merged file 1

8/10/2019 Merged File 1

1/128

www. ijraset.com Special Issue-1, October 20

SJ Impact Factor-3.995 ISSN: 2321-9653

International Journal for Research in Applied Science & Engineering

Technology(IJRASET)

Page 1

Implementation of HDLC Protocol Using Verilog

K. Durga Bhavani1, B. Venkanna

2, K. Gayathri

3

1,2,3Dept. of ECE

1,2RGUKT-Basar

3Intell Engg. College-Anatapur

AbstractA protocol is required to transmit data successfu ll y over any network and also to manage the flow at which

data is transmi tted. HDLC protocol i s the high-level data li nk control protocol established by I nternational Organi zation

for Standardization (I SO), which is widely used in digital commun ications. H igh-l evel Data L ink Control (H DLC) is th

most commonly used Layer2 protocol and is sui table for bit ori ented packet transmission mode. This paper discusses the

Veril og modeli ng of single-channel HDLC Layer 2 protocol and its implementation using Xil inx.

Keywords- H igh Level Data l ink Control (H DL C), F rame Check Sequence (FCS), and Cycli c Redundancy Chec

(CRC)

I. INTRODUCTION

HDLC protocol is the high-level data link control protocol

established by International Organization for standardization

(ISO), which is widely used in digital communication and

are the bases of many other data link control protocols [2].

HDLC protocols are commonly performed by ASIC

(Application Specific Integrated Circuit) devices, software

programming and etc.

The objective of this paper is to design and implement a

single channel controller for the HDLC protocol which is the

most basic and prevalent Data Link layer synchronous, bit-oriented protocol. The HDLC protocol (High Level Data

link Control) is also important in that it forms the basis for

many other Data Link Control protocols, which use the same

or similar formats, and the same mechanisms as employed in

HDLC.

HDLC has been so widely implemented because it

supports both half duplex and full duplex communication

lines, point to point(peer to peer) and multi-point

networks[1]. The protocols outlined in HDLC are designed

to permit synchronous, code-transparent data transmission.

Other benefits of HDLC are that the control information is

always in the same position, and specific bit patterns used for

control differ dramatically from those in representing data,

which reduces the chance of errors.

II. HDLC PROTOCOL

The HDLC Protocol Controller is a high-performance

module for the bit-oriented packet transmission mode. It issuitable for Frame Relay, X.25, ISDN B-Channel (64 Kbits/s

and D-Channel (16 Kbits/s) The Data Interface is 8-bit wide

synchronous and suitable for interfacing to transmit and

receive FIFOs. Information is packaged into an envelope

called a FRAME [4]. An HDLC frame is structured as

follows:

FLAG ADDRESS CONTROL INFORMATION FCS FLAG

8 bits 8 bits 8 /16 bits variable 8 8 bits

Table 1. HDLC Frame

A. Flag

Each Frame begins and ends with the Flag Sequence which

is a binary sequence 01111110. If a piece of data within the

frame to be transmitted contains a series of 5 or more 1s, thetransmitting station must insert a 0 to distinguish this set of

1s in the data from the flags at the beginning and end of the

frame. This technique of inserting bits is called bit-stuffing[3].

B. Address

Address field is of programmable size, a single octet or a

pair of octets. The field can contain the value programmedinto the transmit address register at the time the Frame is

started.

C. Control

HDLC uses the control field to determine how to control the


2/128

www. ijraset.com Special Issue-1,

October 2014



Technology(IJRASET)

Page 2

communications process. This field contains the commands,

responses and sequences numbers used to maintain the data

flow accountability of the link, defines the functions of the

frame and initiates the logic to control the movement of

traffic between sending and receiving stations.

D. Information or Data

This field is not always present in a HDLC frame. It is onlypresent when the Information Transfer Format is being used

in the control field. The information field contains the

actually data the sender is transmitting to the receiver.

E. FCS

The Frame Check Sequence field is 16 bits. The FCS is

transmitted least significant octet first which contains thecoefficient of the highest term in the generated check

polynomials. The FCS field is calculated over all bits of theaddresses, control, and data fields, not including any bits

inserted for the transparency. This also does not include theflag sequence or the FCS field itself. The end of the datafield is found by locating the closing flag sequence and

removing the Frame Check Sequence field (receiver section)[5].

III. HDLC MODULE DESIGN

In this design, HDLC procedures contain two modules, i.e.

encoding-and-sending module (Transmitter) and receiving-

and-decoding module (receiver). The function diagram isshown as below.

Fig.1. HDLC Block Design

Form this diagram we know that, transmitter module

includes transmit register unit, address unit, FCS generationunit, zero insertion unit, Flag generation unit, control and

status register unit and transmit frame timer and

synchronization logic unit. Receiver module includes receiveregister unit, address detect unit, FCS calculator unit, zero

detection unit, flag detection unit, receive control and staturegister unit and frame timer and synchronization logic unit.

A. Transmitter Module

The Transmit Data Interface provides a byte-wide

interface between the transmission host and the HDLC

Controller. The Transmit data is loaded into the controller on

the rising edge of Clock when the write strobe input is

asserted. The Start and End bytes of a transmitted HDLC

Frame are indicated by asserting the appropriate signals with

the same timing as the data byte.The HDLC Controller will, on receipt of the first byte of a

new packet, issue the appropriate Flag Sequence and

transmit the Frame data calculating the FCS. When the las

byte of the Frame is seen the FCS is transmitted along with a

closing Flag. Extra zeros are inserted into the bit stream to

avoid transmission of control flag sequence within the Frame

data.

The Transmit Data is available on TxD pin with

appropriate to be sampled by Clk. If TxEN is de-asserted

transmit is stalled, and TxD pin is disabled.

A transmit control register is provided which can enable

or disable the channel. In addition it is possible to force thetransmission of the HDLC Abort sequence. This will cause

the currently transmitted Frame to be discarded. The transmi

section can be configured to automatically restart after an

abort, with the next frame, or to remain stalled until the host

microprocessor clears the abort.

B. Receiver Module

The HDLC Controller Receiver accepts a bit stream on

port RxD. The data is latched on the rising edge of Clock

under the control of the Enable input RxEN. The Flag

Detection block searches the bit stream for the Flag

Sequence in order to determine the Frame boundary. Anystuffed zeros are detected and remove and the FCS is

calculated and checked. Frame data is placed on the Receive

Data Interface and made available to the host. In addition

Flag information is passed over indicating the Start and the

End byte of the HDLC Frame as well as showing any error

conditions which may have been detected during receipt of

the Frame.

In normal HDLC protocol mode, all Receiver Frames are

presented to the host on the output register. A status registe


3/128

www. ijraset.com Special Issue-1,

October 2014



Technology(IJRASET)

Page 3

is provided which can be used to monitor status of the

Receiver Channel, and indicate if the packet currently being

received includes any errors.

IV. R ESULTS

Fig.2. Simulation Waveform

Device Utilization Report: Clock Frequency: 78.2 MHz

Resource Used Avail Utilization

IOs 60 180 33.33%

Function Generators 205 1536 13.35%

CLB Slices 103 768 13.41%

Dffs or Latches 108 1536 7.03%

Table 2. Synthesis Report

V. CONCLUSION

We designed HDLC protocol sending and receiving RTL

level modules in Verilog and had them tested successfully,

which has the following advantages like easy to program andmodify, suitable for different standards of HDLC procedures,

match with other chips with different interfaces. So thisproposed method can be more useful for many applicationslike a Communication protocol link for RADAR data

processing.

REFERENCES

[1] Implementation of HDLC protocol Using FPGA,[IJESAT] International Journal of Engineering Science

& Advanced Technology, ISSN: 2250-3676, Volume-2

Issue-4, 11221131.[2] M.Sridevi, DrP.Sudhakar Reddy / International Journa

of Engineering Research and Applications (IJERA)

ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5September- October 2012, pp.2217-2219.

[3] ISO/IEC 13239, Information technology -Telecommunications and Information exchange betweensystems High-level data link control (HDLC)

procedures, International Organization for

Standardization, pp 10-17, July 2002.[4] A.Tannenbaum, Computer Networks, Prentice Hall of

India, 1993.[5] Mitel Semiconductor, MT8952B HDLC Protoco

Controller, Mitel Semiconductor Inc., pp 2-14, May

1997.
http://www.ijera.com/


4/128




Technology(IJRASET)

Page 4

p Iterative MMSE-PIC Detection Algorithm for

MIMO OFDM SystemsGorantla Rohini Devi1, K.V.S.N.Raju2, Buddaraju Revathi3

1Department of ECE, 2Head of ECE Department, 3Asst. Professor, Department of ECE,

SRKR Engineering College

Bhimavaram, AP, India

Abstract- Wir eless communi cation systems are requir ed to provide high data rates, which is essential for many services such

as video, high quali ty audio and mobile integrated services. When data transmission is aff ected by fading and in terf erence

eff ects the inf ormation will be altered. Multiple I nput Multi ple Output (MIMO) technique is used to reduce the multipath

fading. Orthogonal F requency Division Multiplexing (OFDM ) is one of the promising technologies to mitigate the ISI. The

combination of M IMO-OFDM systems off ers high spectrum effi ciency and diversity gain against multi path fading channels.

Di ff erent types of detectors such as ZF , MMSE and PI C, I terati ve PIC. These detectors improved the quality of received

signal in high in terf erence envir onment. Implementations of these detectors veri fi ed the improvement of the BER v/s SNR

perf ormance. I terati ve PIC technique give best perf ormance in noise envir onment compared to ZF, MMSE and PIC.

Keywords: Orthogonal Fr equency Division Mul tiplexing (OFDM), Multiple Input Mu ltiple Output (M IMO), Zero Forcing(ZF ), Mi nimum Mean Square Er ror (MMSE), Parallel I nterf erence Cancellation (PI C), Bit Er ror Rate (BER), Signal to

Noise Ratio (SNR), I nter Symbol Interference (I SI ), Binary Phase Shi ft Keying (BPSK).

I. INTRODUCTION

In wireless communication the signal from a transmitter

will be transmitted to a receiver along with a number of

different paths, collectively referred as multipath. These

paths may causes interference from one another and result in

the original data being altered. This is known as Multipath

fading. Furthermore wireless channel suffer from co-channel

interference (CCI) from other cells that share the same

frequency channel, leading to distortion of the desired signaland also low system performance. Therefore, wireless system

must be designed to mitigate fading and interference to

guarantee a reliable communication.

High data rate wireless systems with very small symbol

periods usually face unacceptable Inter Symbol Interference

(ISI) originated from multi-path propagation and their

inherent delay spread. Orthogonal Frequency Division

Multiplexing (OFDM) has emerged as one of the most

practical techniques for data communication over frequency-

selective fading channels into flat selective channels. OFDMis one of the promising technologies to mitigate the ISI. On

the other hand, to increase the spectral efficiency of wireless

link, Multiple-Input Multiple-Output (MIMO) systems [1]. It

is an antenna technology that is used both in transmitter and

receiver equipment for wireless radio communication. MIMO

exploit the space dimension to improve wireless system

capacity, range, and reliability. MIMO system can be

employed to transmit several data streams in parallel at the

same time and on the same frequency but different transmit

antennas.

MIMO systems arise in many modern communication

channels such as multiple user communication and multiple

antenna channels. It is well known that the use of multiple

transmit and receive antennas promises sub performance gains

when compared to single antenna system. The combination

MIMO-OFDM system is very natural and beneficial since

OFDM enables support of more antennas and large bandwidth

since it simplifies equalization in MIMO systems. In MIMO-OFDM system offers high spectral efficiency and good

diversity gain against multipath fading channels [2][3].

In MIMO system depends on the different detection

techniques used at the MIMO receiver. The better detector

that minimizes the bit error rate (BER) is the maximum

likelihood (ML) detector. But the ML detector is practically

difficult as it has computational complexity is exponential. On

the other hand, linear detectors, such as zero-forcing (ZF) and

minimum mean square error (MMSE) receivers, have low

decoding complexity, but detection performance decrease in

portion to the number of transmit antennas.

Therefore, there has been a study on a low complexity

nonlinear receiver, namely, parallel interference cancellation(PIC) receiver, which parallely decodes data streams through

nulling and cancelling. PIC algorithm [4] relies on a parallel

detection of the received block. At each step all symbols are

detected by subtracted from the received block. PIC detection

is used to reduce the complexity and prevents error

propagation. The PIC detection uses the reconstructed signal


5/128




Technology(IJRASET)

Page 5

to improve the detection performance by using iteration

process. Iterative MMSE-PIC detection algorithm [5][6] best

detection technique compared all nonlinear receivers. For

improving the performance of overall system, the output of

detector is regarded as input of the PIC detection to do again.

By exchanging information between the MIMO detection and

decoder, the performance of receiver may greatly beenhanced.

Where number of iteration increases to improve the bit error

rate (BER) performance.

PIC introduces parallely, which enables to reduce the

interference and therefore increases the reliability of the

decision process. The channel as a flat fading Rayleigh

multipath channel and the modulation as BPSK has been

taken. MIMO-OFDM technology has been investigated as the

infrastructure for next generation wireless networks.

II. SYSTEM MODEL

Consider a MIMO OFDM system with transmitting and

receiving antennas. When the MIMO technique of spatial

multiplexing is applied encoding can be done either jointly

over the multiple transmitter branches.

X1

X2

XL

Y1 Y2 YL

Fig1.Schematic of PIC detection for MIMO OFDM system

According to the block diagram in Figure1 consists of two

users, one user source while the other user as destination. The

two users interchange their information as source to different

instant of time. In MIMO channel model, L simultaneous

antennas having same data for transmission, while receiver

has P antennas.

The binary data are converted into digitally modulated signal

by using BPSK modulation technique and after that converted

from serial to parallel through convertor. The digitally

modulated symbols are applied to IFFT block. After the

transformation, the time domain OFDM signal at the output of

the IFFT. After that, Cyclic Prefix (CP) is added to mitigate

the ISI effect. This information is sent to parallel to serial

convertor and again, the information symbols are

simultaneously transmitted over the MIMO channel and laterAWGN noise added at receiver side.

At the receiver side, firstly serial to parallel conversion occurs

and cyclic prefix removed. The received signals samples are

sent to a fast Fourier transform (FFT) block to demultiplex the

multi-carrier signals and ZF / MMSE / PIC / Iterative-PIC

detectors is used for separating the user signals at each

element of the receiver antenna array. Finally demodulated

outputs and the resulting data combined to obtain the binary

output data.

MIMO Techniques:

Current MIMO system includes MISO and SIMO system thatuses MIMO technique to improve the performance of wireless

system can be divided into two kinds. One is spatial

multiplexing which provides a linear capacity gain in relation

to the number of transmitting antenna and the other is spatial

diversity schemes which can reduce the BER and improve the

reliability of wireless link.

A. Spatial Multiplexing

The transmission of multiple data stream over more than one

antenna is called spatial multiplexing. It yields linear (In the

minimum number of transmit and receive antenna) capacity

increases, compared to systems with a single antenna at one or

both sides of the wireless link, at no additional power or

bandwidth expenditure. The corresponding gain is available if

the propagation channel exhibits rich scattering and can be

realized by the simultaneous transmission of independent data

stream in the same frequency band. The receiver exploits

difference in the spatial signature induced by the MIMO

channel onto the multiplexed data stream to separate the

different signals, there by realizing a capacity gain.

B. Diversity Schemes

In which two or more number of signals sent over different

paths by using multiple antennas at the transmitting and

receiving side. The space is chosen, in such a way the

interference between the signals can be avoided. To improve

the link reliability we are using diversity schemes. Spatial

diversity improves the signal quality and achieves higher

signal to noise ratio at the receiver side. Diversity gain is

obtained by transmitting the data signal over multiple

independently fading dimensions in time, frequency, and

U

s

e

r

IFFTMIMO

Channel

P-element

Receiver

antenna

array

F

F

T

ZF/MMSE/

PIC/

Iterative

PIC

U

s

e

r

Modulation

Demodulation


6/128




Technology(IJRASET)

Page 6

space and by performing proper combing in the receiver.

Spatial diversity is particularly attractive when compared to

time or frequency diversity, as it does not incur expenditure in

transmission time or bandwidth. Diversity provides the

receiver with several (ideally independent) replicas of the

transmitted signal and is therefore a powerful means to

combat fading and interference and there by improve linkreliability.

Two kinds of spatial diversities are considered, Transmitter

diversity and Receiver diversity. There are two famous space

time coding schemes. Space time block code (STBC) and

Space time trellis code (STTC).

III. PROPOSED DETECTION ALGORITHM FOR

MIMO-OFDM SYSTEMS

The co-channel interference is one of the major limitations in

cellular telephone network. In the case of cellular network

such as 3G or beyond 3G (4G), the co-channel interference is

caused by the frequency reuse. Our main idea is to reject the

co- channel interference in MIMO-OFDM cellular systems.

To eliminate the inter symbol interference (ISI) different types

of highly interference channel equalization techniques are

used. MIMO-OFDM detection method consists of linear and

nonlinear detection methods. Linear equalizers are ZF [7] and

MMSE [8] and nonlinear equalizers are PIC and Iterative PIC.

1. Zero Forcing (ZF) equalizer:

Zero forcing Equalizer is a linear equalization algorithm used

in communication systems, it inverse the frequency response

of the channel. The output of the equalizer has an overall

response function equal to one of the symbol that is beingdetected and an overall zero response for the other symbols. If

possible, this results in the removal of the interference from

all other symbols in the absence of the noise.

Zero Forcing is a linear method that does not consider the

effects of noise. In fact, the noise may be enhanced in the

process of eliminating the interference.

Consider a 2x2 MIMO system. The received signal on the first

antenna is given by:

1

1 1,1 1 1,2 2 1 1,1 1,2 1

2

xh x h x n h h n

x

(1)

The received signal on the second antenna is given by:

1

2 2,1 1 2,2 2 2 2,1 2,2 2

2

xh x h x n h h n

x

(2)

Where,

y1 and y2 are the received symbol on the first and second

antenna, h1,1 is the channel from 1st

transmit antenna to 1st

receive antenna, h1,2 is the channel from 1st

transmit antenna to

2nd

receive antenna, h2,1 is the channel from 2nd

transmit

antenna to 1st


transmit antenna to 2nd

receive antenna, x1, and x2 are thetransmitted symbols and n1 and n2 are the noise on 1

stand 2

nd

receive antennas respectively.

The sampled baseband representation of signal is given by:

y= Hx+n

(3)

Where,

y = Received symbol matrix,

H = Channel matrix,

x = Transmitted symbol matrix,

n = Noise matrix.

For a system with NT transmit antennas and NR receiver

antennas, the MIMO channel at a given time instant may berepresented as NT x NR matrix:

,

1,1 1, 2 1,

2 ,1 2 ,2 2 ,

,1 ,2

T

T

R R R T

N

N

N N N N

H H H

H H HH

H H H

(4)

To solve for x, we find a matrix W which satisfies WH = I.

The Zero Forcing (ZF) detector for meeting this constraint is

given by,

W = (HHH)

-1H

H(5)

Where,

W= Equalization matrix

H= Channel matrix

This matrix is known as the pseudo inverse for a general m x

n matrix where

(6)

It is clear from the above equation that noise power may

increase because of the factor (HHH)

-1. Using the ZF

equalization approach, the receiver can obtain an estimate ofthe two transmitted symbols and x1 and x2 i.e.

2

1

x

x= (H

HH)

-1H

H

2

1

y

y

(7)

* *1,1 1, 21,1 2 ,1

* *2 ,1 2 ,21, 2 2 , 2

Hh hh h

H Hh hh h


7/128




Technology(IJRASET)

Page 7

2.Minimum Mean Square Error (MMSE) Equalizer:

A MMSE estimator is a method in which it minimizes the

mean square error (MSE), which is a universal measure of

estimator quality. The most important characteristic of MMSE

equalizer is that it does not usually eliminate ISI totally butinstead of minimizes the total power of the noise and ISI

components in the output. If the mean square error between

the transmitted symbols and the outputs of the detected

symbols, or equivalently, the received SNR is taken as the

performance criteria, the MMSE detector [9] is the optimal

detection that seeks to balance between cancelation of the

interference and reduction of noise enhancement.

The received signal on the first receive antenna is,

1

1 1,1 1 1,2 2 1 1,1 1,2 1

2

xy h x h x n h h n

x

(8)

The received signal on the second antenna is,

1

2 2,1 1 2,2 2 2 2,1 2,2 2

2

xy h x h x n h h n

x

(9)

Where,

y1, y2 are the received symbol on the 1st

and 2nd

antenna

respectively, h1,1 is the channel from 1st

transmit antenna to 1st

receive antenna, h1,2 is the channel from 1st

transmit antenna to

2nd


transmit

antenna to 1st


transmit antenna to 2nd

receive antenna, x1, x2 are the

transmitted symbols and n1, n2 is the noise on 1

st

, 2

nd

receiverantennas.

The above equation can be represented in matrix notation as

follows:

1,1 1,21 1 1

2,1 2,22 2 2

h h x n

h h x n

(10)

Equivalently, y = Hx+n

To solve for x, we know that we need to find a matrix W

which satisfies WH=I. The Minimum Mean Square Error

(MMSE) linear detector for meeting this constraint is givenby,

W=[HH

H+NoI]-1

HH

(11)

Using MMSE equalization, the receiver can obtain an estimate

of the two transmitted symbols x1, x2, i.e.

2

1

x

x= (H

HH+N0I)

-1H

H

2

1

y

y(12)

3.Parallel Interference Cancellation (PIC):

Here the users symbols are estimated in a parallel manner.

This detects all layers simultaneously by subtractinginterference from other layers regenerated by the estimation

from ZF or MMSE criteria.

PIC detection is used to reduce the complexity and prevents

error propagation. The parallel MMSE detector consists of

two or more stages. The first stage gives a rough estimation of

substreams and the second stage refines the estimation. The

output can also be further iterated to improve the performance.

The first stage will be implemented by using either ZF or

MMSE detection algorithm. The MMSE detector minimizes

the mean square error between the actually transmitted

symbols and the output of the linear detector is

W=[H

H

H+NoI]

-1

H

H

(13)

By using MMSE detector the output of the first stage is

d = Dec(W.y) (14)

Where, W is the parameter of Equalization matrix which is

assumed to be known and Dec(.) is the decision operation. In

each a vector symbol is nulled.

This can be written as

S=I.d (15)

Where, I is identity matrix and d is rough Estimated symbols

of MMSE.

The PIC detection algorithm can be expressed as

R=y-H.S (16)

Hence S is the estimated symbols of MMSE Equalizer. The

estimated symbol using the detection scheme of the

appropriate column of the channel matrix

Z= Dec(W.R) (17)

Where,

R is the output of PIC Equalizer

W is the parameter of MMSE Equalization matrix

Z is the estimated symbols of PIC Equalizer

4.Iterative PIC detection:


8/128




Technology(IJRASET)

Page 8

In which, the estimated signal by decoder is used to

reconstruct the transmitted code signal. The PIC detection

uses the reconstructed signal to improve the detection

performance by using iterative process.

PIC cancellation estimates and subtract out all the interference

for each user in parallel in order to reduce the time delay. At

iteration process the output of PIC detector is given it as input.Combing MMSE detection with the PIC cancellation directly

impacts on the global performance of the systems and also on

the associated complexity. The complexity directly linked

with the number of iterations for the detection.

The Iterative PIC detection scheme based on MIMO system

algorithm is given by:

For i = 1: nT

nT - 1

c = y - H (: , J). Z

j=1

E = Dec (W. c)(18)

Where,

E is the estimation of transmitted symbols of iterative PIC

detector,

W is the MMSE equalization matrix,

c is the output of iterative PIC detector,

nT is the number of transmitting antennas.

IV. SIMULATION RESULTS

In all simulation results shown by using four equalizers (ZF,

MMSE, PIC and Iterative PIC) in MIMO OFDM system.Rayleigh fading channel is taken and BPSK modulation

scheme was used. Channel estimation as well as

synchronization is assumed to be ideal. We analyze the BER

performance of data transmission in Matlab software.

Fig. 2. BER for BPSK modulation with ZF and MMSE

equalizers in 2x2 MIMO-OFDM system.

From the plot it is clear that 2x2 MIMO-OFDM system with

MMSE equalizer for case of pure equalization compared to ZF

equalizer. Modulation scheme employed here is BPSK.

Fig. 3. Performance comparison of PIC and Iterative PICequalizers in 2x2 MIMO-OFDM system.


Iterative PIC equalizer for case of pure equalization compared

to PIC equalizer. The code BER of proposed scheme is

produced after iteration. when iteration increases the BER is

significantly improved. From simulation results the proposed

scheme Iterative PIC is quite effective compared to PIC.

Modulation scheme employed here is BPSK.

Fig. 4. Performance comparison of ZF, PIC and Iterative PIC

equalizers in 2x2 MIMO-OFDM system.


9/128




Technology(IJRASET)

Page 9


Iterative PIC equalizers for case of pure equalization

compared to ZF, MMSE, and PIC equalizer. The code BER of

proposed scheme is produced after iteration. when iteration

increases the BER is significantly improved. The Zero

Forcing equalizer removes all ISI and is ideal only when the

channel is noiseless. From simulation results the proposedscheme Iterative PIC is quite effective compared to ZF and

PIC. Modulation scheme employed here is BPSK.

Fig .5. Performance comparison of ZF, MMSE, PIC and

Iterative PIC equalizers in 2x2 MIMO-OFDM system .


Iterative PIC equalizers for case of pure equalization

compared ZF, MMSE, and PIC equalizer. The code BER of

proposed scheme Iterative PIC is produced after iteration.

when iteration increases the BER is significantly improved.

From simulation results the proposed scheme is quite effective

in all simulation configurations. However, Iterative PIC

detection scheme is better in the diversity gain and when the

intefrence comes from the other layers is completely

cancelled. Modulation scheme employed here is BPSK.

V. CONCLUSION

The combination of MIMO-OFDM systems are used toimprove the spectrum efficiency of wireless link reliability in

wireless communication systems. Iterative PIC scheme for

MIMO OFDM systems transmission including the feasibility

of using the priori information of the transmit sequence of

MMSE compensation. Performance of Iterative PIC detection

technique is better compared to ZF, MMSE, PIC using BPSK

modulation scheme in high interference environment. The

simulation result shows that the performance of proposed

scheme is greatly improved compared to other detection

receivers for MIMO-OFDM systems.

VI. FUTURE SCOPE

Any type of modulation techniques such as QPSK or QAM

will integrate the channel encoding part.

REFERENCES

[1] I. E. Telatar, Capacity of multiple-antenna Gaussian

channels, Eur. Trans. Telecommun., vol. 10, no. 6, pp.

585595, Nov/Dec. 1999.

[2] G. J. Foschini and M. J. Gans, On limits of wireless

communications in a fading environment when using

multiple antennas, Wirel. Pers. Commun., vol. 6, no. 3,

pp. 311335, Mar. 1998.[3] A. Paulraj, R. Nabar, and D. Gore,Introduction to Space

Time Wireless Communications, 1st ed. Cambridge, U.K.:

Cambridge Univ. Press, 2003

[4] Junishi Liu, Zhendong Luo,Yuanan Liu, MMSEPIC

MUD for CDMA BASED MIMO OFDM System, IEEE

Transaction Communication., vol.1, oct.2005 .

[5] Hayashi,H.Sakai , Parallel Interference Canceller with

Adaptive MMSE Equalization for MIMO-OFDM

Transmission, France telecom R&D Tokyo.

[6] Z.Wang, Iterative Detection and Decoding with PIC

Algorithm for MIMO OFDM System

,Int.J.communication, Network and System Science,published august 2009.

[7] V.JaganNaveen, K.MuraliKrishna, K.RajaRajeswari

"Performance analysis of equalization techniques forMIMO systems in wireless communication" International

Journal of Smart Home, Vol.4, No.4, October, 2010

[8] Dhruv Malik, Deepak Batra "Comparison of various

detection algorithms in a MIMO wireless communication

receiver" International Journal of Electronics and

Compute Science Engineering, Vol.1, No 3, page

no1678-1685.

[9] J.P.Coon and M. A. Beach, An investigation od MIMO

single-carrier frequency-domain MMSE equalizer in

Proc London comm.Symposium, 2002,pp. 237-240.


10/128




Technology(IJRASET)

Page 10

Computational Performances of OFDM using

Different Pruned Radix FFT AlgorithmsAlekhya Chundru1, P.Krishna Kanth Varma2

M.Tech Student, Asst Professor Department Of Eelectronics and Communications,

SRKR Engineering College,

Andhra Pradesh, India

Abstract- The Fast Four ier Transform (FFT) and its inverse (I FFT) are very important algori thms in signal processi

oftware-defi ned radio, and the most promi sing modulation technique i.e. Orthogonal F requency Division Mul tiplex

OFDM). Fr om the standard structure of OFDM we can f ind that I FF T/FFT modules play the vital r ole for any OFDM ba

ransceiver. So when zero valued inputs/outputs outnumber nonzero inputs/outputs, then general I FFT/FFT al gorithm

OFDM is no longer eff icient in term of execution time. It is possible to reduce the execution time by pruning the FFT. I n

paper we have implemented a novel and eff icient input zero traced radix FFT prun ing (algori thm based on radix-2 DI F F

adix-4 DI F FFT, radix-8 DIF F FT ). An intui tive comparison of the computational complexity of orthogonal frequency divis

mul tiplexing (OFDM ) system has been made in terms of complex calculations requi red using di ff erent radix Fast Fouransform techniques with and without pruni ng. The dif ferent transform techniques are intr oduced such as various types of F

Four ier transform (FF T) as radix-2 FFT, radix-4 FF T, radix-8 FFT, mixed radix 4/2, mixed radix 8/2 and split r adix 2/4. W

ntui tive mathematical analysis, it has been shown that with the reduced complexity can be offered with prun ing, OFD

perf ormance can be greatly impr oved in terms of calculati ons needed.

ndex terms- OFDM (Orthogonal frequency division multiplexing), Fast Fourier Transform (FFT), Pruni ng Techniqu

MATLAB.

I. INTRODUCTION

Orthogonal Frequency Divisional Multiplexing (OFDM) is

modulation scheme that allows digital data to be efficiently

nd reliably transmitted over a radio channel, even in multi-path

nvironments [1]. In OFDM system, Discrete Fourier

Transforms (DFT)/Fast Fourier Trans- forms (FFT) are used

nstead of modulators. FFT is an efficient tool in the fields of

ignal processing and linear system analysis. DFT isn't

eneralized and utilized widely until FFT was proposed. But the

nherent contradiction between FFT's spectrum resolution and

omputational time consumption limits its application. To match

with the order or requirement of a system, the common method

s to extend the input data sequence x(n) by padding number of

eros at the end of it and which is responsible for a increased

alue of computational time. But calculation on undesired

requency is unnecessary. As the OFDM based cognitive radio

2] has the capability to nullify individual sub carriers to avoidnterference with the licensed user. So, that there could be a

arge number of zero valued inputs/outputs compare to non-zero

erms. So the conventional radix FFT algorithms are no longer

fficient in terms of complexity, execution time and hardware

rchitecture. Several researchers have proposed different ways

to make FFT faster by pruning the conventional radix F

algorithms.

In this paper we have proposed an input zero traced ra

DIF FFT pruning algorithm for different radix FFT algorith

suitable for OFDM based transceiver. The computatio

complexity of implementing radix-2, radix-4, radix-8, mi

radix and split radix Fast Fourier Transform with and with

pruning has been calculated in an OFDM system and compa

their performance. Result shows IZTFFTP of radix algorith

are more efficient than without pruning.

II. OFDM SYSTEM MODEL

OFDM is a kind of FDM (Frequency Divis

Multiplexing) technique in which we divide a data stream in

number of bit streams which are transmitted through s

channels [3].

The characteristics of these sub-channels are that they orthogonal to each other. As the data that are transmi

through a sub-channel at a particular time are only a portion

the data transmitted through a channel so bit rate in a s

channel can be kept much low. After splitting the data in

parallel data streams each stream is then mapped to a tone


11/128




Technology(IJRASET)

Page 11

nique frequency and combined together using the Inverse Fast

ourier Transform (IFFT) to yield the time domain waveform to

e transmitted [4]. After IFFT is done, the time domain signals

re then converted to serial data and cyclic extension is added to

he signal. Then the signal is transmitted. At the receiving side

we do the reverse process to get original data from the received

ne [4,5].In case of deep fade, several symbols in single carrier is

amaged seriously, but in parallel transmission each of N

ymbol is slightly affected. So even though the channel is

requency selective, the sub-channel is flat or slightly frequency

elective. This is why OFDM provide good protection against

ading [6].

In an OFDM system there are N numbers of sub-channels.

fN is high then it will be very complex to design a system with

N modulators and demodulators. Fortunately, it can be

mplemented alternatively using DFT/FFT to reduce the high

omplexity. A detailed system model for OFDM system is

hown in Figure 1 [5,6].

Figure1: OFDM System Model

III. FOURIER TRANSFORM ALGORITHM

Discrete Fourier Transform (DFT) computational

omplexity is so high that it will cause a long computational

ime and large power dissipation in implementation. Cooley and

Tukey provided a lot of ways to reduce the computatio

complexity. From that, many fast DFT algorithms have b

developing to reduce the large number of the computatio

complexity, and these fast DFT algorithms are named

Fourier transform (FFT) algorithms. Decomposing is

important role in the FFT algorithms. There are

decomposed types of the FFT algorithm. One is decimationtime (DIT), and the other is decimation-in-frequency (D

There is no difference in computational complexity betw

these two types of FFT algorithm. Different Radix D

algorithms we used are

A. Radix-2 DIF FFT Algorithm

Decomposing the output frequency sequence X[k] into

even numbered points and odd numbered points is the

component of the Radix-2 DIF FFT algorithm [6]. We

divideX[k] into 2r and 2r+1, then we can obtain the follow

equations

2= () (1)

2+ 1= () (2)

= 0,1,2, . . , 2 1

Because the decomposition of the Equation (1)

Equation (2) are the same, we only use Equation (1) to exp

as shown in Equation (3).

2= ()

+ () (3)

Finally, by the periodic property of twiddle factors, we

get the even frequency samples as

2

=

(+

+

/2

)

()

(4)

= 0,1,2, . . , 2 1Similarly, the odd frequency samples is


12/128




Technology(IJRASET)

Page 12

2+ 1= /+ 2

()

= 0,1,2, . . , 1 (5) From Equation4) and (5), we can find out the same components, x[n] and[n+N/2], so we can combine the two equations as one basic

utterfly unit shown in Figure 2. The solid line means that x[n]

dds x[n + N / 2] , and the meaning

f the dotted line is thatx[n] subtractsx[n +N / 2] .

Figure 2: The butterfly signal flow graph of radix-2 DIF FFT

We can use the same way to further decompose N-point

DFT into even smaller DFT block. So from the radix-2 dif FFT,

here is a reduction of number of multiplications, which is about

factor of 2, showing the significance of radix-2 algorithm for

fficient computation. So this algorithm can compute N-point

FT inN/2 cycles.

B.Radix-4 DIF FFT

In case N-data points expressed as power of 4M

, we can

mploy radix-4 algorithm [9] instead of radix-2 algorithm for

more efficient estimation. The FFT length is 4M, where M is theumber of stages. The radix-4 DIF fast Fourier transform (FFT)

xpresses the DFT equation as four summations then divides it

nto four equations, each of which computes every fourth output

ample. The following equations illustrate radix-4 decimation in

requency.

()= ()

(6)

=

()

+

()

+

()

+ ()

(7)

Equation (7) can thus be expressed as

()=

()+ ()(+ 4 ) + (1)

(+ 2 ) + ()(+ 3 4 )

(8)

So, Equation (8) can then be expressed as four N/ 4 point DF

The simplified butterfly signal flow graph of radix-4 DIF FFT

shown in Figure 3.

Figure 3: The simplified butterfly signal flow graph of radix

DIF FFT

This algorithm results in (3/8)N log compmultiplications and (3/2)N log

complex additions. So

number of multiplications is reduced by 25%, but the numbeaddition is increased by 50%.

C.Radix-8 DIF FFT

Comparing with the conventional radix-2 FFT algorit

and radix-4 FFT algorithm, the advantage of developing radi

FFT algorithm is to further decrease the complexities, especi

the number of complex multiplications in implementation.

can split Equation (2.1) and replace index k with eight pa

including 8r, 8r+1,8r+2, 8r+3, 8r+4, 8r+5, 8r+6, and 8r

Hence, we can rewrite Equation (6) and obtain the Equation (

(8+ )=


13/128




Technology(IJRASET)

Page 13

=

+ +2 8

+ + 4 8+ +6 8

+ + 8+ +3 8

+ +5 8 + +7 8

/

(9)

The butterfly graph can be simplified as shown in Figure 4

Figure 4: The simplified butterfly signal flow graph of radix-8

DIF FFT

D. Mixed radix DIF FFT There are

wo kinds of mixed-radix DIF FFT algorithms. The first kind

efers to a situation arising naturally when a radix-q algorithm,where q = 2

m> 2, is applied to an input series consisting ofN =

k q

sequally spaced points, where1 k < m. In this case, out

f necessity, k steps of radix-2 algorithm are applied either at the

eginning or at the end of the transform, while the rest of the

ransform is carried out bys steps of the radix-q algorithm.

For example if N = 22m+1

= 2 4m, the mixed-radix

lgorithm [7][8] combines one step of the radix-2 algorithm and

m steps of the radix-4 algorithm. The second kind of mixed-

adix algorithms in the literature refers to those specialized for a

ompositeN =N0 N1 N2 ...Nk. Different algorithms may

e used depending on whether the factors satisfy certain

estrictions. Only the 2 4m of the first kind of mixed-radix

lgorithm will be considered here.

The mixed-radix 4/2 butterfly unit is shown in Figure5.

Figure 5: The butterfly signal flow graph of mixed-radix-4/

DIF FFT

It uses both the radix-22

and the radix-2 algorithms can perfo

fast FFT computations and can process FFTs that are not po

of four. The mixed-radix 4/2, which calculates four butter

outputs based on X(0)~X(3). The proposed butterfly unit

three complex multipliers and eight complex adders.

E. Split-Radix FFT Algorithms

Split-radix FFT algorithm assumes two or more para

radix decompositions in every decomposition stage to fu

exploit advantage of different fixed-radix FFT algorithm. A

result, a split-radix FFT algorithm generally has fewer count

adder and multiplication than the fixed-radix FFT algorith

while retains applicability to all power-of-2 FFT length.

More computational complexity of the odd frequency te

than the even frequency terms, so we can further decompose

odd terms to reduce complexities. If we use radix-2 DIF F

algorithm for the even frequency terms and the radix-22

D

FFT algorithm for the odd parts, we can obtain the split-ra2/4 algorithm [10,11] as shown in the equation

in the Equation (10).

2= + + () (10)

= 0,1,2, . . , 2 1

(4+ 1)=

()+ ()(+ 4 )+(1)(+ 2 ) + ()(+ 3 4 )(11)

(4+ 3)=


14/128




Technology(IJRASET)

Page 14

()+ ()(+ 4 )(+ 2 ) + ()(+ 3 4 )

(12)

Thus the N-point DFT is decomposed into one N/2 -point DFT

without additional twiddle factors and twoN/4 -point DFTs with

widdle factors. TheN-point DFT is obtained by successive use

f these decompositions up to the last stage. Thus we obtain a

DIF split-radix-2/4 algorithm. The signal flow graph of basic

utterfly cell of split-radix-2/4 DIF FFT algorithm is shown in

igure 6

Figure 6: The butterfly signal flow graph of mixed-radix-2/4

DIF FFT

we have

(0

)=

()+

+

(2) = +

4 + + 3

4

(1)= ()+ ()(+ 4 )+(1)(+ 2 ) + ()(+ 3 4 )

(3)= ()+ ()(+ 4 )(+ 2 ) + ()(+ 3 4 )(13)

As a result, even and odd frequency samples of each basic

rocessing block are not produced in the same stage of the

omplete signal flow graph. This property causes irregularity of

signal flow graph, because the signal flow graph is an L-sh

topology.

IV PRUNING TECHNIQUES

To increase the efficiency of the FFT technique sev

pruning and different other techniques have been proposed

many researchers. In this paper, we have implemented a n

pruning technique i.e. IZTFFTP by simple modification

some changes and also includes some tricky mathemat

techniques to reduce the total execution time.

Zero tracing-as in wide band communication system a la

portion of frequency channel may be unoccupied by the licen

user, so no. of zero valued inputs are much greater than the n

zero valued inputs in a FFT/IFFT operation at the transceiv

Then this algorithm will give best response in terms of redu

execution time by reducing the no. of complex computat

required for twiddle factor calculation. IZTFFTP have a str

searching condition, which have an array for storing the inpu

output values after every iteration of butterfly calculation. Iinput searching result whenever it found zero at any inp

simply omit that calculation by considering useful condi

based on radix algorithm used.

A Input Zero Traced Radix-2 DIF FFT Pruning

In radix-2 since we couple two inputs to obtain two outp

we therefore have 4 combinations of those two inputs at radi

butterfly. Now there exist three conditions only based u

zeros at the input.

No zero at input: No pruning happens in this case, butte

calculations are same as conventional radix-2. Any one input zero: Output will be only the copied version

input available, butterfly calculations are reduced compared

conventional radix-2.

All zero input: Output is zero and is obtained fr

mathematical butterfly calculations is zero.

B. Input Zero Traced Radix-4 DIF FFT Pruning In radi

since we couple four inputs to obtain four outputs, we theref

have 16 combinations of those four inputs at radix-4 butter

Now therefore for radix-4 pruning there exist five conditi

only based upon zeros at the input.

No zero at the input: No pruning takes place, butte

calculations are same as radix-4

Any one input zero: Output will be only the copied version

remaining inputs available, butterfly calculations are redu

compared to radix-4.


15/128




Technology(IJRASET)

Page 15

Any two inputs are zeros: Output will be only the copied

version of that remaining two inputs available, butterfly

calculations are reduced compared to radix-4 pruning with

one zero at input.

Any three inputs are zeros: Output will be only the copied

version of that remaining single input available, butterfly

calculations are reduced compared to radix-4 pruning withtwo zero at input.

All zeros input: Output is zero and is obtained from

mathematical calculations is zero.

C. Input Zero Traced Radix-8 DIF FFT Pruning In radix-8

ince we couple eight inputs to obtain eight outputs, we

herefore have 256 combinations of those eight inputs at radix-8

utterfly. Now therefore for radix-8 pruning there exist seven

onditions only based upon zeros at the input. Similarly to

adix-4 pruning, output is the version of non zero input. The

more the number of zeros at input leads to less mathematical

alculations compared to radix-8.

D. Input Zero Traced Mixed radix DIF FFT Pruning If

consider mixed radix 4/2, it uses the combination of radi

pruning and radix-4 pruning. Similarly mixed radix 8/2 uses

combination of radix-2 pruning and radix8 pruning.

E. Input Zero Traced Split radix DIF FFT Pruning If

consider spilt radix 2/4, it uses the combination of radi

pruning and radix-4 pruning.

V RESULTS

In order to compare the computational complexities amo

the different radix DIF FFT algorithms on OFDM,

calculations based on the OFDM block sizes have b

performed which are given in Table 1 and with prun

comparison in Table 2.

The speed improvement factors from without to with prun

of different radix algorithms are seen in Table 3.

OFDM

Block

Size

Radix -2 Radix-4 Radix-8Mixed

Radix-4/2

Mixed

Radix-8/2

Split

Radix-2/4

cm cadd cm cadd cm cadd cm cadd cm cadd cm cadd

2 1 2 - - - - - - - - - -

4 4 8 3 8 - - - - - 0 8

8 12 24 - - 7 24 10 24 - - 4 24

16 32 64 24 64 - - 28 64 22 64 12 64

32 80 160 - - - - 64 160 60 160 36 160

64 192 384 144 384 112 384 160 384 152 384 92 384

Table 2: Comparison of complex additions(cadd) and complex multiplications(cm) of different radix algorithms without prunin

OFDM

Block

Size

Radix -2 Radix-4 Radix-8Mixed

Radix-4/2

Mixed

Radix-8/2

Split

Radix-2/4

cm cadd cm cadd cm cadd cm cadd cm cadd cm cadd

2 0 2 - - - - - - - - - -

4 0 8 3 8 - - - - 0 8

8 12 24 - - 7 24 8 24 - - 4 24

16 31 64 24 64 - - 26 64 22 64 12 64

32 76 160 - - - - 64 160 60 160 36 160

64 179 384 141 384 112 384 157 384 152 384 90 384

Table 2: Comparison of complex additions(cadd) and complex multiplications(cm) of different radix algorithms with prunin


16/128




Technology(IJRASET)

Page 16

FF

T

ize

Radix

- 2

Radix

-4

Radix

-8

Mixed

Radix

-4/2

Mixe

d

radix-

8/2

Split

radix

-2/4

8 1 - 1 1.25 - 1

16 1.03 1 - 1.07 1 132 1.05 - - 1 1 1

64 1.07 1.02 1 1.01 1 1

Table 3: Speed Improvement Factor without to with pruning

in terms of Multiplications

Output shows the significant reduction of computational

omplexity by reducing the total no. of complex operation

e. both the multiplications and additions compare to the

rdinary radix FFT operations. The complex multiplications

nd additions are compared for different radix and pruned

lgorithms. The

omparison of complex multiplications for different radixDIF FFT algorithms is shown in Figure 7 and for different

nput zero traced radix DIF FFT pruned algorithms are shown

n Figure 8.

Figure 7: Comparison of complex multiplications fordifferent radix DIF FFT

Figure 8: Comparison of complex multiplications for

different Radix DIF FFT pruned algorithms

VI CONCLUSION

The computational performance of an OFDM system

epends on FFT as in an OFDM system. FFT works as a

modulator. If the complexity decreases, then the speed

OFDM system increases. Results shows input zero tra

radix DIF FFT pruned algorithms are much efficient than

Radix DIF FFT algorithms as it takes very less time

compute where number of zero valued inputs/outputs

greater than the total number of non zero terms, w

maintaining a good trade-off between time and spcomplexity, and it is also independent to any input data set

REFERENCES

[1] B. E. E. P. Lawrey, Adaptive Techniques for Mu

User OFDM, Ph.D. Thesis, James Cook Univers

Townsville,2001, pp. 33-34.

[2] J. Mitola, III, "Cognitive Radio: An Integrated Ag

Architecture for Software Defined Radio," Thesis (Ph

Dept. of Teleinformatics, Royal Institute of Technol

(KTH), Stockholm Sweden, May 2000.

[3] S. Chen, Fast Fourier Transform, Lecture Note, Ra

Communications Networks and Systems, 2005.[4] OFDM for Mobile Data Communications, T

International Engineering Consortium WEB ProFor

Tutorial, 2006. http://www.iec.org.

[5] Andrea Goldsmith, Wireless Communicatio

Cambridge university press, 2005, ISB

978052170416.

[6] J.G. Proakis and D.G. Manolakis, Digital Signal Pr

essing: Principles, Algorithms and Edition, 2002,

448-475.

[7] E. Chu and A. George,Inside the FFT Black Box :Se

& Parallel Fast FourierTransform Algorithms. C

Press LLC, 2000.[8] B. G. Jo and M. H. Sunwoo, New Continuous-F

Mixed-Radix (CFMR) FFT Processor Using Novel

Place Strategy, Electron Letters, vol. 52, No. 5, M

2005.

[9] Charles Wu, Implementing the Radix-4 Decimatio

Frequency (DIF) Fast Fourier Transform (FF

Algorithm Using aTMS320C80 DSP, Digital Sig

Processing Solutions,January 1998.

[10] P. Duhamel and H. Hollmann, Split-radix F

Algorithm, Electron Letters, vol. 20, pp 14-16, J

1984.

[11] [4] H. V. Sorensen, M. T. Heideman and C. S. Bur

On Computing the Split-radixFFT, IEEE Tra

Acoust., Speech, Signal Processing, vol. ASSP-34,

152-156,Feb. 1986.
http://www.iec.org./


17/128




Technology(IJRASET)

Page 17

Chaos CDSK Communication SystemArathi. C

M.Tech Student, Department of ECE,

SRKR Engineering College,

Bhimavaram, India

Abstract:In recent years chaotic communication systems have emerged as an alternative solution to conventional sp

spectrum systems. The chaotic carrier used in this kind of modulation-demodulation schemes, have unique properties

make them suited for secure, and multi-user communications. The security of chaos communication system is superi

other digital communication system, because it has characteristics such as non-periodic, wide-band, non - predictab

easy implementation and sensitive initial condition. In this paper, a new approach for communication using chaotic sign

presented.

KeywordsChaos Communication System, CDSK

I. INTRODUCTION

Previous digital communication technology continually

used a linear system. However, as this technology reached basiclimit, people started to improve performance of nonlinearcommunication systems applying chaos communication systemsto nonlinear systems [1]. Chaos communication systems have

the characteristics such as non - periodic, wide-band, non-

predictability and easy implementation. Also, chaoscommunication system is decided by initial conditions ofequation, and it has sensitive characteristic according to initialcondition, because chaos signal is changed to different signal

when initial condition is changed [2]. Chaos signal is expressed

as randomly and non-linearly generated signal. If initialconditions of chaos signal is not exact, users of chaos system are

impossible to predict the value of chaos signal because of itssensitive dependence on initial conditions [1][3]. As these

characteristics, the security of chaos communication system is

superior to other digital communication system.Due to security and other advantages, chaos

communication systems are being studied continuously. Look atexisting research, in order to solve disadvantage that bit errorrate (BER) performance of this system is bad, chaos

communication system is evaluated the BER performanceaccording to chaos maps, and find a chaos map that has the best

BER performance [4]. In addition, chaos users evaluate the BER

performance according to chaos modulation system [5][6], andpropose a new chaos map that has the best BER performance.

In this paper, in AWGN and Rayleigh fading channel,BER performances of chaotic CDSK system is evaluated. At

existing study, we proposed a novel chaos map in order to

improve the BER performance [7], and we named a novel chaosmap "Boss map".

II. CHAOTIC SYSTEM

A chaotic dynamical system is an unpredicdeterministic and uncorrelated system that exhibits noisbehavior through its sensitive dependence on its conditions, which generates sequences similar to PN sequ

The chaotic dynamics have been successfully employ

various engineering applications such as automatic cosignals processing and watermarking. Since the sigenerated from chaotic dynamic systems are noise-like, sensitive to initial conditions and have spread and flat spe

in the frequency domain, it is advantageous to carry mes

with this kind of signal that is wide band and has communication security. Numerous engineering applicatio

secure communication with chaos have been developed [8]

III. CHAOTIC SIGNALS

A chaotic sequence is non-converging and non-pe

sequence that exhibits noise-like behavior through its sendependence on its initial condition [1]. A large numbuncorrelated, random-like, yet deterministic and reprodu

signals can be generated by changing initial value. Tsequences so generated by chaotic systems are called ch

sequences [8].

Chaotic sequences have been proven easy to genand store. Merely a chaotic map and an initial conditioneeded for their generation, which means that there is nofor storage of long sequences. Moreover, a large numb

different sequences can be generated by simply changin

initial condition. More importantly, chaotic sequences can bbasis for very secure communication. The secrecy o


18/128




Technology(IJRASET)

Page 18

transmission is important in many applications. The chaotic

sequences help achieve security from unwanted reception in

several ways. First of all, the chaotic sequences make thetransmitted signal look like noise; therefore, it does not attract

the attention of an unfriendly receiver. That is, an ear-dropperwould have a much larger set of possibilities to search through

in order to obtain the code sequences [4][8].Chaotic sequences are created using discrete, chaoticmaps. The sequences so generated even though are completely

deterministic and initial sensitive, have characteristics similar tothose of random noise. Surprisingly, the maps can generate large

numbers of these noise-like sequences having low cross-correlations. The noise-like feature of the chaotic spreadingcode is very desirable in a communication system. This feature

greatly enhances the LPI (low probability of intercept)performance of the system [4].

These chaotic maps are utilized to generate infinitesequences with different initial parameters to carry different userpaths, as meaning that the different user paths will spread

spectrum based on different initial condition [8].

IV. SYSTEM OVERVIEW

A. Correlation delay shift keying system

CDSK system has an adder in transmitter. Existing

modulation system than CDSK system consists switch intransmitter, and problem of power waste and eavesdropping

occurs by twice transmission. Technique that has been proposedfor overcoming these problems is CDSK system. And,transmitted signal does not repeat by replacing an adder with a

switch in the transmitter [9].

Sk

d +1, 1Figure 1: Transmitter of CDSK system

CDSK transmitter is composed of sum in which

delayed chaos signal multiplied with information bit is added togenerated chaos signal from chaos signal generator. Here,information bit that is spread as much as spreading factor is

multiplied by delay chaos signal.

s = x+ dx (1)

Above equation (1) indicates transmitted signal

transmitter.

r y d

r

Figure 2: Receiver of CDSK system

CDSK receiver is correlator based receiver, and

performed in order to recover the symbol. Received signa

delay received signal are multiplied, and this signal is as added as spreading factor. Afterward the signal pass througthreshold, and information signal recover through decoding

Information bits are possible to recover when

time and spreading factor have to use exact value that is us

transmitted signal.

B. Chaos maps

In this paper, types of chaos map used are Tentand Boss map. At existing study, Boss map means a novel that we proposed for BER performance improvement [8].

Figure 3: Trajectory of tent map

Figure (3) shows trajectory of Tent map. The x

and the y-axis of figure (3) mean xn and xn+1, and Tent ma

trajectory of triangular shape.

x = bx c Fx (2)

Equation (2) of tent map is expressed as aEquation (2) of Tent map uses existing output value as cu

L

Chaotic

signal

rr

L


19/128




Technology(IJRASET)

Page 19

input value, and it is indicated as figure when initial value is 0.1

and parameter alpha is 1.9999.

Figure 4: Trajectory of boss map

Figure (4) shows trajectory of Boss map, a novel map

that is proposed in order to improve the BER performance. Thex-axis and the y-axis of Boss map mean xn and yn unlike the

Tent map, it draws trajectory like pyramid shape.

= 0.450.503 = 0.3 (3)

Equation (3) of Boss map is expressed as above.

Equation (3) form of Boss map is similar to Tent map because

Boss map was proposed by transforming from Tent map. And,trajectory of Boss map is indicated as figure (4) when initial

value is 0.1 and parameter alpha is 2.5.

V. PERFORMANCE EVALUATION

In this paper, the BER performance of chaotic CDSKsystem in AWGN (adaptive white Gaussian noise) channel andRayleigh fading channel is evaluated for Tent map and Boss

map.In AWGN channel, figure (5) shows BER performance

of chaotic CDSK system is evaluated. Looking at the figure (5),

the BER performance of chaotic CDSK system with tent mapand boss map is observed. Here, we observe that the BER

performance of Boss map is better than Tent map at each stagei.e. at different values of SNR we observe that the Boss map

shows better performance than Tent map. We also observe that

at initial values the BER is same for both maps but as

increases the BER for Boss map is less than Tent map.

Figure 5: BER analysis in AWGN channel

In Rayleigh fading channel, figure (6) shows the

performance of chaotic CDSK system. Here, the performaevaluated for both Tent map and Boss map. We observe t

initial values of SNR the BER performance is the same formaps. But as SNR value increases the BER performance of

map is better than Tent map.

Figure 6: BER performance in Rayleigh fading channel

VI. CONCLUSION

In this paper, a new type of communication susing chaos is proposed. Chaos sequences are non pe

sequences which are sensitive to their initial conditions. Csequences are generated using chaos map. CDSK system


20/128




Technology(IJRASET)

Page 20

chaos has many advantages over other systems. But the BER

performance of chaos communication system is bad. In order to

improve this, we proposed a new chaos map that has better BERperformance than existing map. In AWGN and Rayleigh fading

channel the chaotic CDSK system is evaluated and we observedthat the BER performance of chaos system with Boss map has

better than with Tent map which improves BER of CDSKcommunication system.

VII. FUTURE SCOPE

Chaos communication system increases the number oftransmitted symbols by spreading and transmitting informationbits according to characteristic of chaos maps. So the research

that improves data transmission speed is necessary for chaoscommunication system. If many antennas are applied to chaos

communication system, the capacity of data is proportional tothe number of antenna. So it is good way applying multiple-input and multiple-output (MIMO) to the chaos communication

system.

REFERENCES

[1] M. Sushchik, L.S. Tsimring and A.R. Volkovskii,

"Performance analysis of correlation- based communication

schemes utilizing chaos," Circuits and Systems I:Fundamental Theory and Applications, IEEE Transactions

on, vol. 47, no. 12, pp. 1684-1691, Dec. 2000.[2] Q. Ding and J. N. Wang, "Design of frequency-modulated

correlation delay shift keying chaotic communication

system," Communications, IET, vol. 5, no. 7, pp. 901-905,

May 2011.[3] Chen Yi Ping, Shi Ying and Zhang Dianlun, "Performance

of differential chaos-shift-keying digital communication

systems over several common channels,"Future Computerand Communication (ICFCC), 2010 2

ndInternational

Conference on, vol. 2, pp. 755- 759, May 2010.

[4] Suwa Kim, Junyeong Bok and Heung-Gyoon Ryu,"Performance evaluation of DCSK system with chaotic

maps," Information Networking (ICOIN), 2013International Conference on, pp. 556-559, Jan. 2013.

[5] S. Arai and Y. Nishio, Noncoherent correlation-based

communication systems choosing different chaotic maps,

Proc. IEEE Int. Symp. On Circuits and Systems, NewOrleans, USA, pp. 1433-1436, June 2007.

[6] Jun-Hyun Lee and Heung-Gyoon Ryu, "New Chaos Mapfor CDSK Based Chaotic Communication System," The

28th International Technical Conference on Circuit/System,

Computers and Communication (ITC-CSCC 2013), Y

Korea, pp. 775-778, July2013.

[7] M.A. Ben Farah, A. Kachouri and M. Samet, "Desisecure digital communication systems using DCSK ch

modulation," Design and Test of Integrated SystemNano-scale Technology, 2006. DTIS 2006.Interna

Conference on, pp. 200-204, Sept. 2006.[8] Ned J. Corron, and Daniel W. Hahs A new approacommunication using chaotic signals, IEEE transac

on circuits and systemsI: fundamental theoryapplications, VOL. 44, NO. 5, MAY 1997.

[9] Wai M. Tam, Francis C. M. Lau, and Chi K. Generalized Correlation-Delay-Shift-Keying SchemNon - coherent Chaos-Based Communication Sys

IEEE transactions on circuits and systemsI: repapers, VOL. 53, NO. 3, MARCH 2006.


21/128




Technology(IJRASET)

Page 21

Design and Analysis of Water Hammer Effect

in a Network of PipelinesV. Sai Pavan Rajesh

Department of Control Systems, St. Marys Group of Institutions,Jawaharlal Nehru Technological University- Hyderabad,Main Road, Kukatpally Housing Board Colony, Kukatpally, Hyderbad, Telangana, India.

Abstract-There wil l be a chance for the destruction of the system due to transient if it i s not provided with adequate

protection devices. Generally, transient takes place when parameters involving in conventional flow are distorted with

respect to the time. Rapid closing of valve in a pipe network wil l be resul ting i nto hydrau li c transient known as water

hammer occurs due to sudden change in pressure and veloci ty of f low wi th respect to time. Due to impulsive action, pressur e

surges are induced in the system tr avel along the pipe network with the rapid f lu id acceleration leading to the dramatic

effects li ke pipe li ne fail ure, damage to the system etc. Consideri ng the impor tance of hydrau li c transient anal ysis, we design

a system capable of verif ying pipe network contain ing flui d flow.Thi s paper demonstrates design of dif ferent pipe structures

in pi pe li ne network and analysis of various parameters li ke excess pressure distr ibu tion , veloci ty variati ons and water

hammer amplitude with respect to time using COMSOL M ulti physics v 4.3. The magnitude of water transient in pipe line

network at dif ferent pressure points has been discussed in detail.

Keywords- COMSOL, Pressure distributi on, Velocity variati on, Water H ammer.

I. INTRODUCTION

The key to the conservation of water is good water

measurement practices. As fluid will be running in water

distribution system, system flow control is dependent based

on the requirement for opening or closing of valves, and

starting and stopping of pumps. When these operations are

performed very quickly, they convert the kinetic energy

carried by the fluid into strain energy in pipe walls, causing

hydraulic transient[1]

phenomena to come into existence in the

water distribution system i.e., a pulse wave of abnormal

pressure is generated which travels through the pipe network.

Pressure surges that are formed or fluid transients in pipelines

are referred to as Water hammer. This oscillatory form of

unsteady flow generated by sudden changes results in system

damage or failure if the transients are not minimized. So now

the steady state flow conditions are altered by this effect[2]

resulting in the disturbance to the initial flow conditions of the

system. Where the system will tend to obtain a static flow rate

by introduction of new steady state condition. The intensity of

water hammer effects will depend upon the rate of change in

the velocity or momentum. Conventional water hammer

analyses provide information under operational conditions on

two unknown parameters i.e., pressure and velocity within a

pipe system. Generally effects such as unsteady friction,

acoustic radiation to the surroundings or fluid structure

interaction are not taken into account in the standard theory of

water hammer, but were considered in general approach[3]

.

But mechanisms acting all along the entire pipe section such

as axial stresses in the pipe and at specific points in the pipe

system such as unrestrained valves will fall under fluid

structure interaction extension theory for conventional water

hammer method.

Figure 1. Pipe connected to control valve at the end with water

inlet from reservoir.


22/128




Technology(IJRASET)

Page 22

In the past three decades, since a large number of water

hammer events occurred in the light-water- reactor power

plants[4]

, a number of comprehensive studies on the

phenomena associated with water hammer events have been

performed. Generally water hammer can occur in any thermal-

hydraulic systems and it is extremely dangerous for the

thermal-hydraulic system since, if the pressure induced

exceeds the pressure range of a pipe given by the

manufacturer, it can lead to the failure of the pipeline

integrity. Water hammers occurring at power plants are due to

rapid valve operation[5]

, void induced operation, and

condensation induced water hammer[6]

. In existing Nuclear

Power Plants water hammers can occur in case of an inflow of

sub-cooled water into pipes or other parts of the equipment,

which are filled with steam or steam-water mixture[7]

.

The water hammer theory has been proposed to account for a

number of effects in biofluids under mechanical stress, as in

the case of the origin of Korotkoff sounds during blood

pressure measurement[8, 9]

, or the development of a fluid-

filled cavity within the spinal cord[10]

. In the voice production

system, the human vocal folds act as a valve[11

which induces

pressure waves at a specific point in the airways (the glottis),

through successive compressing and decompressing actions

(the glottis opens and closes repeatedly). Ishizaka was

probably the first to advocate in 1976 the application of the

water hammer theory, when discussing the input acoustic

impedance looking into the trachea[12]

. More recently, the

water hammer theory was invoked in the context of tracheal

wall motion detection[13]

. Generally Water utilities, Industrial

Pipeline Systems, Hydropower plants, chemical industries,

Food, pharmaceutical industries face this water transient

problem.

The present work reports the design of different pipe

channels and analysis of the pressure distribution and velocity

variation produced all along the pipe flow network when

subjected to one pressure measuring point. Various parameters

like inlet input pressure, wall thickness and measurement

point are changed for analysis.

II. USE OF COMSOL MULTIPHYSICS

The software package selected to model and simulate

the pipe flow module was COMSOL Multiphysics Version

4.3. It is a powerful interactive environment for modelling and

Multiphysics were selected because there was previous

experience and expertise regarding its use as well as

confidence in its capabilities. A finite element method based

commercial software package, COMSOL Multiphysics, is

used to produce a model and study the flow of liquid in

different channels. This software provides the flexibility for

selecting the required module using the model library, whichconsists of COMSOL Multiphysics, MEMS module, micro

fluidics module etc. Using tools like parameterized geometry,

interactive meshing, and custom solver sequences, you can

quickly adapt to the ebbs and flows of your requirements,

particle tracing module along with the live links for the

MATLAB. At present this software can solve almost

problems in multi physics systems and it creates the real world

of multi physics systems without varying there material

properties. The operation of this software is easier to

understand and easier to implement in various aspects for

designers, in the form of finite element analysis system.

Figure 2. Multiphysics modelling and simulation software-

COMSOL


23/128




Technology(IJRASET)

Page 23

In this model as the valve is assumed to close instantaneously

the generated water hammer pulse has a step function like

shape. To correctly solve this problem requires a well posed

numerics. The length of the pipe is meshed with N elements

giving a mesh size dx = L/N. For the transient solver to be

well behaved requires that changes in a time step dt are made

on lengths less than the mesh size. This gives the CFL number

condition

CFL= 0.2= c.dt/dx (1)

Meaning that changes during the time dt maximally move 20

% of the mesh length dx. Thus increasing the mesh resolution

also requires decreasing the time stepping. This advanced

version of software helps in designing the required geometry

using free hand and the model can be analysed form multiple

angles as it provides the rotation flexibility while working

with it.

III. THEORITICAL BACKGROUND

Water hammer theory dates to 19th century, where several

authors have contributed their work in analyzing this effect.

Among them, Joukowsky[14]

conducted a systematic study of

the water distribution system in Moscow and derived a

formula that bears his name, that relates to pressure changes,

p, to velocity changes, v, according to the equation

P = cU (2)

Where is the fluid mass density and c is the speed of sound.

This relation is commonly known as the Joukowsky

equation, but it is sometimes referred to as either the

Joukowsky-Frizell or the Allievi equation.

For a compressible fluid in an elastic tube, c depends on the

bulk elastic modulus of the fluid K on the elastic modulus of

the pipe E, on the inner radius of the pipe D, and on its wall

thickness. The water hammer equations are some version of

the compressible fluid flow equations. The choice of the

version is problem-dependent: basic water hammer neglects

friction and damping mechanisms, classic water hammer takes

into account fluid wall friction, extended water hammer

allows for pipe motion and dynamic Fluid Structure

Interaction[15, 16]

.

In water hammer at static condition pressure wave is a

disturbance that propagates energy and momentum from one

point to another through a medium without significant

displacement of the particles of that medium. A transient

pressure wave, subjects system piping and other facilities to

oscillating at high pressures and low pressures. This cyclic

loads and pressures can have a number of adverse effects on

the hydraulic system. Hydraulic transients can cause hydraulic

equipments in a pipe network to fail if the transient pressures

are excessively high. If the pressures are excessively higher

than the pressure ratings of the pipeline, failure through pipe

or joint rupture, or bend or elbow movement may occur.

Conversely, excessive low pressures (negative pressures) can

result in buckling, implosion and leakage at pipe joints during

sub atmospheric phases. Low pressure transients are normally

experienced on the down streamside of a closing valve. But

when the valve is closed energy losses are introduced in the

system and are normally prescribed by means of an

empirical law in terms of a loss coefficient. This

coefficient, ordinarily determined under steady flow

conditions, is known as the valve discharge coefficient,

especially when the pipeline is terminated by the valve. It

enables to quantify the flow response in terms of the

valve action through a relationship between the flow rate

and pressure for each opening position of the valve. The

discharge coefficient provides the critical piece of missing

information for the water hammer analysis. Because the

existing relationship between pressure and flow rate is often

a quadratic law type, the empirical coefficient is defined in

terms of the squared flow rate. When water distribution

system comprising a short length of pipes (i.e.,


24/128




Technology(IJRASET)

Page 24

tanks, reservoirs, junctions tend to limit further changes in

pressure and counteract the initial transient effects. An

important consideration is dead ends, which may be caused by

closure of check valves that lock pressure waves into the

system in cumulative fashion. Wave reflections will be both

positive and negative pressures; as a result the effect of dead

ends must be carefully evaluated in transient analysis.

These pressure surges provide the most effective and viable

means of identifying weak spots, predicting potentially

negative effects of hydraulic transient under a number of

worst case scenarios, and evaluating how they may possibly

be avoided and controlled. The basic pressure surge modeling

is based on the numerical conservation of mass and linear

momentum equations. For this Arbitary Lagrangian

Elurian(LE)[17]

numerical solution helps in providing the exact

analytical solution. On the other hand when poorly calibrated

hydraulic network models results in poor prediction of

pressure surges thus leading to more hydraulic transients. In

more complex systems especially, the cumulative effect of

several types of devices which influence water hammer may

have an adverse effect. However, even in simple cases, for

example in pumping water into a reservoir, manipulations

very unfavorable with regard to water hammer may take

place. For example, after the failure of the pump, the operator

may start it again. Much depends on the instant of this

starting. If it is done at a time when the entire water hammer

effect has died down, it is an operation for which the system

must have been designed.

IV. DESIGN PROCEDURE

The design and analysis of the hydraulic transient in a

pipe flow includes geometry, defining the parameters for the

required geometry, providing mesh & inputs. The 3D model is

constructed in the drawing mode of COMSOL Multiphysics.

In this, a pipe of length L = 20 m is constructed assuming that

one end is connected to a reservoir, where a valve is placed at

the other end. The pipe with inner radius of 398.5mm, the

thickness of the wall about 8 mm and Youngs modulus of

210GPa was designed. In order to verify pressure distribution,

a pressure sensor measurement point at a distance of z0= 11.15

m from the reservoir was arranged and flow has been sent into

pipe with an initial flow rate of Q0= 0.5 m3/sec.

Figure 3. Single pipe line

merged file 1

Documents