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ByFaaDoOEngineers.com 1 INTRODUCTION Broadband Powerline communications are sets of equipment, software and management services that when overlaid on the electric grid provides users with communication means over existing “power lines”. The new technology operates in the 1-30 MHz and can deliver data rates up to 200Mbps. The rationale behind providing high bit-rate data services exploiting the power grid resides in the vast infrastructure in place for power distribution, and the penetration of the service could be much higher than any other wire line alternative. In spite of the renewed interest in Power line communications, this technology still faces several technical challenges and regulatory issues: the power line channel is extremely difficult to model; it is a very noisy transmission medium; Power line cables in the 240V secondary distribution systems are often unshielded, thus becoming both sources and targets of electromagnetic interference (EMI); transformers can introduce severe distortion in the absence of bypass couplers. Since the power-line network is not designed for communications purposes, the channel suffers from multipath fading and frequency selectivity. A transfer characteristic model for the low voltage indoor power line based on the transmission line theory is developed. To model the transfer characteristics of power lines, basically there are two essential factors: the model parameters and the modeling algorithms. These two factors determine the reliability and accuracy of the model. From the ways the model parameters are obtained, the modeling technique can be classified into two approaches: the top-down approach and the bottom-up approach. In the top-down approach, the model parameters are retrieved from measurements. This approach requires little computation and is easy to implement. However, since the parameters depend on the measurement results, the model is prone to measurement errors. On the contrary, the bottom-up approach starts from theoretical derivation of model parameters. Although this approach requires more computational efforts comparing to the top-down approach, it however describes clearly the relationship between the network behavior and the model parameters. Moreover, this modeling approach is more versatile and flexible since all the parameters are formulated, making it easy to predict the changes in the transfer function should there be any change in the system configuration. The model described in this project adopts this bottom-up approach. Depending on the modeling algorithms used, the above approaches can be achieved in the time domain or the frequency domain. First frequency domain modeling, using scattering matrix is used to obtain the transfer function of the channel from which the attenuation in the signal strength and the delay or phase distortion at different frequencies is calculated. Scattering matrix gives the relationship of the incident (a) and reflected (b) waves. Broadband Power Line Communication is only interested in the transfer function in the forward direction, which is the ratio of the incident power into the receiver over the power injected by the transmitter. This can readily be expressed by b 2 / a 1 or S 21 in the scattering matrix. Secondly, IFFT (Inverse fast Fourier transform) is used to calculate the impulse response from the channel transfer function to know the multipath environment of the power line channel in time domain modeling and an echo model is developed in Simulink to represent this physical characteristics.

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Page 1: Broadband powerline communication.pdf

By FaaDoOEngineers.com

1

INTRODUCTION

Broadband Powerline communications are sets of equipment, software and

management services that when overlaid on the electric grid provides users with

communication means over existing “power lines”. The new technology operates in

the 1-30 MHz and can deliver data rates up to 200Mbps. The rationale behind

providing high bit-rate data services exploiting the power grid resides in the vast

infrastructure in place for power distribution, and the penetration of the service could

be much higher than any other wire line alternative. In spite of the renewed interest in

Power line communications, this technology still faces several technical challenges

and regulatory issues: the power line channel is extremely difficult to model; it is a

very noisy transmission medium; Power line cables in the 240V secondary

distribution systems are often unshielded, thus becoming both sources and targets of

electromagnetic interference (EMI); transformers can introduce severe distortion in

the absence of bypass couplers.

Since the power-line network is not designed for communications purposes, the

channel suffers from multipath fading and frequency selectivity. A transfer

characteristic model for the low voltage indoor power line based on the transmission

line theory is developed. To model the transfer characteristics of power lines,

basically there are two essential factors: the model parameters and the modeling

algorithms. These two factors determine the reliability and accuracy of the model.

From the ways the model parameters are obtained, the modeling technique can be

classified into two approaches: the top-down approach and the bottom-up approach.

In the top-down approach, the model parameters are retrieved from measurements.

This approach requires little computation and is easy to implement. However, since

the parameters depend on the measurement results, the model is prone to

measurement errors. On the contrary, the bottom-up approach starts from theoretical

derivation of model parameters. Although this approach requires more computational

efforts comparing to the top-down approach, it however describes clearly the

relationship between the network behavior and the model parameters. Moreover, this

modeling approach is more versatile and flexible since all the parameters are

formulated, making it easy to predict the changes in the transfer function should there

be any change in the system configuration. The model described in this project adopts

this bottom-up approach. Depending on the modeling algorithms used, the above

approaches can be achieved in the time domain or the frequency domain. First

frequency domain modeling, using scattering matrix is used to obtain the transfer

function of the channel from which the attenuation in the signal strength and the delay

or phase distortion at different frequencies is calculated. Scattering matrix gives the

relationship of the incident (a) and reflected (b) waves. Broadband Power Line

Communication is only interested in the transfer function in the forward direction,

which is the ratio of the incident power into the receiver over the power injected by

the transmitter. This can readily be expressed by b2 / a1 or S21 in the scattering matrix.

Secondly, IFFT (Inverse fast Fourier transform) is used to calculate the impulse

response from the channel transfer function to know the multipath environment of the

power line channel in time domain modeling and an echo model is developed in

Simulink to represent this physical characteristics.

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Noise in LV power-line is characterized within two categories: background and

impulsive noise. Many electric appliances frequently cause man-made

electromagnetic noise on power-line channels. Such man-made noise produces an

impulsive distortion on channel causing a burst of noise. A large impulse often causes

the entire transmitted symbol to be corrupted and it can be devastating to the overall

system performance.

The well-known multicarrier technique, orthogonal frequency division multiplexing

(OFDM), is considered as the modulation scheme for Broadband Powerline

communications. By the application of OFDM, the most distinct property of power-

line channel, its frequency selectivity, can be easily coped with. Moreover, OFDM

can perform better than single carrier modulation in the presence of impulsive noise,

because it spreads the effect of impulsive noise over multiple sub carriers. Like other

communications systems, coding can improve the OFDM system performance but

because of the nature of this channel, the achieved improvements are usually very

restricted.

Since power line technology appears to be more mature for the indoor home-

networking scenario than for the outside broadband access one, focus here is on the

development of channel and noise model for indoor power line network and thus

designing of communication system for performance analysis of Broadband PLC

using block coding techniques using software simulation in MATLAB / Simulink. In

addition, network performance analysis of CORINEX Communication, Inc.

Broadband PLC equipments for indoor power line network using measurements of

different network characteristics parameters such as throughput and latency is

performed.

This project is organized as follows. Chapter 2 deals with channel modeling using

transmission line analysis of indoor powerline. Noise modeling is mentioned in

Chapter 3 while Broadband powerline communication system is designed in Chapter

4. Broadband PLC network performance parameters measurements are discussed in

Chapter 5 while conclusion and scope for future work are mentioned at the end.

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CHANNEL MODELING

2.1 TRANSMISSION LINE ANALYSIS OF POWER LINE

The electromagnetic theory states that to achieve efficient point-to-point

transmission of power and information, the source energy must be guided. When

power lines are used to transmit high frequency communication signals, they can be

regarded as transmission lines, which guide the transverse electromagnetic (TEM)

waves along them. The cable under study in this project is the typical single-phase

house wirings commonly found in India. The cables are made up of stranded copper

conductors with PVC insulation. The three cables (live, neutral, and earth) are usually

laid inside PVC conduits that are embedded inside the concrete wall. Typically, the

live and neutral cables are used as the PLC transmission channel, which can be

approximated as a close form of the “two-wire transmission line”. According to

Electromagnetic theory, the two-wire transmission line must be a pair of parallel

conducting wires separated by a uniform distance. In the actual installation, the power

cables are simply pulled through the conduit and the separation between them is not

uniform at all. However, the conduit normally has small cross-sectional area and this

limits the variation of the separation between the cables. Hence, the assumption of

uniform separation is reasonable in this case. Based on the above consideration, the

paired power cables are regarded as a distributed parameter network, where voltages

and currents can vary in magnitude and phase over its length. Hence, it can be

described by circuit parameters that are distributed over its length as shown in Fig.2.1

below.

Fig. 2.1 Equivalent circuit of two-wire transmission line

The quantities v (z, t) and v (z +z, t) denote the instantaneous voltages at location z

and z + z, respectively. Similarly, i (z, t) and i (z +z, t) denote the instantaneous

currents at z and z +z, respectively. R defines the resistance per unit length for both

conductors (in / m), L defines the inductance per unit length for both conductors (in

H/m), G is the conductance per unit length (in S/m), and C is the capacitance per unit

length (F/m).

2.2 MODEL PARAMETERS.

Based on the lumped-element circuit of a two wire transmission line as shown above,

model parameters per unit length (m) are:

1.) Resistance „R‟ = ac

ccf /1

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Where „‟= skin depth = and is a function of frequency „f ‟. This effect

causes an increase in the resistance of the cable and it worsens as the current

frequency increases.

2.) Inductance

3.) Conductance

4.) Capacitance

Where a = radius of the copper conductor,

D = distance between conductors,

cpermeability of copper conductor,

dconductivity of the dielectric material,

and dpermittivity of the dielectric material.

2.3 MODELING THE INDOOR POWERLINE

Here the indoor power cables are approximated to be a two-wire transmission line

with solid core conductor for the ease of implementation using software simulation as

shown in Fig.2.2. The dielectric material, between the cable conductors, is

inhomogeneous in both space (due to the round shape of the cable conductor) and

contents (mixture of insulation and air). But since the cables are of close proximity to

each other, the thickness of the insulation„t‟ is comparable with that of the air space

between the conductors. In this model, the dielectric is assumed to be just a mixed

content material and the effects of the inhomogeneous in space are neglected to keep

the model tractable.

Fig. 2.2 Approximate model of the power line

Here, distance between the two conductors (Live and Neutral) „D‟= 2t + 2t + 2a

where t = thickness of insulation = 0.7 mm

a = radius of copper conductor = 0.63 mm

Therefore, D = 4.06 mm

Also, Conductivity of copper C = 5.8 x 107 S/m

)2/cosh(

)2/cosh(

)2/cosh(

aDaC

aDaG

aDaL

d

d

c

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Relative permittivity of dielectric [PVC (4) & air (1)] r = 0.8

Conductivity of dielectric d = 1 x 10-5

S/m

The length of transmission line is taken to be „S‟=5 m with shunt stub

terminated in an open circuit as shown in fig.2.3 considering the fact that, indoor

power lines are radial N-branched network as shown in fig.2.4 below.

Fig. 2.3 Configuration of simulated network

Fig. 2.4 A simplified indoor power line channel

In the above figure, port 1 is the transmitter from where the signal is sent, and

port 2 is the receiver where the signal strength is measured.

2.4 TRANSFER FUNCTION MODELING

There are three main types of attenuation for a wave propagating in the forward

direction. The first one is the line attenuation, which is caused by the heat loss and

radiations along the power line. This line attenuation is always present and it depends

on the length of the wave path and the frequency of the wave. The second type of

attenuation is caused by reflections arising from the points of impedance

discontinuities on the propagation channel. The reflected wave from the unmatched

points will interfere with the original incident wave. This kind of interferences may be

constructive or destructive, giving rise to attenuation if it is destructive. The last type

of attenuation is caused by the delayed version of the forward propagating wave

falling out of phase with the main incident forward wave, giving rise to destructive

interference and hence overall signal attenuation. Thus, frequency-domain modeling

approach using scattering matrix technique is used to account for all these reflected

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and delayed paths in the power network. Scattering matrix gives the relationship of

the incident (a) and reflected (b) waves as shown in the fig.2.5 below.

Fig. 2.5 Scattering parameters

Scattering parameters or S-parameters are properties used to describe the electrical

behavior of linear electrical networks when undergoing various steady state stimuli by

small signals.They are members of a family of similar parameters used in electronics

engineering, other examples being: Y-parameters, Z-parameters, H-parameters, T-

parameters or ABCD-parameters.They differ from these, in the sense that S-

parameters do not use open or short circuit conditions to characterize a linear

electrical network; instead matched and unmatched loads are used. Moreover, the

quantities are measured in terms of power. Although applicable at any frequency, S-

parameters are mostly used for networks operating at radio frequency (RF) and

microwave frequencies. S-parameters change with the measurement frequency so this

must be included for any S-parameter measurements stated, in addition to the

characteristic impedance or system impedance. S-parameters are readily represented

in matrix form and obey the rules of matrix algebra. The S-parameter matrix

describing an N-port network will be square of dimension 'N' and will therefore

contain N2 elements. At the test frequency each element or S-parameter is represented

by a unitless complex number, thus representing magnitude and angle, or amplitude

and phase. For all ports the reflected power waves may be defined in terms of the S-

parameter matrix and the incident power waves by the following matrix equation:

[b] = [S] [a]

where S is an N x N matrix the elements of which can be indexed using conventional

matrix (mathematics) notation. The phase part of an S-parameter is the spatial phase

at the test frequency, not the temporal (time-related) phase.

The S-parameter matrix for the 2-port network is probably the most common and it

serves as the basic building block for generating the higher order matrices for larger

networks. In this case the relationship between the reflected, incident power waves

and the S-parameter matrix is given by:

2

1

2221

1211

2

1

a

a

SS

SS

b

b

Expanding the matrices into equations gives:

and 2221212

2121111

aSaSb

aSaSb

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Each equation gives the relationship between the reflected and incident power waves

at each of the network ports, 1 and 2, in terms of the network's individual S-

parameters, S11 , S12, S21 and S22 . If one considers an incident power wave at port 1

(a1) there may result from it waves exiting from either port 1 itself (b1) or port 2 (b2).

However if, according to the definition of S-parameters, port 2 is terminated in a load

identical to the system impedance (Z0) then, by the maximum power transfer theorem,

b2 will be totally absorbed making a2 equal to zero. Therefore

1

1

1

111

V

V

a

bS and

1

2

1

221

V

V

a

bS

Similarly, if port 1 is terminated in the system impedance then a1 becomes zero,giving

2

1

2

112

V

V

a

bS and

2

2

2

222

V

V

a

bS

Each 2-port S-parameter has the following generic descriptions:

S11 is the input port voltage reflection coefficient

S12 is the reverse voltage gain

S21 is the forward voltage gain

S22 is the output port voltage reflection coefficient.

Here, S21 gives the Network Transfer Function.

2.5 DETERMINATION OF TRANSFER FUNCTION

In order to find out the degree of signal degradation in the power line channel

between two access point, software simulation is done using MATLAB programming

to obtain the transfer function whose magnitude (dB) Vs frequency plot gives the

attenuation in the signal strength and angle (radian) Vs frequency plot gives the phase

distortion or delay. The MATLAB program is as given below.

%Attenuation and Phase measurement

h=rfckt.twowire('EpsilonR',0.8,'Linelength',5,'Radius',0.63e-003,'separation',4.06e-

003,'sigmaCond',5.8e07,'sigmaDiel',1e-05,'stubmode','Shunt','termination','open');

freq=[1e6:1e6:3e7];

analyze(h,freq);

figure

plot(h,'s21','dB');

legend show

figure

plot(h,'s21','Angle (radians)');

legend show

Fig.2.6 obtained after running the program shows the presence of deep notches at

certain frequencies in the transfer function. These deep notches are resulted from

signal reflections and multipath propagations through the power line channel. For

communication to establish between two access points, the carrier frequency chosen

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must not fall at deep notches. For instance, the carrier frequency between 15 MHz and

20 MHz in the cable length of 5 m will not be chosen for transmission.

0 5 10 15 20 25 30-14

-12

-10

-8

-6

-4

-2

0

Att

enuation (

decib

els

)

Freq [MHz]

S21

Fig. 2.6 Attenuation measurement

Also, Fig.2.7 shows that when there is deep notch at certain frequency in the transfer

function, there is a discontinuity in the phase characteristics leading to phase

distortion or delay.

0 5 10 15 20 25 30-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Angle

(ra

dia

ns)

Freq [MHz]

S21

Fig. 2.7 Phase Characteristics

2.6 DETERMINATION OF IMPULSE RESPONSE

In addition to the frequency dependent attenuation that characterizes the powerline

channel, deep narrowband notches occur in the transfer function, which may be

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spread over the whole frequency range. These notches are caused by multiple

reflections at impedance discontinuities. The length of the impulse response and the

number of the occurred peaks can vary considerably depending on the environment.

This behaviour can be described by an “echo model” of the channel as illustrated in

fig.2.8 below.

Fig. 2.8 Echo model representing the multipath Broadband PLC channel model

Complying with the echo model, each transmitted signal reaches the receiver over N

different paths. Each path i is defined by a certain delay i and a certain attenuation Ci.

The Broadband PLC channel can be described by means of a discrete-time impulse

response h (t) as in equation given below:

ifjN

i

ii

N

i

i eCfHtCth 2

11

.)()(.)(

Here the impulse response is calculated from the transfer function i.e. the frequency

response by using Inverse Discrete Fast Fourier Transform (IFFT).

%Impulse response of powerline channel G =[0.9981-0i 0.9978-0.0095i 0.9974-0.0191i 0.9969-0.0291i 0.9960-0.0397i 0.9948-0.0509i 0.9932-0.0633i 0.9909-0.0771i 0.9877-0.0929i 0.9831-0.1115i 0.9764-0.1341i 0.9660-0.1623i 0.9489-0.1992i 0.9182-0.2492i 0.8562-0.3189i 0.7118-0.4053i 0.3687-0.3819i 0.1671+0.1607i 0.5660+0.4316i 0.7984+0.3643i 0.8927+0.2830i 0.9361+0.2234i 0.9589+0.1803i 0.9722+0.1480i 0.9805+0.1227i 0.9860+0.1022i 0.9898+0.0851i 0.9925+0.0703i 0.9944+0.0572i 0.9958+0.0454i 0.9968+0.0345i]; w =[0:1e6:3e7]; Ts=pi/3e7; datafr =idfrd(G,w,Ts); datf =iddata (datafr); dat =ifft (datf); plot(dat)

Impulse response plot after running the program is as obtained below. In the impulse

response, the multiple propagation paths can be seen.

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0 1 2 3 4 5 6 7

x 10-6

-1

0

1

2

3

4

5

6

7

Time (microseconds)

outp

ut

'y(t

)'

0 1 2 3 4 5 6 7

x 10-6

0

2

4

6

8

Time (microseconds)

input

'u(t

)'

Fig. 2.9 Impulse response plot at output and input, the multiple propagations paths can

be seen

The dominant paths of the impulse response are sufficiently covered by the simple

N=6 path model from which the attenuations and delays are calculated to develop a

six path echo model in SIMULINK as shown below. Impulsive noise is added in the

echo channel model which is explained in the later chapters.

Path Number Attenuation ‘Cn’ Delay ‘ n’

1 0.875 0

2 0.1775 0.315e-6

3 0.07 0.579e-6

4 0.0525 1e-6

5 0.0375 1.3e-6

6 0.0375 1.8e-6

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Fig. 2.10 Echo model representing the multi-path channel model of Broadband PLC

developed in Simulink

1

Out1

imp

Signal From

Workspace

Product5

Product4

Product3

Product2

Product1

Product

z-N

Delay5

z-N

Delay4

z-N

Delay3

z-N

Delay2

z-N

Delay1

z-N

Delay

0.037

Constant5

0.037

Constant4

0.052

Constant3

0.07

Constant2

0.177

Constant1

0.875

Constant

Add1

In1

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NOISE MODELING

3.1 NOISE DESCRIPTION

Since the power-line network is not designed for communications purposes, the

channel exhibits an unfavorable frequency selective transfer function. Furthermore,

this channel is distorted by impulsive noise and by severe narrowband interference.

Unlike many other communication channels, power-line channel does not represent

an additive white Gaussian noise (AWGN) environment. Noise in LV power-line is

characterized within two categories: background and impulsive noise. Many electric

appliances frequently cause man-made electromagnetic noise on power-line channels.

Such man-made noise produces an impulsive distortion on channel causing a burst of

noise. A large impulse often causes the entire transmitted symbol to be corrupted and

it can be devastating to the overall system performance. Background noise usually

consists of coloured background noise and narrowband noise. Here only coloured

background noise in residential environment is considered.

3.1.1 COLOURED BACKGROUND NOISE

Coloured background noise power spectral density (psd) is relatively lower and

decrease with frequency. This type of noise is mainly caused by a superposition of

noise sources of lower intensity. Contrary to the white noise, which is a random noise

having a continuous and uniform spectral density that is substantially independent of

the frequency over the specified frequency range; the coloured background noise

shows strong dependency on the considered frequency. The parameters of this noise

vary over time in terms of minutes and hours.

For the model of the coloured background noise psd, the measurements have shown

that a first-order exponential function is more adequate, as formulated by equation

given below;

1/

1.ff

OCBN eNNN

with No the constant noise density, N1 and f 1 are the parameters of the exponential

function, and the unit of psd is dBV/Hz1/2

. The psd of coloured background noise in

residential environment according to [2] is given by following equation:

6,3/][.3535)( MHzf

BN efN for residential environments

Matlab program for plot of coloured background noise is as given below:

%Equation for coloured background noise for resi. environment function y = cbnpsd(f) y = -35 + 35*exp (-(f/6)); %coloured background noise power spectral density

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f=[1:1:30]; a=cbnpsd(f); plot(f,cbnpsd(f)) title('power spectral density');

Fig. 3.1 Power Spectral density of coloured background noise in residential

environment

3.1.2 IMPULSIVE NOISE

The impulsive noise class is composed of the periodic impulses that are synchronous

with the main frequency and the asynchronous impulsive noise. The measurements

taken in [2] show that this class is largely dominated by asynchronous impulsive

noise, whose impulses are mainly caused by switching transients in the networks.

These impulses have durations of some microseconds up to a few milliseconds with

an arbitrary interarrival time. Their power spectral density can reach value of more

than 50 dB above the level of the background noise, making them the principal cause

of error occurrences in the digital communication over PLC networks. One approach

to model these impulses is a pulse train with pulse width tw, pulse width „A‟,

interarrival time ta and a generalized pulse function p (t / tw) with unit amplitude and

impulse width tw as given by equation below:

)(.)(,

,

iw

ia

i

iimpt

ttpAtn

power spectral density

Frequency (MHz)

dB

mic

rovolt /

Hz 1

/ 2

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Fig. 3.2 The impulse model used for impulsive noise class modeling

The parameters tw,i, Ai and ta,i of impulse i are random variables, whose statistical

properties are measured and investigated in []. The measured impulses have shown

that 90% of their amplitudes are between 100 and 200mV. Only less than 1% exceeds

maximum amplitude of 2V. The measurements of the impulse width tw have also

shown that only about 1% of the measured impulses have a width exceeding 500s

and only 0.2% of them exceeded 1 ms. Finally, the interarrival time that separates two

successive impulses is below 200ms for more than 90% of the recorded impulses.

Based on the above formulation an impulsive noise is simulated using MATLAB

program which is then converted to a Simulink block and then added in the channel

model as shown in fig 2.10. Here, only impulsive noise is added to the channel model

since they are the principal cause for burst error, and the effect of coloured

background noise is neglected for the ease of software simulation

Matlab program for generation of impulsive noise is as given below:

% Impulsive noise plot imp=rand(1,470); n=1; while n<470-250; n=n+floor(249*rand(1))+5; imp(n:n+4)=[100 120 80 60 150]; end plot(imp)

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Fig. 3.3 Impulsive Noise Plot

0 50 100 150 200 250 300 350 400 450 5000

50

100

150

Time (ms)

Am

plit

ude (

mV

)

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BROADBAND PLC SYSTEM DESIGN

4.1 INTRODUCTION

A block diagram of general coded communication system is shown in fig. Forward

error correction (FEC) coding is done on the input data prior to transmission, to

provide some security. The data stream is converted by digital data modulator into a

signal suitable for transmission over the waveform channel. The demodulator

recovers its best estimate of the input data from the received signal which on power

line waveform channels is an attenuated, noisy and distorted version of input signal.

As a result bit errors occur during the detection process.

4.2 BLOCK CODING

In block coding, the data to be transmitted is segmented into blocks of a fixed length

k. To each block of the information message m, a certain amount of parity bits are

added. The information bits and the parity bits together form the code words c of

length n, as illustrated by fig.4.1, which shows a communications system coding the

original information before submitting them to the modulation. The rate of a (n,k)

block code is defined as r = k/n.

Fig. 4.1 General model of a block coded communication system

The block coding process is one to one i.e. the same dataword is always encoded as

the same codeword. An error detecting code can detect only the types of errors for

which it is designed, other types of errors may remain undetected. One of the central

Data

source Block encoder

Modulator

Channel

Demodulator Decoder Data sink

Noise

Information

vector

m c

Channel codeword

^m

Estimate of m Received

vector

r

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concepts in coding for error control is the idea of the hamming distance. The

hamming distance between two words (of the same size) is the no. of differences

between the corresponding bits. The hamming distance can easily be found if we

apply the XOR operation on the words and count the no. of 1‟s in the result. The

measurement i.e. used for designing a code is the minimum hamming distance. The

minimum hamming distance is the smallest hamming distance between all possible

pairs in a set of words. To guarantee the detection of up to s errors in all cases, the

minimum hamming distance in a block code must be dmin = s+1, so that the received

codeword does not match a valid codeword. To guarantee correction of up to t errors

in all cases, the minimum hamming distance in a bock code must be dmin = 2t+1.

Almost all block codes belong to a subset called linear block codes. A linear block

code is a code in which XOR (addition modulo-2) of two valid codewords creates

another valid codeword. However, more formal definition requires the knowledge of

abstract algebra particularly that of Galois field. A Galois field is an algebraic field

that has a finite number of members. Galois fields having 2m

members are used in

error-control coding and are denoted GF (2m

). A primitive polynomial for GF(2m

) is

the minimal polynomial of some primitive element (a cyclic generator of the group of

nonzero elements of GF(2m

) i.e. every nonzero element of the field can be expressed

as the primitive element raised to some integer power) of GF(2m

). That is, it is the

binary-coefficient polynomial of smallest nonzero degree having a certain primitive

element as a root in GF (2m

). As a consequence, a primitive polynomial has degree m

and is irreducible.

4.2.1 Hamming Encoder and Decoder:

It is a type of a linear block code. The Hamming Encoder block creates a Hamming

code with message length K and codeword length N. The number N must have the

form 2m

-1, where m is an integer greater than or equal to 3. Then K equals N-m. The

input must contain exactly K elements. If it is frame-based, then it must be a column

vector. The output is a vector of length N. The coding scheme uses elements of the

finite field GF (2m

). A default primitive polynomial is used. The algorithm uses

„gfprimdf (m)‟ as the primitive polynomial for GF (2m

).

The Hamming Decoder block recovers a binary message vector from a binary

Hamming codeword vector. For proper decoding, the parameter values in this block

should match those in the corresponding Hamming Encoder block.

The fig.4.2 and fig.4.3 shows the settings used in the hamming encoder and decoder

block of the Simulink. Here, N = 255, m = 8 this implies K = 255 – 8 = 247.

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Fig. 4.2 Hamming Encoder settings

Fig. 4.3 Hamming Decoder settings

4.3 PUNCTURE AND INSERT ZERO BLOCK

The Puncture block creates an output vector by removing selected elements of the

input vector and preserving others. The input can be a real or complex vector of

length K. The block determines which elements to remove or preserve by using the

binary Puncture vector parameter. If Puncture vector (k) = 0, then the kth element of

the input vector does not become part of the output vector. If Puncture vector (k) = 1,

then the kth element of the input vector is preserved in the output vector.

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Here, k is between 1 and K. The preserved elements appear in the output vector in the

same order in which they appear in the input vector. The block can accept the data

type‟s int8, uint8, int16, uint16, int32, uint32, Boolean, single, double, and fixed-

point. The data type of the output will be the same as that of the input signal. If the

input is frame-based, then both it and the Puncture vector parameter must be column

vectors. The length of the Puncture vector parameter must divide K. The block repeats

the puncturing pattern, if necessary, to cover all input elements.

The Insert Zero block constructs an output vector by inserting zeros among the

elements of the input vector. To implement punctured coding using the Puncture and

Insert Zero blocks, one should use the same vector for the Insert zero vector

parameter in this block and for the Puncture vector parameter in the Puncture block.

The settings used in the simulation are shown in fig.4.4 and fig.4.5 below:

Fig. 4.4 Puncture settings

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Fig. 4.5 Insert Zero settings

4.4 INTERLEAVING / DE-INTERLEAVING

The occasional deep fades in the frequency response of the transmission channel

cause some group of sub-carriers to be less reliable than other groups and hence cause

bit errors to occur in bursts rather than independently. Since channel coding schemes

are normally designed to deal with independent errors and not with error bursts, the

interleaving technique is used to guarantee this independence by effecting randomly

scattered errors. For this reason, in the transmitter and after the coding, the bits are

randomly permuted in such a way that adjacent bits are separated by several numbers

of bits. At the receiver side, before the decoding, the de-interleaving is performed in

order to get the original ordering of bits. The interleaving function can be realized by

block or convolution interleavers. Interleaver used here is Random Interleaver / de-

interleaver, a type of block interleaver.

4.4.1 Random Interleaver / De-Interleaver:

The Random Interleaver block rearranges the elements of its input vector using a

random permutation. The Number of elements parameter indicates how many

numbers are in the input vector. If the input is frame-based, then it must be a column

vector. The block can accept the data type‟s int8, uint8, int16, uint16, int32, uint32,

Boolean, single, double, and fixed-point. The data type of this output will be the same

as that of the input signal. The Initial seed parameter initializes the random number

generator that the block uses to determine the permutation. The block is predictable

for a given seed, but different seeds produce different permutations.

The Random De-interleaver block rearranges the elements of its input vector using a

random permutation. If this block and the Random Interleaver block have the same

value for Initial seed, then the two blocks are inverses of each other. The settings used

in the blocks for simulation are shown in the fig.4.6 and fig.4.7 below:

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Fig. 4.6 Random Interleaver

Fig. 4.7 Random Deinterleaver

4.5 DIGITAL MODULATION

4.5.1 Introduction

Converting digital data to a bandpass analog signal is traditionally called digital to

analog conversion. It is the process of changing one of the characteristics of an analog

signal based on the information in digital data. Any of the three characteristics

(amplitude, frequency, or phase) can be altered giving at least three mechanisms for

modulating digital data into an analog signal like Amplitude shift keying (ASK),

Frequency shift keying (FSK), and Phase shift keying (PSK).

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In addition there is a fourth and better mechanism that combines changing both the

amplitude and phase, called Quadrature Amplitude Modulation (QAM). It is the most

efficient and commonly used.

4.5.2 M-ary Modulation Techniques

In an M-ary signaling scheme, one of M possible signals, s1(t), s2(t)… sM(t), is sent

during each signaling interval of duration T. For almost all applications, the no. of

possible signals M = 2n, where n is an integer, and symbol duration T = nTb, where Tb

is the bit duration. These signals are generated by changing the amplitude, phase, or

frequency of a carrier in M discrete steps for M-ary ASK, M-ary PSK, and M-ary

FSK digital modulation respectively. Another way of generating M-ary signals is to

combine different methods of modulation into a hybrid form. One form of this hybrid

modulation, called M-ary QAM, has some attractive properties.

M-ary signaling schemes are preferred over binary signaling schemes for transmitting

digital information over band-pass channels when the requirement is to conserve

bandwidth at the expense of increased power. E.g. for transmitting information

consisting of binary sequence with bit duration Tb using Binary PSK requires a

bandwidth inversely proportional to Tb . However, for a block of n bits and using an

M-ary PSK scheme with M = 2n and symbol duration T = nTb, the bandwidth required

is inversely proportional to 1 / nTb .thus, it is seen that it enables a reduction in

transmission bandwidth by a factor n = log2 M over Binary PSK.

4.5.3 Constellation diagram

Constellation diagram is useful when we are dealing with multilevel ASK, PSK or

QAM. In a constellation diagram, a signal element type is represented as a dot,

particularly helpful to define amplitude and phase of a signal element when using two

carriers (in phase and quadrature). The diagram has two axes, horizontal related to in-

phase carrier and vertical related to quadrature carrier. For each point on the diagram,

four pieces of information can be deduced. The projection of the point on the X axis

defines the peak amplitude of the quadrature component; the projection of the point

on the Y axis defines the peak amplitude of the quadrature component. The length of

the line (vector) that connects the point to the origin is the peak amplitude of the

signal element (combination of the X and Y components); the angle the line makes

with the X axis is the phase of the signal element.

4.5.4 Quadrature Amplitude Modulation (QAM)

QAM, which combines ASK and PSK, is the dominant method of digital to analog

modulation. PSK is limited by the ability of the equipment to distinguish small

differences in phase which limits its potential bit rate. The idea of using two carriers,

one in phase and the other quadrature, with different amplitude levels for each carrier

is the concept behind quadrature amplitude modulation (QAM). QAM has the same

advantage as PSK over ASK i.e. it is less susceptible to noise. Moreover, it is seen

that in an AWGN (Additive White Gaussian Noise) channel, M-ary QAM

outperforms the corresponding M-ary PSK in error performance for M>4.

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The Rectangular QAM Modulator Baseband block modulates using M-ary quadrature

amplitude modulation with a constellation on a rectangular lattice. The output is a

baseband representation of the modulated signal. The signal constellation has M

points, where M is the M-ary number parameter. M must have the form 2K for some

positive integer K. The block scales the signal constellation based on the setting of the

Normalization method parameter. Possible scaling conditions for value of

Normalization method parameter are:

Min. distance between symbols: The nearest pair of points in the constellation is

separated by the value of the Minimum distance parameter.

Average Power: The average power of the symbols in the constellation is the Average

power parameter.

Peak Power: The maximum power of the symbols in the constellation is the Peak

power parameter.

The input and output for this block are discrete-time signals. The Input type parameter

determines whether the block accepts integers between 0 and M-1, or binary

representations of integers. If Input type is set to Integer, then the block accepts

integers. The input can be either a scalar or a frame-based column vector, and can

accept the data type‟s int8, uint8, int16, uint16, int32, uint32, single, and double. If

Input type is set to Bit, then the block accepts groups of K bits, called binary words.

The input can be either a vector of length K or a frame-based column vector whose

length is an integer multiple of K. For bit inputs, the block can accept int8, uint8,

int16, uint16, int32, uint32, boolean, single, and double. The Constellation ordering

parameter indicates how the block assigns binary words to points of the signal

constellation. Such assignments apply independently to the in-phase and quadrature

components of the input:

If Constellation ordering is set to Binary, then the block uses a natural binary-coded

constellation.

If Constellation ordering is set to Gray and K is even, then the block uses a Gray-

coded constellation.

If Constellation ordering is set to Gray and K is odd, then the block codes the

constellation so that pairs of nearest points differ in one or two bits.

The Rectangular QAM Demodulator Baseband block demodulates a signal that was

modulated using quadrature amplitude modulation with a constellation on a

rectangular lattice. The settings used are shown in the fig.4.8 and fig.4.9 below:

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Fig. 4.8 Rectangular QAM Modulator Baseband

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Fig. 4.9 Rectangular QAM Demodulator Baseband

4.6 OFDM (Orthogonal Frequency Division Multiplexing)

4.6.1 Modulation Principles

MultiCarrier Modulation (MCM) is the principle of transmitting data by dividing the

stream into several parallel bit streams, each of which has a much lower bit rate, and

by using several carriers, called also subcarriers, to modulate these substreams.

Orthogonal Frequency Division Multiplexing is a special form of MCM with densely

spaced subcarriers and overlapping spectra. To allow an error-free reception of

OFDM signals, the subcarriers waveforms are chosen to be orthogonal to each other.

In spite of its robustness against frequency selectivity, which is seen as an advantage

of OFDM, any time-varying character of the channel is known to pose limits to the

system performance. Time variations are known to deteriorate the orthogonality of the

subcarriers.

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4.6.2 Generation of OFDM Signals

The generation of the OFDM symbols is based on two principles. First, the data

stream is subdivided into a given number of substreams, where each one has to be

modulated over a separate carrier signal, called subcarrier. The resulting modulated

signals have to be then multiplexed before their transmission. Second, by allowing the

modulating subcarriers to be separated by the inverse of the signaling symbol

duration, independent separation of the frequency multiplexed subcarriers is possible.

This ensures that the spectra of individual subcarriers are zeros at other subcarrier

frequencies, consisting of the fundamental concept of orthogonality and the OFDM

realization. Fig. 4.10 shows the basic OFDM system. The data stream is subdivided

into N parallel data elements and are spaced by t = 1/fs, where fs is the desired

symbol rate. N serial elements modulate N subcarrier frequencies which are then

frequency division multiplexed. The symbol interval has now been increased to Nt

which provides robustness to the delay spread caused by the channel. Each one of two

adjacent subcarrier frequencies is then spaced by the interval formulated by equation:

tNf ./1

This ensures that the subcarrier frequencies are separated by multiples of 1/T where T

in this phase is the OFDM symbol duration, so that the sub carriers are orthogonal

over symbol duration in the absence of distortions.

Fig. 4.10 Basic OFDM Transmitter

According to the basic OFDM realization, the transmitted signal s(t) can be expressed

by

lTtkbtsN

k l

kl

1

0

][)(

with the pulse having the function p(t) and fk = k/T, each subcarrier can be formulated

by

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tfj

kketpt

2.

The basis { 110 ,, N } is orthogonal, therefore

kiif

kiifdttt i

T

K

,0

,1{*

0

Then the transmitted signal can be expressed as

tfjN

k l

lkelTtpkbts

21

0

.)(

By sampling at a rate Ts= T/N

SS NTknTj

N

SS

N

k l

l elNTnTkbnx/2

1

0

.][

NknjN

k l N

l elNnkbnx /21

0

.][

with

otherwise

NlnlNforlNn

N

,0

)1(,1{

the signal can be presented in the form

NknjN

K

l

l N

ekblNnnx /21

0

.][

),(.][ nbIDFTlNnnx l

l N

where IDFT is Inverse Discrete Fourier Transform.

From the above derivation, it can be deduced that for the generation of the OFDM

signals x[n] an IDFT block processing is required. The OFDM signal generation can

be further optimized by calculating the IDFT of the original signals by the mean of

the Inverse Fast Fourier Transform (IFFT).

The blocks used in Simulink for OFDM Transmitter and OFDM Receiver are shown

in fig.4.11 and fig.4.12 respectively.

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1

Out

Zero pad

for OFDM

Zero pad for OFDM

Unipolar to

Bipolar

Converter

PN Sequence

Generator

PN Sequence

Generator

Select

Rows

Multiport

Selector

Vert Cat

Matrix

Concatenation

IFFT

IFFT

-1

0+0i U U(E)

Add Cyclic

Prefix

1

In

Fig. 4.11 OFDM Transmitter

2

PIlots

1

DataU U(E)

Remove Cyclic

Prefix

Select

Rows

Remove

Pilots

U U(E)

Remove

zero-padding

and

reorder

To

Frame

Frame Status

Conversion

FFT

FFT

1

Received signal

Data

Pilots

Fig. 4.12 OFDM Receiver

4.7 DATA SOURCE

4.7.1 Bernoulli Binary Random Generator

The Bernoulli Binary Generator block generates random binary numbers using a

Bernoulli distribution. The Bernoulli distribution with parameter p produces zero with

probability p and one with probability 1-p. The Bernoulli distribution has mean value

1-p and variance p(1-p). The Probability of a zero parameter specifies p, and can be

any real number between zero and one. The number of elements in the Initial seed and

Probability of a zero parameters becomes the number of columns in a frame-based

output or the number of elements in a sample-based vector output. The settings used

for simulation are shown in fig.4.13 below:

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Fig. 4.13 Bernoulli Random Binary Generator

4.8 DATA SINK

4.8.1 Error Rate Calculation

The Error Rate Calculation block compares input data from a transmitter with input

data from a receiver. It calculates the error rate as a running statistic, by dividing the

total number of unequal pairs of data elements by the total number of input data

elements from one source. This block can be used to compute either symbol or bit

error rate, because it does not consider the magnitude of the difference between input

data elements. If the inputs are bits, then the block computes the bit error rate. If the

inputs are symbols, then it computes the symbol error rate. This block inherits the

sample time of its inputs.

This block produces a vector of length three, whose entries correspond to:

The error rate.

The total number of errors, that is, comparisons between unequal elements.

The total number of comparisons that the block made.

The Receive delay and Computation delay parameters implement two different types

of delays for this block. One is useful when part of the model causes a lag in the

received data, and the other is useful when one want to ignore the transient behavior

of both input signals:

The Receive delay parameter is the number of samples by which the received

data lags behind the transmitted data. This parameter tells the block which

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samples "correspond" to each other and should be compared. The receive

delay persists throughout the simulation.

The Computation delay parameter tells the block to ignore the specified

number of samples at the beginning of the comparison.

The settings used in the simulation are as shown in fig. 4.14 below.

Fig. 4.14 Error Rate Calculation settings

4.8.2 DISPLAY

The Display block shows the value of its input on its icon. The amount of data

displayed and the time steps at which the data is displayed are determined by block

parameters:

The Decimation parameter enables to display data at every nth sample, where

n is the decimation factor. The default decimation, 1, displays data at every

time step.

The Sample time parameter enables to specify a sampling interval at which to

display points. This parameter is useful when using a variable-step solver

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where the interval between time steps might not be the same. The default

value of -1 causes the block to ignore the sampling interval when determining

the points to display.

The settings used are:

Fig. 4.15 Display block settings.

4.9 SIMULATION AND RESULTS

Communication system is designed in Simulink with each block simulated as a

different subprogram as shown in fig.4.16 below.

Unipolar to

Bipolar

Converter

Unipolar to

Bipolar

Converter

Terminator

Rectangular

QAM

Rectangular QAM

Modulator

Baseband

Rectangular

QAM

Rectangular QAM

Demodulator

Baseband

Random

Interleaver

Random

Interleaver

Random

Deinterleaver

Random

Deinterleaver

In1

Out1

PLC Channel

Puncture

P2 Puncture

OFDM

Transmitter

OFDM

Receiverpilots

Insert Zero

Insert Zero

Hamming en

Hamming Encoder

Hamming de

Hamming Decoder

sRef

sDeldelay

Find

Delay

Find Delay

Error Rate

Calculation

Tx

Rx

0

Display1

0

Bernoulli

Binary

Bernoulli Random

Binary Generator

BR

BR

Fig.4.16 Simulink model of the Broadband powerline communication system.

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The display block showed the following bit error rate as shown in fig.4.17 below

obtained after running the model i.e. when both channel multipath effect and the

effect of impulsive noise is considered.

Fig.4.17 Bit Error Rate when the effect of both multipath and impulsive noise is

considered.

Now, when the effect of impulsive noise is neglected from the model than the bit error

rate obtained is as shown in fig.4.18 below:

Fig.4.18 Bit Error Rate when effect of only multipath effect is considered.

From the above figures it is seen that both impulsive noise and multipath effect have a

very adverse effect on the BER. Moreover it is also seen that BER caused by

multipath effect almost does not change even when the effect of application of

impulsive noise is neglected. Thus, it can be concluded that the main obstacle to

achieve the good BER performance of the OFDM system is the multipath effect.

The plot of BER versus SNR is drawn using „bertool‟ in MATLAB under the

effect of both multipath effect and impulsive noise as shown below in fig.4.19. An

almost constant BER is obtained up to SNR of 10 dB.

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Fig.4.19 BER under the effect of both impulsive noise and multipath effect

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BROADBAND PLC NETWORK PERFORMANCE PARAMETERS

MEASUREMENT

5.1 BROADBAND PLC EQUIPMENTS

Head End (HE): Communication infrastructure module for injection of PLC signal

over Low voltage electrical network.

Customer Premises Equipment (CPE): Ethernet Bridge switch 10/100 Mbps by

PLC, designed for connection of end-user multimedia devices through any power

outlets.

The Broadband PLC equipments used for performance analysis network are of

CORINEX Communications, Inc. The “Corinex AV200 Powerline Adapter” can be

configured both as a Head End as well as a Customer Premises Equipment by

changing settings in its configuration files in the firmware.

It enables users to connect individual PCs or other devices with Ethernet

communications links into a local area network through existing electric power lines

(Powerline). After successful installation, the AV Powerline network behaves like a

traditional LAN for computers. The Corinex AV200 Powerline Adapter supports up

to 200 Mbps network speed.

Front Panel Description

Fig 5.1: Corinex AV200 Powerline Adapter front view

LED Definitions (LEDs from left to right)

1. POWER Green On: Power on

Off: Power off

2. PLC Green On: Powerline activity

Off: No Powerline activity

Blinking: Receiving/Transmitting data

3. ETHERNET Green On: Link on LAN

Off: No link on LAN

Blinking: receiving/transmitting data

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Rear Panel Description

Fig 5.2: Corinex AV200 Powerline Adapter rear view

Connector Definitions (Connectors from left to right)

1. LAN: 1x RJ-45 LAN10/100 Ethernet port

2. Power cord: Power supply & Powerline connector

Specifications:

Standards Compliance: IEEE 802.3u

Speed: Up to 200 Mbps on physical layer

AC Plug Type: US, EU, UK and AUS

LED Status Lights: Power, PLC Link/Activity, Ethernet Link

Interface: 10/100BaseT Fast Ethernet, Powerline

Frequency Range used: 2 – 34 MHz

Power Input: 85 to 265 V AC, 50/60 Hz

Dimensions: 148 mm L x 106 mm W x 47 mm H

Transmitted Power spectral density: -56 dBm/Hz

Power Consumption: 5W

Safety & EMI:UL/EN 60950, FCC Part 15, EN 55022 EMC limits

5.2 BROADBAND PLC NETWORK

Network performance analysis of CORINEX Communication, Inc. Broadband PLC

equipments for indoor power line network using measurements of different network

characteristics parameters such as throughput and latency is conducted for the

network as shown in fig.5.3 below:

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Fig.5.3 SCADA Lab Broadband PLC network.

The throughput is a measure of how fast the data can be sent through the network. It

is different from the bandwidth in the sense that a link may have bandwidth of B bps,

but only T bps can be sent through this link with T always less than B. Bandwidth is a

potential measurement of a link. The measurements obtained for different CPE‟s

(Customer Premises Equipment) both in uplink (i.e. from the CPE to the Head End)

and the downlink (i.e. from the Head End to the CPE) direction is as measured below.

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Fig.5.4 Telnet screen showing throughput.

The latency or delay defines how long it takes for an entire message to completely

arrive at the destination from the time the first bit is sent out from the source. It is

made up of four components: propagation time (time required for a bit to travel from

the source to the destination equal to the distance divided by the propagation speed),

transmission time (depends upon the message size divided by the bandwidth), queuing

time (time needed for each intermediate or end device to hold the message before it

can be processed, it is not a fixed factor, it changes with the load imposed on the

network) and processing delay. It is calculated by using the ping program.

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Measurement of throughput and latency for different CPE‟s is shown in Table 5.1 in

tabular form as shown below:

TABLE 5.1: Measurement of Throughput and Latency

Customer

premises

Equipment

(CPE) no.

Transfer speed

[ Mbps ]

Latency

[ ms ]

Distance with

respect to Head

End (HE) DL UL

A 50 55 7 ~ 14 < 15 m

B 65 66 -do- < 15 m

C 101 63 -do- > 15 m

It is seen that broadband plc promises high bit rate in terms of throughput. Latency

measured using ping program almost remains same with distance. However,

throughput for CPE C almost doubles due to straight wire connection with respect to

other CPE‟s which belong to a branched network of the SCADA Lab.

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CONCLUSION AND FUTURE WORK

Bit Error Rate (BER) of the OFDM system under both impulsive noise and multipath

effect is calculated & it is seen that they have a very adverse effect on the BER.

Moreover it is also seen that BER caused by multipath effect almost does not change

with the application of impulsive noise. Thus, it can be concluded that the main

obstacle to achieve the good BER performance of the OFDM system is the multipath

effect.

It is seen that broadband plc promises high bit rate with measurements taken

on a real Broadband PLC network. Latency measured using ping program almost

remains same with distance. However, throughput for CPE C almost doubles due to

straight wire connection with respect to other CPE‟s which belong to a branched

network.

The main purpose of this work was the designing of the Broadband powerline

communication system using software simulation and to do performance analysis in

terms of bit error rate and as well as to test the real system in terms of their network

performance parameters. However, for future work performance enhancement of the

system can be done in terms of the bit error rate and the real system can be used for

different utility applications like automatic meter reading (AMR), transformer

monitoring, etc.

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REFERENCES

[1] C W Gellings and K George, “Broadband over power lines 2004: Technology and

Prospects", Electric Power Research Institute (EPRI),USA, Oct 2004.

[2] H. Hrasnica, A. Haidine, and R.Lehnert, Broadband Powerline Communications.

London, U.K.: Wiley, 2004.

[3] H. Meng, S. Chen, Y. L. Guan, C. L. Law, P. L. So, E. Gunawan, and T.T. Lie,

"Modeling of transfer characteristics for the broadband power line communication

channel,” IEEE Trans. Power Del., vol. 19, no.3, Jul. 2004, pp. 1057–1064.

[4] Ludwig, Reinhold and Pavel Bretchko, RF Circuit Design: Theory and

Applications, Prentice-Hall, 2000.

[5] P Amirshahi, S M Navidpour and M Kavehrad, “Performance Analysis of

uncoded & coded OFDM Broadband transmission over low voltage power line

channels with impulsive noise,” IEEE Trans. Power Del., vol. 21, no.4, Oct. 2006, pp.

1927-1934.

[6] S. Galli and T. Banwell, “A novel approach to the modeling of the indoor power

line channel-part II: Transfer function and its properties,” IEEE Trans. Power Del.,

vol. 20, no. 3, Jul. 2005, pp. 1869–1878.

[7] V C Gungor and F C Lambert, “A survey on communication networks for Electric

system automation”, ELSEVIER Journal of computer networks 50(2006), pp 877-

897.

[8] Y H Ma, P L So and E Gunawan, “Performance analysis of OFDM systems for

Broadband power line communications under impulsive noise and multipath effects”,

IEEE Trans. Power Del., vol. 20, no. 2, Apr. 2005, pp. 674-682.