes, training sequences and synchronization words that can be used for

the coherent detection. The narrow band TV signal has pilots for audio and video carriers.

CDMA systems have devoted synchronization channels and spreading codes for pilots. In

OFDM system, it has preambles for packet acquisition.

The main advantage of matched filter is only samples to achieve detection error

probability constraints [37]. The main drawback of matched filter is that a CR needs special

receivers for every PU.

3.5.1.1.2 Energy Detection

Energy detector is a sub-optimal non coherent receiver. The energy detector is a sub-optimal

way for detecting the unknown signal. The energy detector is also known as periodogram or

radiometry. An energy detector can be implemented by averaging frequency bins of a Fast

Fourier Transform (FFT) like a spectrum analyzer as represented in Figure 3.5 [38].

In this block diagram, processing gain is proportional to FFT size N and averaging time T.

When N increases, it improves the frequency resolution that helps in narrow band signal

detection. The noise power level is reduced by longer averaging time (T) which leads to

improve the SNR. However, the energy detector requires only samples to

achieve a detection error probability constraint.


X(t)

Figure 3. 5 -Implementation of an energy detector using Welch Periodgram

averaging

There are several drawbacks of energy detector. The energy detector is not difficult to

implement but it can only identify the presence of the signal not to differentiate signal types

(modulated, signals, noise and interference). So, the energy detector provides often false

detection. Energy detector cannot identify the interference. So, it can’t be benefited from

adaptive signal processing (for cancelling the interference). A threshold that used for primary

users detection is highly susceptible to unknown or uncertain noise levels. Adaptive threshold

would be set for primary user detection but it can generate the false alarm or increase miss

detection probability. Finally, the energy detectors do not work properly for detecting spread

spectrum signal [40].

3.5.1.1.3 Cyclostationary Detection

The cylostationary detection is another method for detecting primary users signal by

exploiting the received signals in spectrum sensing.

Implementation of a spectrum correlation function for cyclostationary feature detection is

illustrated in Figure 3.6. Modulated signal are generally paired with sine wives carriers,

repeating spreading, pulse train, cyclic prefixes, hopping sequences which result in built-in

periodicity. These modulated signals are distinguished by cyclostationarity, since their auto-

correlation and mean show periodicity. The feature detection can utilize this inherent

periodicity in the PU’s signal by analyzing a spectral correlation function. The

cyclostationary detection can differentiate noise energy from primary users signal. This is a

consequence for the noise is Wide Sense Stationary (WSS) with no correlation while

modulated signal are cyclostationary with spectral correlation due to spectral redundancy

caused by periodicity [41].

A/D N pt. FFT Average

over T

Energy

Detect

Threshold


X(t)

Figure 3. 6 -Implementation of a cyclostationary feature detector

The Cyclic Spectral Density (CSD) function of received signal as given below:

(3.2)

where,

Cyclic Auto-Correlation Function (CAF) is and α is

the cyclic frequency.

The key advantages of cyclostationary detection or feature detection is more robust to noise

uncertainty than an energy detector. This cyclostationary is also able to distinguish different

type of signals and abide false alarm caused by external signal such as those from

interference or other CR users.

Transmitter detection as matched filter detection, energy detection, and cyclostationary

feature detection are described and comparison is shown these different detection techniques

in Table 3.1.

A/D N. pt

FFT Correlate

Feature

Detect

Average

over T


Table 3. 1- Comparison different techniques of spectrum sensing

Spectrum Sensing

Scheme

Advantages Disadvantages

Match Filter

Energy Detection

Cyclostationary Detection

Optimal detection

Works at low computational

cost

Sub-optimal Detection

No need to any prior

Information of the primary

User

It is robust to interference

and robust in low SNR.

Needs a prior knowledge of

the primary user

It can’t perform in low SNR

and can’t differentiate users

sharing the same channel.

Needs partial information of

the primary user and

computational cost is high

3.5.1.2 Cooperative Detection

A cognitive user (Transmitter) can have a good line of sight to CR receiver although it may

not be able to detect the primary transmitter because of shadowing. Moreover, transmitter

detection techniques can’t avoid causing interference to primary receivers due to lack of

information of primary receiver. As a result, sensing information from other users is needed

the primary transmitter. So, Cooperative sensing is required to handle in this situation. A few

problems arise in spectrum sensing due to fading, shadowing and noise uncertainty which can

be solved by cooperative sensing. Cooperative sensing can also solve the hidden primary user

problem and can reduce the sensing time as discussed in the literature [43].

3.5.1.3 Primary Receiver Detection

Generally the primary receiver emits the Local Oscillator (LO) leakage power from its RF

front end when it receives signals from the primary transmitter. A primary receiver method

utilizes this LO leakage power instead of the transmitted signal from the primary transmitter

in order to detect the presence of the primary receiver directly.


3.5.1.4 Interference Temperature Management

In 2003, the FCC has proposed the concept of ‘’Interference temperature’’ in order to

determine to RF interference. This model is designed to operate to the distance at which the

receive power approaches the level of noise floor. The noise floor is location specific

depending on the additional interfering signal. This model shows a recommended

interference temperature limit that is the amount of new interference which the primary

receiver can tolerate. The objective of spectrum sensing is best fitted by this proposed model

but the difficulties to properly determine the interference temperature. Naturally a CR is

aware of its transmitted power level and global positioning system and also knows it precise

location. A CR can compute the probability with the help of this information that its

transmission can cause significant interference to a neighboring receiver on the same

frequency.

Unfortunately, however, the interference temperature techniques may not give guarantee.

Actually there is no practical way for CR user to estimate or measure the interference

temperature because CR users have difficulties in differentiating the actual signals from the

PU and interference [44].


Chapter Four: OFDM Based Cognitive

Radio

4.1 Merits and Challenges for OFDM Based CR

The challenges of OFDM based cognitive radio (CR) can be grouped into three categories as

represented in Figure 4.1. The first category includes challenges are related to OFDM

systems including Peak-to-Average Power Ratio (PAPR), phase noise, synchronization and

sensitivity to frequency offset. The second category comprise of CRs, i.e. spectrum sensing,

cross layer adaptation and interference avoidance. Third category can be arising when OFDM

technique is employed by CR.

CR provides the tempting solution to spectral crowding problem by introducing the proper

opportunistic usage of frequency bands that are not heavily occupied by licensed user [45].

CR demands that the physical layer (PHY) should be adaptable and flexible. CR may

achieve this objective through the use of OFDM. OFDM has gained popularity and is used in

many current wireless communications system nowadays, e.g., Wireless Metropolitan Area

Network (WMAN), Wireless Local Area Network (WLAN), and Digital Video Broadcasting

(DVB) that is already proven as an adaptive, flexible and reliable transmission method. In

spite of this, identified challenges (adaptation, awareness, etc.) need to be researched further

to find firm solutions. It is predicted that OFDM will be an attractive PHY technology for CR

systems.

4.2 OFDM Based CR Architecture

The OFDM applications to CR bring new features and challenges to system design. An

OFDM based CR model is shown in Figure 4.2. The cognitive engine is the main unit of this

model. Basically, cognitive engine is an intellectual unit that is responsible for making the

intelligent decisions and configures the PHY parameters. The decision unit can identify the

spectral opportunities based on the information from policy engine through local and network

spectrum sensing data [10].

After that, policy engine gives information to the cognitive engine regarding to the present

policies to be measured depending on the system location. This makes sure that CR doesn’t

use unauthorized waveform or break any policies.


Figure 4. 1- Research challenges in CR and OFDM [10]

Alternatively, local spectrum sensing unit handles the spectrum information and recognize

licensed users accessing the spectrum, their signal specification such as power level and their

bandwidth. When the necessary information is available, the decision unit can make an

execution in a proper way for the system. The decision encompasses selecting the suitable

channel coding, modulation bandwidth and operating frequencies. At this present stage,

OFDM technology has advantages over other similar transmission technologies with its

adaptive and flexibility features. The cognitive engine system can correspond with different

radio access technologies in the environment by only changing the configuration parameters

of OFDM in Table 4.1. The radio circuit is divided into digital and analog parts. The digital

part consists of digital IF, ADC, and DAC. On the other hand the analog part consists of

software tunable analog radio. The digital and analog parts are reconfigurable by the

cognitive engine in order to increase the flexibility of the system. This includes controlling

the operating frequency, filters, mixer and bandwidth, and antenna parameters. The antenna

parameters (beam forming, number of antennas) can be configured in order to improve the

system performance.

OFDM challenges… Cognitive Radio challenges…

›ICI

›PAPR

›Synchronization

›Spectrum

sensing

CR - OFDM › Cross layer

specific adaptation

challenges › Interference

avoidance


Software Tunable Radio

Figure 4. 2- OFDM base CR system block diagram [10]

Table 4. 1- OFDM Based Wireless Standards

Standard

IEEE

802.11 (a/g)

IEEE

802.16 (d/e)

IEEE

802.22

DVB-T

FFT

Size

64

128, 256, 512,

1024, 2048

1024, 2048,

4096

2048, 8192

CP

Size

¼ 1/4, 1/8,

1/16, 1/32

Variable 1/4, 1/8

1/16, 1/32

Bit per symbol

1, 2, 4, 6 1, 2, 4, 6 2, 4, 6 2, 4, 6

Pilots

4 Variable 96, 192,

384

62, 245

Bandwidth

(MHz)

20 1.75 to 20 6, 7, 8 8

Multiple

Accessing

CSMA OFDMA

/TDMA

OFDMA

/TDMA

N/A

Local

Policies

Spectrum

Informat-

ion from

Network

Upper

Layers

PHY Layer

So

ftw

are

Tu

na

ble

an

alo

g R

ad

io

Coding S/P IFFT

Decoding Equalizer P/S FFT

Synch & channel

estimation

Digital

RF

DA

C

Digital

RF

Cognitive Engine

Policy Engine Decision Unit Local Spectrum

Sensing

AD

C

Radio


4.3 Proposed System

This work describes the OFDM based CR architecture’s block diagram. In order to reduce the

high peak power ratio by introducing repeated clipping and frequency domain filtering which

is demonstrated in Figure 4.3.

This thesis, we attempt to exploit previous channel information in Non-Contiguous OFDM

(NC-OFDM) based CRs under dynamic spectrum sharing environments. In the traditional

OFDM systems have base on the resource allocation problem such as the allocated

transmission spectrum is fixed. The spectrum is co-shared in the CR and the operating

bandwidth is not continuously fixed in frequency, time, and geographical domains. The

channel and power status will be tracked through this system and provided reliable feedback

from channel state information to the transmitter. In this thesis, we take NC-OFDM radio link

under a varying available bandwidth and maintain the SNRs of sub-channels under total

power constraints. We have adaptively chosen high PAPR reduction approach which are

clipping and filtering.

Figure 4. 3 -Proposed block diagram of NC-OFDM based CR

High PAPR brings disadvantages like an increased complexity of the analog to digital and

digital to analog converters and reduced efficiency of radio frequency (RF) power amplifier.

One of the uncomplicated and effective PAPR reduction techniques is clipping, which cancel

the signal components that exceed some unchanging amplitude called clip level. However,

clipping yields power distortion which called clipping noise.


Power and past channel status Channel and spectrum info. feedback

Input

Output

Null subcarried selection


Spectrum sensing measurement

ADC RF

frontend

Remove

CP

P

/

S

S

/

P

FFT Demod

Receiver

Transmitter

DAC ADD

CP Mod Clip

Filter

RF

frontend

P / S

S / P

IFFT


Clipping is a nonlinear process. It causes in-band noise distortion, which causes degradation

in the system performance and out-of-band noise, which decreases the spectral efficiency.

The clipping ratio is defined as the ratio of the clipping level to the root mean square value of

the unclipped signal which causes no distortion to the in-band power and rejects out-of-band

power of the signal. With the form of filtering, clip-and-filter operation causes some peak re-

growth. By using repeated clip-and-filter operations can be reduced the overall peak re-

growth. The clipping and filtering technique has discussed in details the Section 2.8.2.4.


Chapter Five: Channel Models

5.1 Introduction

The communications channel is the physical medium connecting the transmitter to receiver.

Wired telephone, wireless phones, optical fiber are examples of communication channels. An

accurate and appropriate modeling of the radio channel is crucial in order to predict the

performance of wireless radio systems. A narrow band model is based on predicted mean

signal level and some assumptions about the envelope fading statistics. The assumption is

necessary due to the limited bandwidth of the signal being sent through the channel. For this

reason, a narrow band channel is usually called flat fading channel. However, in wide band

channels, the signal fading caused by time dispersion varies as a function of frequency and

the channel model is usually called frequency selective fading.

Early Binary symmetric channel (BSC) model was described by Wozencraft and Jacobs in

1967. This channel was memory less channel. Bello presented a channel using a tapped delay

line representation that is based on knowledge of the correlation properties of the channel in

1963.

In the literature; Stein, Schwartz and Bennett used a deterministic characterization of the

channel and then introduced dynamics into the model to describe the time varying fading

channel in 1996 [47].

The modeling of channel has been continuing research to recent days. The modeling of the

multiple-input and multiple-output (MIMO) channel has attracted attention, because it offers

significant increases in data throughput without additional bandwidth or transmits power. In

this section we introduce the Additive White Gaussian Noise (AWGN) channel and Rayleigh

fading channel.

5.2 Additive White Gaussian Noise (AWGN) Channel

The simplest channel model in wireless commutations is the well known Additive White

Gaussian Noise (AWGN) model. The mathematical expression of the AWGN channel as

follows in Figure 7.1:

, (5.1)

where, X(t) is the transmitted signal and plus N(t) is the white Gaussian Noise.


Transmitted Signal Received Signal

X (t) Y (t) = X (t) + N (t)

Noise N (t)

Figure 5. 1- AWGN channel

AWGN channel is considered as an important reference or benchmark model for comparing

the performance evaluation of communication systems and modulation formats. However,

when the signal travels from transmitter to receive via multiple propagation paths then a

practical fading channel model must be used to model the propagation environment. There

are some factors affecting fading including multipath propagation, speed of surrounding

objects, speed of the mobile, the transmission symbol duration and the transmission

bandwidth of the signal.

5.3 Rayleigh fading channel

Multipath is the propagation phenomenon that results in radio signal reaching the receiver

antennas via multiple propagation paths. There are many causes of multipath fading including

atmospheric ducting, refraction, reflection from terrestrial objects such as buildings and

mountains, etc.

The most harmful fading channel is the Rayleigh fading model which was first introduced by

Bell Lab in 1970s. It is a statistical model of the communication channel to represent the

effects of a propagation environment on a radio signal. A statistical Rayleigh fading model is

the simplest fading channel model. A Rayleigh fading channel is viewed as a model when a

radio signal passes through communication channel that varies power of signal according to

Rayleigh distribution.

Note that when there is no dominant propagation along a Line-of-Sight (LOS) between

transmitter and receiver, Rayleigh fading is most applicable. Rician fading is more applicable

when there is a dominant LOS component.

Rayleigh fading model performs as reasonable channel model when there are many objects

(such as building and mountain) in the propagation environment which scatter the radio

signal before it arrives at the receiver.


5.3.1 Mathematical Expression

Clark model is applicable to mobile reception in general scattering environments. R. k Clark

modelled the mobile channel as a Rayleigh fading channel in [52]. R. K Clark considered

non-LOS between the transmitter and the receiver. The radio signal is reflected and scattered

due to obstacles such as buildings, mountains and trees. Clark has also considered the

Doppler effect because of motion of mobile unit.

The phase and angle of arrival of each element wave will be statistically independent. Clark

model expresses the carrier signals received at the mobile whose phases are supposed to be

Gaussian random variables and the phase angle is uniformly distributed on the interval 0 to

2π. Doppler shift is given by:

(5.2)

where,

is the carrier frequency

v is the mobile velocity

is the angle

λ is the carrier wave length

According to the Clark assumption, the E–filed can be represented as an in phase and

quadrature.

(5.3)

where,

Here, and both are Gaussian random processes. Those are uncorrelated zero-mean

Gaussian random variables and variance is equal which given by:

Then, the magnitude of E-field is specified by:

(5.4)

Rayleigh distribution is given by in [52]:


, (5.5)

where, is the variance of the Rayleigh distributed variables.


Chapter Six: Simulation and Results

6.1 Introduction

This chapter is divided into two sections. The first section calculates peak-to-average power

ratio (PAPR) reduction and bit error rate (BER) for the single channel. The second section

calculates BER for different channels.

In the system, the following specifications are used for the PAPR reduction of OFDM:

Parameters of the simulation: bandwidth is 10 MHz, FFT/IFFT block is 256, and

clipping ratio is 4.

Three types of modulation schemas BPSK, QPSK, QAM16 used in simulations.

There are two types of channel models AWGN, Rayleigh fading used in simulations.

The three different outputs of the simulation are: reduction of PAPR, BER vs. SNR

for single channel (theoretical, without clipping, and with clipping), and BER vs. SNR

for two different channels.

6.2 Mathematical Definitions

The theoretical BER for BPSK or QPSK using AWGN channel:

, (6.1)

where, is spectral noise density and is energy per bit.

The theoretical BER for BPSK or QPSK using Rayleigh fading channel:

, (6.2)

where, is spectral noise density and is energy per bit.

The peak-to-average power ratio of OFDM, PAPR{s(t), τ} is given in equation 2.9.

where, s(t) is the signal and τ is the time interval.


A clipping method in its basic form is based on simple time domain signal limitation. Clipped

signal can be expressed by following relationship:

(6.3)

where, A is clipping level and Φ(t) is the phase of original signal s(t).

6.3 PAPR Reduction

Simulation results of PAPR reduction using different modulation techniques are given below

(Figure 6.1-6.3):

The complementary cumulative distribution function (CCDF) of the PAPR for the

transmitted signal are plotted in Figure 6.1, 6.2, and 6.3, where the PAPR technique being

employed by the iterated clipping and filtering. It is evident from these results that the PAPR

can be improved by using iterated clipping and filtering.

0 2 4 6 8 10 1210

-3

10-2

10-1

100

PAPR [dB]

CC

DF

PAPR reduction using BPSK

Original

First Iteration

Second Iteration

Third Iteration

Fourth Iteration

Figure 6. 1- PAPR reduction using BPSK with clipping and filtering


In Figure 6.1, by increasing PAPR, we observed that CCDF is 10-3

at 11.5 dB; but during

next iteration, same 10-3

is achieved at less PAPR, i.e., 10.05 dB. At fourth iteration, PAPR is

6.8 dB.

0 2 4 6 8 10 1210

-3

10-2

10-1

100

PAPR [dB]

CC

DF

PAPR reduction using QPSK

Original

First Iteration

Second Iteration

Third Iteration

Fourth Iteration

Figure 6. 2- PAPR reduction using QPSK with clipping and filtering

In the Figure 6.2, by increasing PAPR, we observed that CCDF is 10-3



is achieved at less PAPR 9.95 dB. At fourth iteration, PAPR is 6.7

dB.


0 2 4 6 8 10 1210

-3

10-2

10-1

100

PAPR [dB]

CC

DF

PAPR reduction using QAM16

Original

First Iteration

Second Iteration

Third Iteration

Fourth Iteration

Figure 6. 3- PAPR reduction using QAM16 with clipping and filtering

In the Figure 6.3, by increasing PAPR, we observed that CCDF is 10-3



is achieved at less PAPR 9.7 dB. At fourth iteration, PAPR is 6.65

dB.

In the system, repeated clipping and filtering significantly reduce PAPR, where modulation

schemas are BPSK, QPSK, and QAM16. PAPR of QAM16 is low compared to BPSK and

QPSK which gives better result can be as seen in Figure 6.3. According to the fourth

iteration, PAPR are given respectively 6.8, 6.7, and 6.65 dB for BPSK, QPSK, and QAM16.

Finally, it can be concluded that QAM16 is given better result compared to the BPSK and

QPSK.


6.4 BER calculation for AWGN channel

Simulation results of BER vs. SNR using BPSK and QPSK modulation techniques through

AWGN channel are given below (Figure 6.4-6.5):

0 5 10 1510

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

BE

R

BER vs SNR using BPSK through AWGN channel

Theoretical

Without Clipping

With Clipping

Figure 6. 4- BER Vs SNR using BPSK through AWGN channel

Theoretical and without clipping curves are nearly same while with clipping gives worse

results in Figure 6.4. The results are given below:

BER= 10-3

at SNR ≈ 7 dB for theoretical and without clipping.

BER= 10-3

at SNR ≈ 11 dB with clipping.


0 5 10 1510

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

BE

R

BER vs SNR using QPSK through AWGN channel

Theoretical

Without Clipping

With Clipping

Figure 6. 5- BER Vs SNR using QPSK through AWGN channel



BER= 10-3


BER= 10-3

at SNR ≈ 11.5 dB with clipping.


6.5 BER calculation for Rayleigh fading channel

Simulation results of BER Vs SNR using BPSK and QPSK modulation techniques through

Rayleigh fading channel are given below (Figure 6.6-6.7):

0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

BE

R

BER vs SNR using BPSK through Rayleigh fading channel

Theoretical

Without Clipping

With Clipping

Figure 6. 6- BER Vs SNR using BPSK through Rayleigh fading channel



BER= 10-2


BER= 10-2

at SNR ≈ 17.5 dB with clipping.


0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

BE

R

BER vs SNR using QPSK through Rayleigh fading channel

Theoretical

Without Clipping

With Clipping

Figure 6. 7- BER Vs SNR using QPSK through Rayleigh fading channel



BER= 10-2


BER= 10-2

at SNR ≈ 20 dB with clipping.


6.6 BER calculation for Different channels

Simulation results of BER vs. SNR using BPSK and QPSK modulation techniques through

different channels are given below (Figure 6.8-6.9):

0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

BE

R

BER vs SNR using BPSK through different channels (with clipping)

AWGN channel

Rayleigh fading channel

Figure 6. 8- BER Vs SNR using BPSK through two different channels

Rayleigh fading channel gives worst result compared to AWGN channel in Figure 6.8.

The results are given below:

BER= 10-2

at SNR ≈ 8.5 dB for AWGN channel.

BER= 10-2

at SNR ≈ 17.5 dB for Rayleigh fading channel.


0 5 10 15 20 2510

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

BE

R

BER vs SNR using QPSK through different channels (with clipping)

AWGN channel

Rayleigh fading channel

Figure 6. 9- BER Vs SNR using QPSK through two different channels

Rayleigh fading channel gives worst result compared to AWGN channel in Figure 6.9.

The results are given below:

BER= 10-2

at SNR ≈ 9 dB for AWGN channel.

BER= 10-2

at SNR ≈ 20 dB for Rayleigh fading channel.


Chapter Seven: Conclusion

This thesis presented an overview of Orthogonal Frequency Division

Multiplexing (OFDM) and Cognitive Radio (CR). For flexibility and

adaptability, the OFDM is an attractive candidate for CR systems. This

dissertation proposes a novel non-contiguous OFDM (NC-OFDM) technique,

where the implementation achieves high data rates of non-contiguous

subcarriers while simultaneously avoiding any interference to the

transmissions. The main goal of this thesis work was to investigate PAPR

reduction techniques for non-contiguous bands of OFDM based CR system.

Simulation results of PAPR reduction has shown that the performance of

QAM16 is good compared to others modulation schemes such as BPSK, QPSK.

According to the BER results, we need higher value of SNR to achieve the same

BER as compared to lower constellation modulation schemes.


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Peak to Average Power Ratio (Papr) Reduction in Ofdm Based Radio Systems_3