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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
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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
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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.
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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
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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
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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
receive antenna, h2,2 is the channel from 2nd
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
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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
receive antenna, h2,1 is the channel from 2nd
transmit
antenna to 1st
receive antenna, h2,2 is the channel from 2nd
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:
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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.
From the plot it is clear that 2x2 MIMO-OFDM system with
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.
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From the plot it is clear that 2x2 MIMO-OFDM system with
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 .
From the plot it is clear that 2x2 MIMO-OFDM system with
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.
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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
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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
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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+ )=
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=
+ +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)=
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()+ ()(+ 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.
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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
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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.
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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
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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
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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
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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.
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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.
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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
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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.,
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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