Simulation and Analysis of the Impulsive NoiseEffects in DSL Video Using Ptolemy II

Fernanda Smith, Francisco Muller, Aldebaro Klautau and Evaldo PelaesSignal Processing Laboratory, Federal University of Para, Belem, Brazil

E-mails: {fesmith, fmuller, aldebaro, pelaes}@ufpa.br.

Abstract—This work presents the simulation and analysis ofthe impact of impulsive noise on video transmission over ADSLusing the Ptolemy II framework. The implemented softwaremodem includes channel coding and modulation used in theADSL standard and an impulsive noise channel model. In theresults, the effect of the use of different interleaver depth valuesin video transmission is evaluated using two different metrics:peak signal-to-noise ratio and double stimulus impairment scale.

I. INTRODUCTION

With the growing demand for higher data rates, new tech-

nologies have emerged over recent years to enable fast and

cheap access to the Internet, mainly targeting home users. In

this sense, the Digital Subscriber Line (DSL) technologies are

highlighted, using the conventional telephone lines to transmit

data at high speed, without affecting the service of voice

communication. Asymmetric Digital Subscriber Line (ADSL)

is one of the most popular DSL technologies, in which data

transmission is asymmetric, i.e., the data transfer rate is higher

in downstream direction (toward the subscriber), than in the

upstream direction (subscriber to the network).

Besides data transmission, ADSL allows voice and video

transmission. Transmission of video services is one of the most

important applications for the end user. As an example, the

Internet Protocol Television (IPTV) is a emerging technology

that delivers Television signals over DSL.

The use of telephone lines limits data transmission rates,

mainly due to crosstalk and impulsive noise. Crosstalk arises

due to electromagnetic interference between the twisted pairs

of telephone lines, while impulsive noise is caused by tempo-

rary electromagnetic events that occur in the vicinity of the

telephone lines [1]. Crosstalk can be minimized by the use of

Dynamic Spectrum Management (DSM). However, impulsive

noise is considered a major problem due to the irregularity of

its occurrence and intensity, making it difficult to be modeled

and predicted. Therefore, it can have a major impact in video

transmission in ADSL. In IPTV services over ADSL, these

impacts are significant and need to be minimized, since IPTV

needs to meet certain levels of QoS (quality of service), QoE

(quality of experience), interactivity and reliability. In [2], an

analysis of the impact of impulsive noise on DSL systems in

general is described.

The degradation caused by different types of interference

and noise can be prevented with the use of channel coding

techniques. Error correcting codes are used to detect and

correct errors occurring during transmission. Channel coding is

essential to combat impulsive noise. In ADSL, Reed-Solomon

codes are used as a method for error detection and correction,

along with the use of an interleaver, which spreads the burst

errors caused by impulsive noise, therefore allowing for a more

effective use of Reed-Solomon.

The implementation of an ADSL modem (as standardized

by ANSI [3] and ITU [4]) in software has several benefits,

including the possibility of modifying transmission parameters

that are not accessible in a commercial modem. Moreover,

an ADSL software modem can serve as a tool for learning

and research, allowing for a greater understanding of the

ADSL modem operation, as well as helping in the test of

new algorithms. An example of a software implementation of

ADSL2 systems using LabVIEW is shown in [5].

The objective of this work is to present the simulation and

analysis of the impact of impulsive noise on video transmission

over ADSL. In order to achieve this, an ADSL software

modem was developed. This software modem implements

modulation and channel coding of an ADSL modem using

the Ptolemy II framework. The choice of Ptolemy II is based

on the possibility and facility of modeling and simulating

complex systems like an ADSL modem.

The work is organized as follows: Section II describes

the coding and error control in ADSL systems. Next, a

brief description of the modulation scheme is presented in

Section III, followed by the description of the ADSL software

modem implemented using Ptolemy II in Section IV. The

video transmission results are shown in Section V. Finally,

the conclusions are given in Section VI.

II. CODING AND ERROR CONTROL IN ADSL SYSTEMS

A. Frame Structure

ADSL uses a superframe structure composed of 68 data

frames (0 to 67) and a synchronization symbol, which are

encoded and modulated into a DMT symbol [4]. The duration

of an ADSL superframe is 17 ms. Each frame is composed of a

fast data buffer and an interleaver data buffer. The difference

between the two buffers is that in the interleaver data bufferthere is a convolutional interleaver that protects against burst

errors, as depicted in the block diagram in Fig. 1.

Two cyclic redundancy checks (CRC) (crcf for the fast data

buffer, and crci for the interleaver data buffer) are generated

for each frame. CRC [6] is a coding technique used for error

detection only. In CRC coding, the parity bytes or redundancy

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Fig. 1. Block diagram of ATU-C transmitter.

are added to the message to be sent to ensure that errors can

be detected in decoding. G(X) = X8 +X4 +X3 +X2 + 1is the generator polynomial specified for ADSL for the CRC

calculation, which uses 8 bits.

After CRC, scrambling and FEC coding are applied to the

contents of each buffer separately. Scramblers are used to

randomize bit streams. Their goal is to prevent the trans-

mission of repeated bits streams, as long sequences of 0s

or 1s. The scrambling is done bitwise using shift registers

and a predefined primitive polynomial, defined in ADSL as:

H(X) = 1 +X18 +X23. The next section briefly discusses

FEC.

B. Forward Error Correction

In ADSL, the Forward Error Correction (FEC) block uses

Reed-Solomon (RS) [7] codes for detecting and correcting

errors in a transmission. Reed-Solomon are cyclic non-binary

codes that operate on multiple bits over a finite field, called the

GF (2m) Galois field, where all the arithmetic is performed.

In ADSL, the number of bits is m = 8, so the arithmetic is

performed in a GF (28) = GF (256) Galois field generated by

the following primitive polynomial: p(X) = X8+X4+X3+X2 + 1. Also, the codewords in RS codes are represented in

the form of polynomials.

RS codes are systematic codes specified as RS(N,K),where N is defined as the total number of coded bytes in

a codeword and K is the number of information bytes to be

encoded. The difference R = (N−K) is the number of parity

bytes that must be added to the K bytes for error correction, as

shown in Fig.2. The correction capability of an RS code, i.e.,the number of errors that can be corrected by each codeword,

is called t and defined as: t = N−K2 [8].

Fig. 2. RS codeword.

According to [4], during encoding the R redundant bytes

are calculated using:

R(X) = M(X)XR mod G(X), (1)

where the redundancy R(X) is obtained as the remainder of

the polynomial division of the message M(X) and a generator

polynomial defined as G(X) =∏R−1

i=0 (X + βi).During decoding, the RS codes use the algorithm of

Berlekamp-Massey [9] to recover the transmitted codeword.

C. Interleaver

Interleaving is a technique used to protect data against burst

errors. In ADSL, the errors caused by impulsive noise are

concentrated in bursts, whose duration may cause the number

of erroneous bytes in a codeword to be much larger than

the correction capability of the RS code. The combination

of an interleaver with Reed-Solomon raises the possibility of

correcting those errors, as the interleaver rearranges the bytes

of the codewords in a different order, spreading the erroneous

bytes among different codewords [10].

ADSL adopts a convolutional interleaver consisting of a set

of shift registers, each with a fixed delay. Each of the N bytes

of a codeword are delayed by an amount that varies linearly

with the byte index i = 0, 1, 2, ..., N − 1. More precisely, the

bytes are delayed by (D− 1)× i bytes, where D is called the

interleaver depth.

At the receiver, the deinterleaver performs the reverse pro-

cess, recovering the byte order of the codeword.

III. DISCRETE MULTITONE MODULATION

ADSL uses the Discrete Multitone Modulation (DMT)

technique, in which the frequency spectrum is divided into

several subchannels (tones). The number of allocated bits in

each subchannel is based on their SNR, supporting a maximum

of 15 bits [1].

A very simplified description of the ADSL’s transmission

follows: The bit stream is sent to the tone ordering block,

responsible for classifying the tones and then allocate the

bits accordingly. Next, the Quadrature Amplitude Modulation

(QAM) Modulator maps the bit strings obtained from the tone

ordering block to symbols in a QAM constellation. These

symbols are represented by complex numbers, which are then

converted to a real signal in time domain using an Inverse

Fast Fourier Transformation (IFFT). Hermitian symmetry is

enforced prior to the IFFT operation to ensure a real signal is

generated. A cyclic prefix is also added for protection against

intercarrier and intersymbol interference.

At the receiver, a Time Domain Equalizer (TEQ) is applied

to the digitalized received signal to ensure the channel is

shorter than the cyclic prefix length. Then the samples pass

through the Fast Fourier Transformation (FFT). At the output

of the FFT, a Frequency Domain Equalizer (FEQ) is applied

in order to compensate for the amplitude and phase distortions

caused by the channel. After FEQ, the bits pass through the

QAM demodulator to be recovered [10].

IV. SOFTWARE MODEM ADSL DESIGN USING PTOLEMY II

Ptolemy II is the software used by Ptolemy project [11],

a research group that studies heterogeneous modeling, sim-

ulation and design of systems, especially embedded ones.

Ptolemy II is a open-source framework implemented in Java

that uses a actor-orientated, hierarchically heterogeneous ap-

proach to compose different models, dividing a complex model

in a tree of submodels and combining a large variety of models

of computation in a friendly interface, making system design

easier to understand, analyze and optimize.

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In Ptolemy II, models are built graphically using an in-

terface called Vergil. These models are characterized by an

interconnection of actors (software components) that run and

communicate through ports. Each actor represents a function

or set of functions that receives information from an input port

and produces a result in an output port. The actors run through

a Director that determines how these components should be

scheduled for execution. Moreover, the Directors implement

the models of computation based on domains. For this work,

the Synchronous Data Flow (SDF) and Finite State Machine

(FSM) domains were used.

The blocks responsible for channel coding and modulation

of the ADSL software modem used during the Showtimephase have been implemented in Ptolemy II following the

specifications of [4]. Each block was created and implemented

in Java and then instantiated within Ptolemy II.

For example, take the implementation of the scramblerblock. First, the class Scrambler is created. It consists of

methods responsible for performing bit scrambling. Next, the

class ScramblerActor is created with the methods used by

Ptolemy to perform the actions. Finally, the class Scram-blerActor aggregates the class Scrambler, so it can then

perform the operations of the scrambler. Fig. 3 shows all the

transmitter and receiver blocks implemented in Ptolemy. Only

the interleaved path is used because it is more suitable for

video transmission.

Fig. 3. Block diagram of the ADSL software modem implemented inPtolemy.

A. TraceSpan Validation

To validate the ADSL software modem implemented here,

i.e., to ensure that its specifications and operation are consis-

tent with [4], the TraceSpan analyzer [12] was used. It is a

tool that provides an accurate analysis of the information from

the physical layer.

For the validation, a real ADSL transmission is recorded,

generating binary files that are used to test each block indi-

vidually, comparing the data obtained by TraceSpan with the

data obtained by the software modem for a given input. All

the receiver blocks listed in Fig. 3 were successfully tested.

B. Impulsive Noise Model Implementation

The impulsive noise model adopted in this paper follows

the description in [13], [14], based on measurements made

by British Telecom (BT), Deutsche Telekom (DT) and Uni-

versity of Edinburg to simulate realistic impulsive noise. The

impulsive noise model, in time domain, is characterized by

the amplitude and impulse duration and the interval between

impulses called inter-arrival times. These characteristics are

model statistically.

1) Impulse Amplitude: The impulse amplitudes are mod-

eled by the following Weibull probability density func-

tion [14]:

P (y) =1

2αb|y|α−1e−b|y|α , (2)

where α = 0.263 and b = 4.77 are parameters adopted by BT.

2) Impulse Duration: The impulse durations are modeled

by a sum of two log-normal components, as proposed in [15]:

fl(t) = B1√

2πs1te−(1/s21)ln2(t/t1)

+(1−B)1√

2πs2te−(1/s22)ln2(t/t2) (3)

where t is the impulse duration and B = 0.45, s1 = 1.25,

t1 = 1.3μ, s2 = 21.5 e t2 = 129μ are parameters.

3) Inter-arrival times: Inter-arrival times present a complex

statistical behavior and can be modeled by a Markov process

with N states. A model with two states was proposed in [13]

for simulation purposes. This model defines two states, one

with inter-arrival times below 1 ms and another with intervals

greater than 1 ms. However, for the purpose of comparison,

the impulsive noises were set to occur at regular intervals,

which means that the inter-arrival times were set on a fixed

value, as mentioned in [2].

The impulsive noise model in Ptolemy was created with 2

finite state machines using the FSM domain. FSM 1 is a 2-state

machine that switches between a noisy and a noiseless state.

The duration of the noisy state is set according to (3) and the

noiseless state has a fixed duration, as previously mentioned.

The FSM 2 is also a 2-state machine responsible for creating

the impulsive noise itself. When the state in the FSM 1 is

noiseless, FSM 2 sends what is in its input port directly to

its output port. However, when the state of FSM 1 is noisy,

FSM 2 changes to a state where impulsive noise samples are

generated according to (2) and then added to its input signal,

until FSM 1 changes back to the noiseless state again.

V. SIMULATION RESULTS

This section presents the results of the simulations using the

ADSL software modem presented in this work using video

transmission in the presence of impulsive noise. Then, an

analysis of the video quality for different values of interleaver

depth is performed. Fig. 4 shows the Ptolemy model used

for the simulation of video transmission with the transmitter

and receiver blocks, considering only the downstream direction

and the interleaver path. The channel was assumed ideal, and

only impulsive noise is added. The parameters used in both

transmitter and receiver blocks were those recommended in

[4] and listed in Table I. In the simulations, the number of

parity bytes was set to the largest correction capacity allowed

in ADSL (16 bytes per codeword), i.e., the RS code used was

the RS(255, 239).

The simulations were performed using two types of MPEG-

4 video stream with 1.5 Mbps, 300 frames and 12 s: news.mp4

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TABLE IPARAMETERS USED BY THE TRANSMITTER AND RECEIVER BLOCKS.

Parameters ValuesInformation frame size (K) 239 bytesSuperframe size 68 framesCodeword size (with parity) (N) 255 bytesParity bytes (R) 16 bytesNumber of DMT symbols per codeword 1Interleaver depth (D) 2, 4, 8, 16, 32, 64Number of tones 256Number of FFT / IFFT points 512Cyclic Prefix 32 samples

(consisting of a low-activity video, with small movements)

and soccer.mp4 (consisting of fast motion video, characterized

by a sequence moving uniformly). Two metrics were used to

assess video quality. The Peak Signal-to-Noise Ratio (PSNR)

is an objective metric calculated as the difference between

each pixel of the original frame with each pixel of the

distorted frame. As a subjective methodology for video quality

evaluation, it was used the Double Stimulus Impairment Scale

(DSIS) [16], where viewers are shown multiple sequence pairs

consisting of a “reference” and a “test” sequence and rates

the overall amount of impairment in the test sequence on a

discrete five-level scale ranging from “imperceptible” (5) to

“very annoying” (1). Fifteen viewers were used in the tests.

Fig. 4. Ptolemy model for video transmission in the presence of impulsivenoise.

Table II shows the simulation results consisting in a scenario

with an ideal channel, and fixed inter-arrival time of 3 ms,

using the video news. Five simulation were performed for

each value of D and also for cases without interleaver and

without coding. For each simulation, the average video PSNR

was calculated. The table also shows the corresponding DSIS

results.

It can be observed from Table II that the PSNRs for D from

2 to 16 showed values close to each other, and the videos

feature practically the same quality. Only from D = 32 and

up the PSNR increases considerably and the video quality is

considered good. For D = 64, the PSNR is maximum, i.e., it

was possible to correct all errors and fully recover the video.

This behavior can be explained by analyzing the impact of

the interleaver (as in [17]). Up to D = 16 the errors are just

being spread over more codewords, without being corrected

yet. This occurs because the error spreading is not enough for

TABLE IIAVERAGE PSNRS OF THE news VIDEO FOR DIFFERENT INTERLEAVER

DEPTHS VALUES, CONSIDERING THE 3 MS INTER-ARRIVAL TIME.

Interleaver depth (D) Average PSNR DSIS64 100 5 (Excellent)32 35,75 4 (Good)16 27,25 3 (Fair)8 27,30 3 (Fair)4 27,22 3 (Fair)2 26,05 3 (Fair)No Interlaver 24,57 2 (Poor)No Coding 24,28 2 (Poor)

the FEC to correct, i.e., the number of errors in the codewords

is still greater than the correction capability of the RS code,

so the errors can not be corrected. On the other hand, from

D = 32 the error spreading begins to be enough for the FEC

code to correct and, therefore, the quality and PSNR of the

videos increase. This effect can be better viewed in Fig. 5. It

shows the number of frame errors and PSNR as a function

of the interleaver depth. Up to D = 16 the number of frame

errors increases. Then, from D = 32, the frame errors starts

to decrease and ends at zero for D = 64. The PSNR remains

fairly stable for D values up to 16 and slightly below for

the case without interleaver (D = 0). From D = 32, the

PSNR values increase, showing that the frames are now being

corrected.

0 2 4 8 16 32 64 700

50

100

150

200

250

300300

Interleaver Depth (D)

Num

ber

of F

ram

e E

rror

s

0 2 4 8 16 32 64 700

20

40

60

80

100

110110

PS

NR

(dB

)

Frames

PSNR

Fig. 5. Number of frame errors and PSNR as a function of the interleaverdepth.

Fig. 6 shows some video frames from news with different

amounts of error due to the presence of impulsive noise for

one of the simulations, indicating the PSNRs calculated for

each frame individually.

Table III shows the results for the simulation using the

soccer video, using the same scenario of the news video.

In this case, it is observed that the PSNRs follow the same

behavior of the news video. However, comparing Table III

with Table II, it is observed that the absolute values of the

PSNRs for the soccer video are lower than those for the newsvideo, as well as the qualities observed. This happens because

the soccer video has more movement, resulting in more errors

in the images. In this case, it was not possible to correct all

errors of the video even for D = 64 in some simulations.

Fig. 7 shows some video frames from soccer with different

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(a) D = 64, PSNR = 100. (b) D = 32, PSNR = 24,26.

(c) D = 8, PSNR = 19,69. (d) No cod., PSNR = 13,27.

Fig. 6. News video frames with different amounts of error due to the presenceof impulsive noise.

TABLE IIIAVERAGE PSNRS OF THE SOCCER VIDEO FOR DIFFERENT INTERLEAVER

DEPTHS VALUES, CONSIDERING THE 3 MS INTER-ARRIVAL TIME.

Interleaver depth (D) Average PSNR DSIS64 49,44 4 (Good)32 31,61 4 (Good)16 21,60 2 (Poor)8 19,85 2 (Poor)4 20,20 2 (Poor)2 19,35 2 (Poor)No Interleaver 19,59 1 (Bad)No Coding 19,11 1 (Bad)

amounts of error due to the presence of impulsive noise for

one of the performed simulations. It also shows the PSNRs

calculated for each frame individually.

(a) D = 64, PSNR = 22,51. (b) D = 32, PSNR = 20,08.

(c) D = 16, PSNR = 16,61. (d) No cod., PSNR = 13,17.

Fig. 7. Soccer video frames with different amounts of error due to thepresence of impulsive noise.

VI. CONCLUSIONS

The paper presented the analysis of the impact of impulsive

noise on the transmission of video over ADSL. To achieve this,

an ADSL software modem was developed using the Ptolemy II

framework. The software modem implements the modulation

and channel coding of an ADSL modem needed to simulate the

physical layer, as well as an impulsive noise channel model.

In the results, the video quality was analyzed for different

values of interleaver depth. The results have shown that for

both videos, news and soccer, for D values up to 16, the video

quality observed is practically the same, because the error

spreading is not enough to allow the FEC to work effectively.

However, from D = 32 and up, the spreading starts to be

enough, and the errors starts to be corrected by the FEC

block, resulting in better video qualities. The soccer video,

however, shown worse video qualities for the same values of Dcompared to the news video, due to the amount of movement

present in the video, resulting in more errors.

The developed software modem can be extended to support

other DSL standards, channel and noise models, allowing for

the simulation of different scenarios.

REFERENCES

[1] T. Starr, J. M. Cioffi, and P. J. Silverman, Understanding DigitalSubscriber Line Technology. Prentice-Hall, 1999.

[2] N. H. Nedev, “Analysis of the impact of impulse noise in digital sub-scriber line systems,” Ph.D. dissertation, The University of Edinburgh,Mar. 2003.

[3] ANSI, “T1.413 - Network to customer installation interface - Asymmet-ric Digital Subscriber Lines (ADSL) Metallic Interface,” 2004.

[4] ITU-T, “Asymmetric Digital Subscriber Line (ADSL) transceivers,” June1999.

[5] “ADSL transceiver design - UT Austin ADSL2 simulator.” [Online].Available: http://users.ece.utexas.edu/∼bevans/projects/adsl/index.html

[6] S. B. Wicker, Error Control Systems for Digital Communications andStorage. Prentice Hall, 1994.

[7] S. Lin and D. C. Jr., Error Control Coding: Fundamentals and Appli-cations. Prentice-Hall, 1983.

[8] B. Sklar, Digital communications: Fundamentals and applications,2nd ed. Prentice Hall, 2001.

[9] E. R. Berlekamp, “On decoding binary bose-chaudhuri-hocquenghemcodes,” IEEE Trans. Inf. Theory, vol. IT-11, pp. 577–580, October 1965.

[10] P. Golden, H. Dedieu, and K. Jacobsen, Fundamentals of DSL Technol-ogy. Auerbach Publications, Taylor & Francis Group, 2006.

[11] C. Hylands, E. Lee, J. Liu, X. Liu, S. Neuendorffer, Y. Xiong, Y. Zhao,and H. Zheng, “Overview of the Ptolemy Project,” University of Cali-fornia, Berkeley, Tech. Rep., July 2, 2003.

[12] TraceSpan, “DSL Xpert User’s Guide,” 2004. [Online]. Available:http://www.tracespan.com/

[13] S. McLaughlin, W. Henkel, R. Kirkby, and T. Kessler, “Text for realisticimpulsive noise model,” Submission to ETSI WG TM6, TD20, 011T20,February 2001.

[14] I. Mann, S. McLaughlin, W. Henkel, R. Kirkby, and T. Kessler, “ImpulseGeneration With Appropriate Amplitude, Length, Inter-Arrival, andSpectral Characteristics,” IEEE J. Select. Areas Commun., vol. 20, no. 5,pp. 901–912, June 2002.

[15] W. Henkel and T. Kessler, “A wideband impulsive noise survey in thegerman telephone network: statistical description and modelling,” AEU,vol. 48, pp. 277–288, November/December 1994.

[16] ITU-R BT.500, “Methodology for the subjective assessment of thequality of television pictures,” 2000.

[17] N. H. Nedev, S. McLaughlin, and D. I. Laurenson, “Estimating errors inxDSL due to impulse noise,” Int. Zurich Seminar on Communications(IZS), 2004.

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