10.1.1.113.5204

8/7/2019 10.1.1.113.5204

1/49

Unio Europeia Fundos Estruturais Governo da Repblica Portuguesa

PROJECTOS DE INVESTIGAO CIENTFICA E DESENVOLVIMENTO TECNOLGICO

RELATRIO DE PROGRESSO

Relatrio de Execuo MaterialRelatrio de Execuo Financeira

REFERNCIA DO PROJECTO N __POSC/EEA-CPS/59401/2004

RELATRIO REFERENTE AO ___2__ ANO DE EXECUO

8/7/2019 10.1.1.113.5204

2/49


Identificao da instituio proponente

Nome ou designao social Instituto de Engenharia de Sistemas e Computadores, Investigao e

Desenvolvimento em Lisboa (INESC-ID)

Morada R. Alves Redol, 9

LocalidadeLisboa Cdigo postal 1000-029

Telefone 213100300 Fax 213145843 Email [email protected]

Unidade responsvel pela execuo do projecto

Nome Sistemas de Processamento de Sinal

Morada R. Alves Redol, 9

LocalidadeLisboa Cdigo postal 1000-029


Identificao do investigador responsvel

Nome Jos Antnio Beltran Gerald


Data de Entrada_____________________ Data de Verificao__________________N de Registo ______________________ Assinatura ________________________

Espao reservado Fundao para a Cincia e a Tecnologia

Referncia do projecto: POSC/_EEA-CPS/_59401/_2004__

Ttulo do projecto: Sistema de Comunicao OFDM Adaptativo na Rede de

Distribuio de Energia Elctrica

Data de Incio do Projecto: __1__/___Abril______/__2005__

Durao: _24___ Meses

8/7/2019 10.1.1.113.5204

3/49


Instituies que participam no projecto(preencher s em caso de haver alteraes)

DESIGNAO

Instituio 1

Instituio 2

Instituio 3

Instituio 4

Equipa de investigao(preencher s em caso de haver alteraes)

NOME CARGO/FUNO TAREFAS %TEMPO

Esforo global do projecto, expresso na unidade pessoa*ms

(referente ao ____2____ ano de execuo)

Unidade: em nmero

Instituio Proponente 17,1

Instituio 1 5,2

Instituio 2

Instituio 3

Instituio 4

8/7/2019 10.1.1.113.5204

4/49


Resumo dos trabalhos desenvolvidos

No perodo aqui relatado, decorreram as Tarefa 1 - "Power Line ModelValidation" (de 1de Janeiro a 31 de Maro de 2006), Tarefa 2 - "Comparative studyof Coding Schemes and Digital Modulation Techniques" (de 1 de Janeiro a 31 deDezembro de 2006) e Tarefa 3 - "Adaptive Communication Techniques" (de 1 deJaneiro a 31 de Dezembro de 2006).

Os trabalhos visaram os objectivos das respectivas tarefas, especialmente a devalidao do modelo a adoptar para a linha de distribuio de energia, objectivo quese tem revelado bastante mais difcil de concretizar devido ao complexo hardwarenecessrio. Tambm, foi continuada a realizao de um sistema de simulao emcomputador para estudo das tcnicas OFDM, necessrio execuo das restantes 2tarefas.

Tarefa 1:No referente primeira tarefa, foi realizado um novo modem PLC em

hardware para acoplamento linha de distribuio de energia elctrica. Este novomodem-prottipo tem a vantagem, sobre o circuito inicialmente utilizado, de sermodular, permitindo uma relativa independncia entre os mdulos Interface deLinha (AFE- Analog Front End), Processamento de Sinal (DPB - Data ProcessingBoard) e Fontes de Alimentao (PSB - Power Supply Board) (o que se requer numcircuito para desenvolvimento e teste de solues), para alm do modem poder serligado a um utilizador externo via USB. Foram realizadas experincias com a linhade 220 V. As experincias realizadas no foram contudo suficientes paracaracterizao completa da linha. Desta forma, esta tarefa no est completamenteencerrada. Dos resultados obtidos nesta tarefa foi submetida uma comunicao emconferncia internacional (ISCAS'07).

Tarefa 2:No referente Tarefa 2 (esta tarefa deveria ter terminado a 30 de Setembro de

2006 mas ainda continua, em parte devido interrupo que ocorreu na bolsa deiniciao investigao, por desistncia do primeiro bolseiro), foi continuado a serdesenvolvido um sistema base de simulao em computador (utilizando o programaMatlab com Simulink) de comunicao na linha de distribuio de energia elctricausando OFDM, tendo sido acrescentado uma parte de recuperao de sincronismono receptor. Tambm, foi recentemente acrescentada uma parte do simuladorcorrespondente codificao do sinal OFDM, nomeadamente no que se refere utilizao de Turbo Codes ou Low-Density Parity-Check Codes. Foi assimcontinuado o desenvolvimento da aplicao computacional.

Tarefa 3:No referente Tarefa 3, foi continuado o desenvolvimento de tcnicas

adaptativas para melhorar a comunicao com OFDM. Foram desenvolvidos novosalgoritmos adaptativos (o Kalman LMS e suas simplificaes) e um novoequalizador adaptativo com cancelamento de rudo cruzado entre as sub-bandas deOFDM. Dos resultados obtidos nesta tarefa foram submetidas 2 comunicaes emconferncias internacionais (ICASSP'07 e ISCAS'07).

8/7/2019 10.1.1.113.5204

5/49


Indicadores de realizao fsica

(Referente ao _2____ ano de execuo)

Unidade: em nmero

A- Publicaes

Livros

Artigos em revistas internacionais

Artigos em revistas nacionais

B- Comunicaes

Em congressos cientficos internacionais 2

Em congressos cientficos nacionais

C- Relatrios

D- Organizao de seminrios e conferncias 1

E- Formao Avanada

Teses de Doutoramento

Teses de Mestrado

Outra

F- Modelos

G- Aplicaes computacionais 1

H- Instalaes Piloto

I- Prottipos laboratoriais 1

J- Patentes

L- Outros (discriminar) Relatrio de Bolsa de Investigao

8/7/2019 10.1.1.113.5204

6/49


Publicaes(listar as publicaes com origem no projecto)

PUBLICAES[1] PAULO LOPES, GONALO TAVARES, JOS GERALD, A New Type ofNormalized LMS Algorithm Based on the Kalman Filter, submitted to ICASSP07.[2] PAULO LOPES, JOS GERALD, New Normalized LMS Algorithms Basedon the Kalman Filter, submitted to ISCAS07.[3] PAULO LOPES, Survey of Adaptive OFDM and Application to the PowerLine Channel, INESC-ID Seminar, October 2006.[4] ANTNIO NUNES, Power Line Communication System using AdaptiveOFDM",Relatrio da parte realizada da bolsa de iniciao investigao cientficano mbito do projecto POS_C/EEA-CPS/59401/2004 Power Line CommunicationSystem using Adaptive OFDM, Dezembro de 2006.

8/7/2019 10.1.1.113.5204

7/49


RELATRIO DE EXECUO MATERIAL

(incluir o relatrio de execuo material elaborado de acordo com as normas)

Authors:Jos A. B. Gerald

Gonalo N. G. TavaresLuis Miguel G. Tavares

Paulo A. C. LopesJos Vaz

8/7/2019 10.1.1.113.5204

8/49


Relatrio de Execuo Material( in english)

Objectives (as stated in the proposal):

The main objective of this research project is the study and simulation of adigital communication system over power lines using OFDM-like multicarriertechnology, and operating with data-rates above 1 Mbps.

The tasks to be performed in this project will lead to a deep understanding ofthe power line transmission medium. The characterization of the transmissionmedium will also provide a mathematical model for the communication channel.

The identification of digital modulation schemes for OFDM subcarriermodulation, which best suits the specific problems in PLC is also one of the keyobjective of this project.

Another objective is the development of new OFDM coding techniques that willeffectively mitigate the adverse effect of the channel. These techniques will allowreliable transmission even in the presence of deep spectral nulls in the channel

transfer function and will provide a blind channel identification algorithm.The development of a custom, user-friendly and versatile software simulation

tool, specific tailored to the PLC environment, is also an important goal of thisproject.

Task 1 - Power Line Model Validation (01-04-2005 to 31-03-2006)

To find the theoretical models that best fit the experimental results already

available by the project team and some yet to be obtained.

Results at month 12:

The work in this task began by implementing a hardware system to interface

with the power lines. The system was implemented (almost all) and experimentalresults were obtained. Results obtained till month 9 were already presented in 1st yearreport. Next a new PLC modem development and new results obtained with thisimproved version are presented.

1.1 PLC modem Version IIIn this work we improved the PLC Modem for domestic communication,

including software for easy handling, using the adequate modulation for data powerline communication.

The idea of a PLC communication is to add a broadband signal to the 50 Hzsignal of the power line. This modulated signal does not affect in any way the normal

8/7/2019 10.1.1.113.5204

9/49


work of the domestic electric/electronic devices or industrial machines since thesignal power is insignificant.

The normal indoor power line layout, with its several plugs and domesticdevices that may be connected to them, cause a lot of interference in thiscommunication channel. Therefore, the use of a modulation which manages the noiseelimination in an effective manner is required. For this purpose we tried to implement

an adequate modulation such as OFDM. Unfortunately, there was no time to obtainfull experimental results with OFDM and the power line. The work towards the finalPLC modem prototype still goes on.

The previous PLC modem circuitry already performs the 220 V networkcoupling and A/D and D/A transmitted data conversion. The expected channelbandwidth goes from 1 MHz to 20 MHz. The bottom frequency is due to thegenerated noise by other appliances, which is not filtered by any kind of device in thenetwork, and the top frequency is due to either the electric network frequencyresponse or the transformer bandwidth.

The present work consists in implementing the modem control system andOFDM modulator/demodulator and a Universal Serial Bus (USB) interface with the

user. For simplicity it will be used USB 1.1 specification. The transmission rateshould be no less than 1 Mbps. The emitted OFDM signal center frequency of 4 MHzwas chosen after some experiences with the AFE. Some attention was paid to theemitted signal power: It does not go over 30 V/m in a 30 m distance above 3.5 MHz,which correspond to a -86dBW (-56dBm) level (according to the US FCC Part 15standards, also used in Europe) [1].

For data processing implementation it was chosen a Field-Programmable GateArray (FPGA) instead of a Reduced Instruction Set (RISC) processor, because theformer is faster. The chosen FPGA was considered for its capability of executingFFT/IFFT very fast. Fig. 1.1 shows the PLC modem architecture.

Fig. 1.1 PLC Modem architecture simplified diagram.

Along with the required modem circuitry, other facilities were implementedwith the goal of creating a "demoboard", suitable for testing other modemalternatives. So, this FPGA has in fact more outputs then those strictly needed, and

Modem

USB

FPGA

Memory

AFE

Power

Extra: communication plugs and buttons

8/7/2019 10.1.1.113.5204

10/49


the board has a Series protocol DB9 connector and a parallel protocol DB25connector. The FPGA is connected to a 4 Mb Xilinx memory through a JTAGconnection in order to keep its firmware. This data processing unit is dedicated tomodulate/demodulate the data and connect to the user by means of an 8-bit USBcircuit (for its simplicity). With the implemented circuit the transmission rate is up to8 Mbps.

Fig. 1.2 shows the signal path (AFE excluded) in the considered OFDM PLCtransmission. Except for the Line path, all the signal processing is performed in theFPGA board.

Fig. 1.2 Signal path in the OFDM PLC transmission.

Next the modem main units are presented, i.e., the Analog Front End (AFE),the Data Processing Board (DPB), and the Power Supply Board (PSB).

A)Analog Front End (AFE)

Starting with the AFE Board, which connects to the Data Processing Boardthrough a 26 pin socket and flat cable. The AFE block diagram and AFE board areshown in Fig. 1.3 and Fig. 1.4, respectively. The AFE is composed by:

Analog-to-Digital Converter (ADC)

Digital-to-Analog Converter (DAC)

Lowpass Filters

Automatic Gain Control (AGC)

Bandpass Fiters

Line Drivers

Coupling Circuitry

Series/Parallel QPSKModulation

IFFT Parallel/Series

CarrierMultiplication

NoiseEchos

CarrierMultiplication

Series/Parallel DemodulationFFT Parallel/Series

Input

Output

Line = Unknown channel

8/7/2019 10.1.1.113.5204

11/49


Fig. 1.3 AFE block diagram.

Fig. 1.4 AFE board.

As shown in Fig. 1.3, the 26-pin connector is used for a 10-bit data bi-directional bus, a clock signal, an emission signaling bit (connected to a LED), areception signaling bit (connected to a LED) and 3 gain control signal bits for theAGC. All communication between the AFE and the Data Processing Board issynchronized by the FPGA, with the help of a crystal oscillator implemented in theData Processing Board.

I/OGate

(26pins)

ADC

10bit

AGC

Driver

BPF

Transformer

LPFDAC

Connector (26 pins)DAC

Output stageTransistors

50Hz FilteringCapacitors

Transformer1:1

AGCFilters ICs

8/7/2019 10.1.1.113.5204

12/49


B) Data Processing Board (DPB)

In the Data Processing Board one can find the following components:

FPGA Xilinx Spartan 3 XC3S1000

Xilinx XC18v04 memory

120MHz oscillatorUSB circuit FTDI FT245BM

Microchip memory 93LC46

6 MHz crystal for the USB FTDI

Transceiver RS232 MAXIM M3386E and DB9 connector for seriesinterface

Transceiver Philips 74ALVC16245 and DB25 connector for parallelinterface

8 LEDs (red)

8 Dipswitches

5 pressure buttonsXilinx advises the XC18v04 memory for programming the 1000 kgates FPGA.

Pressure buttons serve for FPGA testing, by choosing input debug bits, as well as thered LEDs connected to the FPGA outputs. The parallel connection may serve for slowcommunication (in both directions) using the transceiver direction control bit.

Fig. 1.5 shows the Data Processing Board.

Fig. 1.5 Data Processing Board of the PLC modem.

RS232

USB

JTAG MemoryPressureButtons

FPGA

Dipswitches Leds

Oscillator

AFEConnector

Transceiver

Parallel Connector

8/7/2019 10.1.1.113.5204

13/49


The FPGA is the DPB main processing unit, and it interacts with all the circuitsaround. Fig. 1.6 illustrates the FPGA connections. One can see in Fig. 1.6 that there isno direct communication among the surrounding circuits. All communication isestablished through the FPGA. The communication between the digital gates and theAFE is controlled by the FPGA firmware. The 120 MHz signal from the crystaloscillator is used as clock, for instance for the FPGA and for the AFE converters. The

only exception is the USB circuit, which has its own clock.Note that the flash memory connected to the FPGA, where its firmware is

stored, can also be used for storing program data, depending on the implementedprogram.

Figura 1.6 FPGA connections block diagram.

Table 1.1 illustrates the connection among all the ICs' pins.

The USB circuit was implemented with the FTDI FT245BM circuit. The USBunit schematic and functional blocks can be seen in Fig. 1.7 and Fig. 1.8, respectively.It has an 8-bit input/output bus and several control bits. Fig. 1.9 and Fig. 1.10 showthe connections between the other circuits of the USB unit.

The series connection is implemented by the Maxim MAX3386E transceiverand the 9-pin DB9 socket, which connect to the input and output transmission signalpins. It were used the T3 and R1 signal lines (which correspond to pins 9-15 and 11-14, respectively).

FPGA

USB

Parallel Connector

JTAGEEPROM

Clock

AFEConnector(26 Pins)

SeriesConnector

8/7/2019 10.1.1.113.5204

14/49


TransceiverUSB

FPGA PressureButtons

FPGA LEDs FPGA

D0 A4 S1 T5 LD1 J16

D1 A3 S2 R4 LD2 K15

D2 B1 S3 T4 LD3 K16D3 C1 S4 R3 LD4 L15

D4 D1 S5 T3 LD5 M16

D5 E1 Dipswitches LD6 N16

D6 G1 DS0 R6 LD7 P15

D7 H1 DS1 T7 LD8 P16

RD# J1 DS2 R7 RS232

WR K1 DS3 T8 TX A5

TXE# M1 DS4 T9 RX A7

RXF# N1 DS5 R9SI/WU P1 DS6 T10

PWREN# R1 DS7 R10

ParallelConnector

FPGA AFE FPGA

D0 H16 D0 T12D1 G16 D1 R13D2 G15 D2 R11D3 E16 D3 N15D4 E15 D4 M15

D5 D16 D5 B14D6 D15 D6 B13D7 C16 D7 A14

D8 A13D9 A10

OSCILATOR A8 CLK B16TX_EN A12RX_EN A9

GAIN_A0 R16GAIN_A1 R12GAIN_A2 T14

Table 1.1 FPGA pin-out.

8/7/2019 10.1.1.113.5204

15/49


Fig. 1.7 Schematic

Figura 1.8 USBchip functional blocks

Fig. 1.9 Connection tothe USB chip memory

Fig. 1.10USB and Power socket connections

Fig. 1.11 shows the usual connection of the MAX3386E circuit, with the

required capacities for the RS-232 transmission line adaptation.

8/7/2019 10.1.1.113.5204

16/49


Fig. 1.11 RS232 transceiver connection.

C) Power Supply Board (PSB)

In order to make the PLC modem the most modular possible, the power supplycircuitry was implemented in a separate board. In this board all the required voltageswill be generated from the 220 V AC signal. The AFE board needs +5V, -5V, and+3,3V; the DPB needs +1,2V, +2,5V and +3,3V. First the 220V AC signal must beconverted in a DC low voltage one. This is accomplished with the help of atransformer plus a rectifier bridge and some capacitors, as shown in Fig. 1.12 (TP6and TP7 inputs are connected to the mains).

+ C102200uF

DGND

C1110uF

DGND

AC1AC1

V++

AC2AC2

V--

D1 BRIDGE

15

6710 9

T1TRANS5

+

C92200uF

DGND

C810uF

DGND

DGND

TP6MAIN1

TP7MAIN2

+9V-9V

Fig. 1.12AC/DC Converter circuit.

8/7/2019 10.1.1.113.5204

17/49


In order to generate the +5V and the -5V it were used the LM317 and LM337voltage regulators, respectively, as shown in Fig. 1.13.

R11240R

VDD_+5V

C21100nF

VDD_-5

R12240R

C22100nF

+ C19100uF

+ C20100uF

+ C2410uF

+ C2310uF

C25100nF

C26100nF

ADJ

1

IN3

OUT2

U6

LM317T

ADJ

1

Vin2

Vout3

U5

LM337T

DGND

R9240R

R10240R

+9V

-9V

Fig. 1.13 Power source (+5V, -5V) circuit.

The positive voltage circuit (+5 V) functioning is as follows: C19 and C21capacitors are used to eliminate some residual signal fluctuations. The LM317 circuithas a 1.25 V reference signal, which allows to obtain at its output a DC signal withamplitude given by

+=+

11

95 125.1

R

RV VDD

(1.1)

The negative voltage circuit has a similar functioning. Although the negativevoltage signal follows right after to the AFE board, the positive voltage signal is still

used as reference for the lower voltage regulators of this board.The 3.3V is obtained with the circuit shown in Fig. 1.14, which uses the

LT1761ES5-BYP IC. The 2.5V and 1.2V are obtained with similar circuitry.

+ C7

10uF

C210nF

R247K

DGND

R3

2K

C33.3uF

+ C410uF

C5100nF

R4

100R

C6100nF

C110uF

R12K7

62

3

4

7

U2

AD797ARIN1

GND

2

BYP3

ADJ

4

OUT5

U1LT1761ES5-BYP

L1

10uH 60MHz

DGND

T2

BD139

DGND

VDD_3V3

R21330R

DGND

TP1PROBE TEK DPO

+5V

Fig. 1.14 Power source (+3.3V) circuit.

Once again the output voltage for the 3.3V circuit is given by

8/7/2019 10.1.1.113.5204

18/49


+=

21

166.033R

RV VDD

(1.2)

It was necessary to use a feedback architecture for these power supplies

because the maximum output current for the regulators is about 100 mA, which has

revealed not to be enough for the AFE and the DPB all together.To note that the R21 resistor is extremely important, because the regulator

must have always some output current to ground. Also, R3, C3, C4 and C5 serve forfiltering purpose only, attenuating the high-frequency components at the amplifierinput. This amplifier has a very low output noise (0.9nV/Hz) and low distortion (-120 dB of THD at 20 kHz), and its purpose is only to function as a voltage follower,being the BD139 transistor the one responsible for all the output current required.

The 1.2V circuit was implemented with the TPS72201 circuit from TexasInstruments.

Fig. 1.15 shows the Power Supply Board with its main blocks identification.

Fig. 1.15 Power Supply Board.

Experimental Results

The required VHDL code for implementing the OFDM data processing andAFE control was introduced in the FPGA. Some experimental results were obtainedin order to confirm the modem performance.

Rectifier bridge

+5V and -5VRegulators

Other VoltageRegulators

OutputTransistors

Transformer

8/7/2019 10.1.1.113.5204

19/49


The transmitted OFDM signal in time can be observed in Fig. 1.16. In thisfigure one can differentiate the OFDM amplitude signal variation, which is typical inthis type of modulation. A detail of this figure can be observed in Fig. 1.17, where onecan better see the signal transitions. The OFDM signal is centered at 3.7675 MHz, asexpected, as can be confirmed in Fig. 1. 18. This figure shows the OFDM transmittedsignal spectrum.

Fig. 1.16 OFDM transmitted signal in time.

Fig. 1.17 Detail of the OFDM transmitted signal in time.

8/7/2019 10.1.1.113.5204

20/49


Fig. 1.18 OFDM transmitted signal spectrum

In these experiences the carrier had peak-to-peak maximum amplitude of 16

mV and the modulating signal had peak-to-peak maximum amplitude of 8 mV. Onecan also observe that the SNR at the Channel is high (although it was not measured).

Other experiences were made with the test pressure buttons and the red LEDs,which confirmed the DPB good functioning, especially in what concerned the USBcircuitry.

Next, some relevant results from the ModelSim compilation report obtained inthe FPGA programming task are presented in Table 1.2. In those results one can seethe Spartan 3S1000 ft256 FPGA is far from being full, remaining much space formore data processing implementation. For instance, with 64-points IFFT and no USBcommunication, the occupied portion rounds the 15% of its full capacity. Althoughnot yet accomplished, we believe that there is enough capacity in the FPGA forimplementing the full emitter/receiver plus the USB communication.

Timing Summary:Speed Grade: -5

Minimum period: 9.593ns (Maximum Frequency: 104.239MHz)

Device utilization summary:

Selected Device : 3s1000ft256-5

Number of Slices: 555 out of 7680 7%

8/7/2019 10.1.1.113.5204

21/49


Number of Slice Flip Flops: 809 out of 15360 5%

Number of 4 input LUTs: 942 out of 15360 6%

Number used as logic: 864

Number used as Shift registers: 78

Number of bonded IOBs: 27 out of 173 15%

IOB Flip Flops: 1

Number of BRAMs: 3 out of 24 12%Number of MULT18X18s: 5 out of 24 20%

Number of GCLKs: 2 out of 8 25%

Table 1.2 - ModelSim report detail for the FPGA programming.

Fig. 1.19 shows the full PLC modem, already assembled in 3 boards.

Fig. 1.19 Full Modem

1.2. Line Model

A general description and characterization of the power line communications

(PLC) channel is not feasible due to the variety of lines and cable types presented in ahome network.Despite this general agreed difficulty, several studies have attempted to achieve

a simulation model characterization of the power line. The main parameters affectingthe power line characterization are the channel impedance, signal attenuation andinterferences and noise.

Channel Impedance varies from place to place due to lines and cablecharacteristics and also the network topology, as also with the loads connected at each

Data Processing Board

Connection plug

Power Supply Board

AFE BoardPlug for 230V 50Hz

8/7/2019 10.1.1.113.5204

22/49


time to the network. This issue causes multipath signal propagation, thus degradingcommunication performance.

The Signal Attenuation is caused by the signal loss along the channel, increasingwith the frequency and distance between devices connected. Interferences and Noise,having different causes whether in an indoor or outdoor environment. For indoorcommunications, the main causes for interference and noise are the

equipments/appliances connected to the power line.It is commonly accepted that five classes of noise are present in the power line:

colored background noise; narrow-band noise; periodic impulsive noise asynchronousto the mains frequency; periodic impulsive noise synchronous to the mains frequencyand asynchronous impulsive noise. Although natural the appearance of any of theabove classes of noise, asynchronous impulsive noise raises from the switching on andoff of appliances connected to the network, causing severe system degradation.

The PLC channel model is far from being standardized, and there is no widelyaccepted model as is the case with other communications environments, namelytelephone or mobile channels. Studies on this issue are based on measurements takendirectly from the network, or using the knowledge of topology and characteristics ofthe network elements (wiring, cable type, distances, loads). These models will have aspecific scope, and are valid for the local network being used.

Models based on topology knowledge are available in the literature, e.g. [2-5]but are considered as unfeasible to use in a general context due to the inherent need ofdetermining all the network parameters.

A power line model of the transfer function, based on extended measures wasgiven by Zimmermann and Dostert [6] in 2002, using multipath signal propagation,with several paths and different delays and attenuations. The model is given as:

( )

( )0 1 2

1

. .k

i i

Na a f d j f

ii attenuation delayweighting

factor factor factor

H f g e e +

=

= (1.3)

Where N is the number of paths between emitter and receiver; i is the pathnumber; a0 and a1 are attenuation parameters; k is factor typically varying between 0.5and 1; gi is the weighting factor for path i; di is the length of path i; and i is the delayfor path I ( /i i pd v= , di is the length of path I and vp is the signal propagationvelocity).

The above model is obtained by analyzing a multipath signal propagation withjust one tap on the line, and then combined by superposition for the complete network.

The attenuation factor given was obtained as a simplification of the initial formgiven in [6] by extensive analysis of real measured attenuation results.

Comparison results for this model where given in [6] and, although thecomplexity inherent to the model is high (namely the numerous parameters needed to

estimate for an accurate matching of the channel model), it gives accurate results for alarge signal bandwidth. Fig. 1.20 shows the results for a measurement in a 110 meterlink containing 6 branches of about 15 meters, together with the simulated model(N=44 in this case):

Although, for practical use, the model complexity is very high, it can besimplified, reducing the total number of branches (N) still giving good results. In thiscase, as N is reduced, the model will start to give poor agreement on the deep notchespresent in the transfer function (see Fig. 1.21).

8/7/2019 10.1.1.113.5204

23/49


Fig. 1.20 Model with 44 paths. (a) Amplitude response. (b) Phase details.

(c) Impulse response. (From [6]).

Fig. 1.21 Model with 15 paths. (a) Amplitude response. (b) Phase details.

(c) Impulse response. (From [6]).

8/7/2019 10.1.1.113.5204

24/49


Task 1 References

[1] AMERICAN RADIO RELAY LEAGUE, ARRL, the National Association forAmateur Radio(http://www.arrl.org)

[2] J. BARNES, A physical multi-path model for power distribution networkpropagation, in Proc. 1998 Int. Symp. Powerline Communications and itsApplications, Tokyo, Japan, Mar. 1998.

[3] A. DALBY, Signal transmission on powerlines Analysis of powerlinecircuits, in Proc. 1997 Int. Symp. Powerline Communications and itsApplications, Essen, Germany, April 1997.

[4] H MENG, S. CHENG et al, Modeling of Transfer Characteristics for theBroadband Power Line Communication Channel, IEEE Transactions on PowerDelivery, Vol. 19, No. 3, July 2004.

[5] S. GALLI, T. C. BANWELL, A deterministic Frequency-Domain Model for theIndoor Power Line Transfer Function, IEEE Journal on Selected Areas inCommunications, Vol. 24, No. 7, July 2006.

[6] M. ZIMMERMAN, K. DOSTERT, A Multipath Model for the Powerline

Channel, IEEE Transactions on Communications, Vol. 50, No. 4, April 2002.

8/7/2019 10.1.1.113.5204

25/49


Task 2 Comparative Study of Coding Schemes and DigitalModulation Techniques (01-10-2005 to present)

To study and compare the different coding strategies that best suits the

communication over power lines.

Also, to compare the different digital modulation techniques that can be used tomodulate OFDM subcarriers.


2. Matlab Simulink Simulation of a MODEM for high speedPower Line Communications with Error Correction Codes

The proposed power line communication (PLC) system simulation developmenthas continued. Synchronization at the receiver and other main blocks (as for instanceother type of channels and new equalizer) were added. More recently, some blocks

concerning correction codes were implemented. This process is still in the beginning,and both Turbo codes and Low-Density Parity-Check (LDPC) codes are underevaluation.

Since the PLC channel is a very noisy environment (usually a mix oftime-varying erasure and fading channels), the usual trade-off in decrease the errorrate is to increase transmission power or reduce data rate. Since these two standardapproaches run against the original goal of low-complexity hardware and high datarates, another route must be taken.

Hence, in order to maintain data integrity across the PLC channel whileproviding high data rates, it is essential to consider the use of error-correction codingconstructs. [See appendix A2.1 for a comparison of relevant ECC methods]. Thecurrent (2006) most efficient ECC schemes used are Turbo Codes and Low Density

Parity Check codes (LDPC).The following ECC's are scheduled to be implemented and tested: Low Density

Parity Check codes (LDPC) and Turbo Codes. See Appendix A2.1 for a quickcomparison.

2.1. Linear Block CodesThe structure of a linear block code is completely described by the generator matrix G or the

parity check matrix H. The capacity of correcting symbol errors in a codeword is determined bythe minimum distance (dmin).

For a (7,4) Hamming Code with generator matrix H

H=[1 1 1 0 1 0 0

1 1 0 1 0 1 0

1 0 1 1 0 0 1]

dmin is the least number of columns in H that sum up to 0.

2.2 Low Density Parity Check

8/7/2019 10.1.1.113.5204

26/49


While LDPC [1], [2] and other error correcting codes cannot guarantee perfect

transmission, the probability of lost information can be made as small as desired. As of2001 codes have been built within 0.0045 dB of the Shannon Limit [3].

As with other linear codes, a LDPC code is completely described by it's thegenerator or parity check matrix, although LDPC matrices have special properties

such as H is sparse

Very few 1s in each row and column.Expected large minimum distance

Regular LDPC codesH contains exactly Wc 1s per column and exactly Wr=Wc(n=m) 1s

per row, where Wc m.

If the number of 1s per column or row is not constant, the code is an irregularLDPC code, which usually (with adequate construction) outperforms regularLDPC codes.

The encoding, as with other linear block codes, can be described by the

relationship C=XG. Unfortunately, there are some issues with this algebraicimplementation such as G being very large (10000,5000), and not sparse (as opposedto H). An alternative approach to simplified encoding is to design the LDPC code viagraph methods.

The general decoding of linear block codes follows the relationship CHT=0only ifC is a valid codeword. The used decode does not use this relationship, since itsfull implementation is very inefficient. A sum-product algorithm and message passingalgorithm are being evaluated for decoding purposes

2.3. Methodology

For modelling the full ECC system, an incremental design philosophy waschosen, and building upon previously achieved results. The following table shows thechannel and ECC combination matrix, and expected implementation complexity.

Channel ECC

Class Complexity Class Complexity

BER low regular LDPC low

AWGN low irregular LDPC high

BER+AWGN moderate turbo moderate

PLC moderate turbo + interleaver high

Table 2.1: List of expected channel and ECC's combinations (16) expected to be

tested.

Currently, the channels being modelled are BER and AWGN. The codes beingtested are regular LDPC, and irregular LDPC.

8/7/2019 10.1.1.113.5204

27/49


The current modelling architecture is shown in the next figure:

Fig. 2.1 Modeling architecture.The original data (source block) is error-encoded and sent through the channel.

After an iterative decoding, in order to correct transmission errors, the data isdelivered to the receiver. An observer module (ECC performance evaluation)monitors and signals error rates.

The Channel block can be any of the following:Average White Gaussian Noise (AWGN)

Binary Erasure (BERPower Line (PLC)any mix of the above channels

The ECC encoder/decoders can be either be of following codesLow Density Parity CheckTurbo Codes

Modulator and Demodulator currently implement Binary Phase Shift Key(BPSK), but future work will evaluate other modulation blocks.

2.4. Preliminary Results

The usual LDPC code is a regular one (i.e. all columns have the same weight).

It has been shown in [4] that irregular codes (with different weights per column) withcycles removed, outperform regular ones. Cycles are a pattern or sequence of zeros orones (in the parity check matrix) that repeats itself in different columns. The existenceof cycles degrades the performance of the code. The following figure shows theperformance difference of regular code and code with all cycles of size 4 removed.

8/7/2019 10.1.1.113.5204

28/49


Fig. 2.2 - BER for regular and 4-cycle removed.

2.5. Conclusions and Short Term Work

The current work stage, is focused in the topmost four tasks of table 2, whichmeans the current effort is centred in LDPC codes. Some preliminary results show theadequacy of LDPC codes as a effective choice for ECC.

As stated in the two previous sections, a more thorough comparison betweenTurbo Codes and LDPC parameters will have to be carried out.

The following are a list of the (expected) remaining tasks:

Tasks Reasoning

LDPC cycle removal Assure current algorithm for cycle removal is optimal

LDPC parameters Select appropriate LDPC parameters n, k, r for optimal code

performanceLDPC decoding algorithm Must selected between MAP and sum-product decoding

LDPC + channel modeling Must develop a larger test result bank, for AWGN and BERchannels

LDPC PLC channelmodeling

Full suite of results for evaluation LDPC as selected ECC

Turbo code encoder/decoder Create turbo encoder/decoder implementation

Modulation schemes Test the overall performance of the system, while usingmodulation schemes, other than BPSK

TC + PLC channel modeling Full suite of results for evaluation Turbo Codes as selectedECC

Final data analysis, and end report

Table 2.2 - List of expected tasks.

AppendicesA2.1 ECC methods and features quick reference

A2.1.1 Turbo Codes

Pros: close to approaching the Shannon limit well research (since 1993)

Cons: high encoding/decoding complexity patent encumbered

8/7/2019 10.1.1.113.5204

29/49


A2.1.1 LDPC

Pros: suitable parallel encoding/decoding linear decoding complexity in time lowest error rate floor (minimum distance is proportional to code

length) as close as desired to Shannon limit

Cons: cycles within the code internal structure degrade decoding performance

(see preliminary results) worst than turbo codes for short code block lengths

A2.2 Timeline of significant ECC discoveries and events1949 Claude E. Shannon publishes his landmark paper Communication in the Presence of

Noise, defining a bound on the maximum amount of error-free digital data (that is,information) that can be transmitted over such a communication link with a

specified bandwidth in the presence of the noise interference, under the assumptionthat the signal power is bounded and the Gaussian noise process is characterized bya known power or power spectral density.

1950 Richard Hammingintroduces Hamming codes for forward error correction (FEC - atype of ECC whereby the sender adds redundant data to its messages), which allowsthe receiver to detect and correct errors (within some bound) without the need to askthe sender for additional data.

1955 Peter Elias introduces convolutional codes

1960 Irving S. Reed and Gustave Solomon propose Reed-Solomon codes

1960 Robert G. Gallagerproposes Low-density parity-check codes; they are unused for30 years due to technical limitations.

1967 Andrew Viterbi presents the Viterbi algorithm, making decoding of convolutionalcodes practicable.

1993 Claude Berrou, Alain Glavieux and Punya Thitimajshima introduce Turbo codes

1998/9 Richardson, Urbanke, and MacKay, rediscover LDPC

Task 2 References[1] D. MACKAY, "Good Error-Correcting Codes Based on Very Sparse Matrices,"

IEEE Transactions on Information Theory, vol. 45, no. 2, March 1999

[2] D. MACKAY, R.M. NEAL, Near Shannon limit performance of low densityparity check codes, Electronic Letters, vo1.32, No.18, pp.1645-1646, August1996

[3] SAE-YOUNG CHUNG, G. D. FORNEY, T. J. RICHARDSON and R.URBANK, "On the Design of Low-Density Parity-Check Codes within 0.0045dB of the Shannon Limit," IEEE Communications Letters, vol. 5, no. 2,February 2001

[4] J. MCGOWAN, R. WILIAMSON, "Removing Loops from LDPC Codes,"Australian Communication Theory Workshop Proceedings, 2003

8/7/2019 10.1.1.113.5204

30/49


Task 3 Adaptive Communication Techniques (01-10-2005 to 31-03-2007)

To compare the different adaptive OFDM techniques already available fortransmission over media other than power lines (for instance, bit rate reduction,precoding, OFDM subcarrier supression), and assess their suitability for PLC

communication.To develop new adaptive schemes that take in consideration the specific nature of thepower line transmission medium.


In this Task, adaptive techniques have been developed and added to thecommunication system program developed in Task 2. A new equalizer, whichperforms the crossing information of the noise channel between adjacent sub-bandshave been developed and tested. Also, a new adaptive algorithm - the Kalman LMS -have been developed and tested.

3.1 The KLMS Algorithm and It Simplification SIKLMS

The Least Mean Squares (LMS) algorithm for adaptive filters has beanextensively studied and tested in a broad range of applications [14]. In [1] and in [5]a relation between the Recursive Least Squares (RLS) and the Kalman filter [6]algorithm is determined, and in [1] the tracking convergence of the LMS, RLS andextended RLS algorithms, based on the Kalman filter, are compared. However, thereis no link established between the Kalman filter and the LMS algorithm.

The classical adaptive filtering problem can be stated in the following manner.

Given an input signal u(n) and a desired signal d(n) determine the filter, w, thatminimizes the error, e(n), between the output of the filter, y(n), and the desired signal,d(n). An algorithm that solves this problem is the well known LMS, which for thecase of Finite Impulse Response (FIR) Transversal filters, is given by,

w(n + 1) = w(n) + mu(n)* e(n) (1)

This equation updates the vector of the filter coefficients w(n).The output of thefilter is y(n) = wT(n) u(n) with u(n) =[u(n) u(n-N+1)] were Nis the filter length, ande(n) = d(n) - y(n).

It is known that the LMS algorithm is only stable if the step size is limited,namely it should be inversely proportional to the power of the reference signal [1].This leads to the normalized LMS algorithm (NLMS). It is shown in [1] that thisalgorithm is stable as long as the step size abe restricted to0

8/7/2019 10.1.1.113.5204

31/49


where q is selected to be small enough when compared with uT(n) u(n)*. This isusually chosen in an ad doc fashion. Techniques to select this value based on theproposed algorithm are presented later.

The Kalman filter can be used in adaptive filtering by making a number ofcorrespondences. The adaptive filtering problem is reformulated as a state estimationproblem, were the state vector corresponds to the filter coefficients vector. Since the

state estimate is the state that minimizes the square of the error at each coefficient, itwill also minimize the output error of the filter [6]. The optimal filter variation in timeis modeled as a Markov model with white noise input, n(n), and state transitionmatrix, F(n) = I with close to one. The measured signal d(n) is related to the statethrough the reference signal vector u(n) plus an additional measurement noise v(n).

The resulting algorithm is then,

( ) ( ) ( ) ( )Tn d n u n w n = (3)

( ) ( ) ( )( 1) ( )( ) ( ) ( ) ( )

wT

w v

n u n nw n w nu n n u n q n

+ = + +

(4)

2 2( ) ( ) ( ) ( )

( 1) ( ) ( )( ) ( ) ( ) ( )

Tw w

w w nnTw v

n u n u n nn n Q n

u n n u n q n

+ = +

+ (5)

The variance matrix ( )w n can be made diagonal by carefully selecting the statenoise autocorrelation matrix Qnn(n) at each iteration. More, this can be done withoutchanging the state noise total power, tr{Q

nn(n)}, were tr{}stands for the trace of the

matrix. To do this one simply makes ( )w n =2 ( )w n I and tr{Qnn(n)} = N qn(n) and

apply the trace operator to (5). The resulting algorithm is the Kalman based LMSalgorithm (KLMS) and is represented in table 3.1. Note that tr{u(n) uT(n)}= uT(n)u(n).The actual algorithm presented in table 3.1 has been modified to allow complexsignals. Namely, in the calculation of the power and in the coefficients update, u(n)*,the conjugate of u(n), is used in its place.

Initialize

w(0)=0 (6) 2 (0)

w

= 20w

(7)

Iterate from n=0 to

P=uT(n)u(n)

* (8) (n)=d(n)-u

T(n)w(n) (9)

8/7/2019 10.1.1.113.5204

32/49


*

2

( ) ( )( 1) ( )

( ) ( ) / ( )v w

u n nw n w n

P n q n n

+ = +

+(10)

2 22

( ) /( 1) ( ) 1 ( )

( ) ( ) / ( )w w n

v w

P n Nn n q n

P n q n n

+ = +

+ (11)

Table 3.1 - Normalized LMS algorithm based on the Kalman filter - KLMSalgorithm.

The model for the state variation is,

( 1) ( ) ( )j j jw n w n n n+ = + (12)

Each coefficient corresponds to a low frequency signal, with time constant given

by = T / ln() were Tis the sampling period. This can be approximated by = T /(1- ) ifis close to one. So one has, (1-T/). The variance of each coefficient iseasily calculated as,

221

nw

q

=

(13)

This should be equal to the value chosen to initialize the algorithm 2w=2

0w .This results that the state noise can be chosen as,

2 2) 20 0(1 2n w wT

q

= (14)

where the last approximation is valid for large , where is the time constant of theunderlaying model, as previously discussed.

The use of the NLMS algorithm can lead to amplification of the measurementnoise in low order filters when the reference signal power takes low values. This canbe seen by assuming d(n) = uT(n)wop(n) + v(n) and rearranging the NLMS algorithmto,

*( )

( 1) ( ) ( ) ( ) ( ) ( )op T v nw n I w n w n u n u n q+ = + + + (15)

where is a matrix given

*

*

( ) ( )

( ) ( )

T

T

u n u n

u n u n q =

+(16)

8/7/2019 10.1.1.113.5204

33/49


Equation (15) may be made diagonal to represent a bank of lowpass first orderIIR filters with added noise, the last term in the equation. For low reference signalpower this term will assume high values, resulting in poor performance of thealgorithm. The KLMS solves this problem by carefully selection of the value ofq.

Simulation results are presented for the case of a one coefficient complex filterand a ten real coefficient filter. Comparisons are made with the LMS and NLMS

algorithm. The one-coefficient complex filter is typically used in orthogonal frequencydivision multiplexing (OFDM) [7] channel equalization. In this applicationequalization is done in the frequency domain resulting in one-coefficient filters. Also,due to the presence of nulls in the channel frequency response and due to the low passcharacteristics of many channels, the input signal power varies considerably.

The measurement noise, which is a prior to the algorithm, can be consideredconstant, resulting in a large variation of the signal-to-noise ratio. This fits nicely tothe KLMS formulation while the NLMS is more suitable for a fixed signal to noiseratio, since the parameter is related to it. Also, the NLMS will perform poorly whenthe input signal power takes low values, as shown in the simulations.

0 20 40 60 80 1000.2

0.4

0.6

0.8

1

1.2

Iterations

MeanSquareError

Kalman LMS

NLMS

LMS

Fig. 3.1 - Mean Square error convergence of the KLMS, NLMS and LMSalgorithm. The parameters off all the algorithms were optimized for best

performanceFig. 3.1 presents the convergence curves of the mean square error between the

output of the adaptive filter and the desired signal for the case of a one coefficient

complex filter. The reference signal was uniform distributed with power of one, andthe measurement error had a standard deviation or root mean square value (RMS) of0.3. This results in a signal to noise ratio of 10 .4 dB that is enough to allow fairly lowbit error rate in QPSK communication. The measurement noise power of the KLMShas set to, qv= (0.3)

2, the optimal value, and the state noise to zero. The step size ofthe LMS and NLMS were optimized to achieve a similar residual noise. The curvesare the result of the ensemble average of 100 trials.

It can be seen that the KLMS has the best performance. In the case of theNLMS, due to the low filter order, occasional low values of the reference signal power

8/7/2019 10.1.1.113.5204

34/49


result in very high values of the residual error. The LMS has slower initialconvergence.

0 20 40 60 80 1000.2

0.4

0.6

0.8

1

1.2

Iterations

MeanSquareError

Kalman LMS

NLMS

LMS

Fig. 3.2. Mean Square error convergence of the KLMS, NLMS and LMS

algorithm, with a 3 times higher reference signal level than in Fig. 1 but with thesame algorithms parameters.

In Fig. 3.2 the reference signal level was amplified three times, while the

parameters of all the algorithms were kept constant. It can be seen that the LMSalgorithm gets unstable. The NLMS has fewer problems, but it still suffers frommeasurement noise amplification occasionally. The KLMS still performs accurately.In addition, the KLMS has faster convergence than the NLMS.

Fig. 3.3 provides a comparison of the convergence of the mean square error of

the KLMS, and NLMS.

20 40 60 80 1000.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

Iterations

MeanSquareError

Kalman LMS

NLMS

Fig. 3.3. Mean Square error convergence of the KLMS, NLMS and LMSalgorithm, for a 10 real coefficient filter.

8/7/2019 10.1.1.113.5204

35/49


The desired signal was equal to the reference signal filtered by a sinusoidalbandpass filter, with unit gain at the center frequency. The reference signal had unitpower and the RMS of the measurement error was 0 .3. Some care had to be taken inthe initial stages, when the filter buffer was not full. The NLMS buffer was initiallyfilled with ones to prevent the step size to increase to much at the initial stages. In thecase of the KLMS the buffer can be left at zero, as long as care is taken in choosing

the prior standard deviation of the filter coefficient, 20w . Both algorithms have similarperformance.

Simplification of the AlgorithmIf one is not interested in the initial convergence, then the algorithm in Table 3.1

can be simplified. The coefficients estimation error standard deviation 2w(n)

converges to a steady state value, resulting that qv(n)/2w(n) converges to,

24

( ) / ( ) ( 1 1 )2vv w

n

qPq

NPq = + + (3.17)

This can be used in place of 2( ) / ( )v wq n n . The value of the state noise can becalculated as in (3.14).

Another approximation can be made if, the state noise is low or zero. In this caseequation 3.5 can be written as,

1 1( ) ( )

( 1) ( )T

w w

v

u n u nn n

q

+ = + (3.18)

The matrix 1( 1)w n + can be approximated by a diagonal matrix if the reference

signal autocorrelation is narrow. Doing this and applying the trace operator results,

2 2( )

( 1) ( )( )w w v

P nn n

Nq n

+ = + (3.19)

by defining the total power up to (but not counting) time n, ( )TP n , by the equation,

( 1) ( ) ( )T TP n P n P n+ = + (3.20)

one can prove by finite induction that

2( )

( ) Twv

P n Xn

Nq

+= (3.21)

8/7/2019 10.1.1.113.5204

36/49


as long as

20/ and (0) 0v w TX Nq P = = (3.22)

resulting in the algorithm presented in Table 3.2. Note that this algorithm is equivalentto the KLMS for the case N= 1.

Initialize

w(0)=0 (3.23) (0)TP =0 (3.24)

Iterate from n=0 to

P=uH(n)u(n) (3.25)

(n)=d(n)-u

T(n)w(n) (3.26)

*

20

( ) ( )( 1) ( )

( ) ( ) / ( ) /T v w

u n nw n w n

P n P n N q n

+ = +

+ +(3.27)

PT(n+1)=PT(n)+P(n) (3.28)

Table 3.2 - Information Form Kalman Based LMS - IKLMS - algorithm

The algorithm also suggests further simplification where the time varyingquantity PT(n+1) is replaced by an estimate of its value at time M, resulting in,

*

20

( ) ( )( 1) ( )

1( ) ( ) /H v w

u n nw n w n

N Mu n u n q

N

+ = ++ +

(3.29)

We call this algorithm the Simplified Information Form Kalman LMS -

SIKLMS.

In the next simulation results, the reference signal is the output of the channeland the desired signal is the input of the channel. The input of the channel was aQAM64 signal with a power of one. A noise signal with standard deviation of 0 .25was added at the output of the channel. This means that the channel is driven with acapacity gap of 3 dB. The measurement noise power of the KLMS has set to, qv=(0.25)2, the optimal value. The step size of the LMS and NLMS and the N of theSIKLMS were optimized to maximize the convergence rate of the algorithms,resulting in the values of 0.5, 0.5 and 2.0. The curves are the result of the ensembleaverage of 100 trials.

8/7/2019 10.1.1.113.5204

37/49


Fig. 3.4 presents the convergence curves of the mean square error between theoutput of the adaptive filter and the desired signal for the case of a one coefficientcomplex filter for channel equalization and tracking.

The step of the NLMS and LMS algorithm was set to 0.5 and the Mparameterof theSIKLMS was set to 2.0. It can be seen that the KLMS has the best performance.In the case of the NLMS, due to the low filter order, occasional low values of the

reference signal power result in very high values of the residual error. The LMS andSIKLMS both have good results.

Fig. 3.4. Mean-square error convergence of the NLMS, KLMS and

SIKLMS algorithms. The parameters of the LMS and NLMS were optimized formaximum convergence.

In Fig. 3.5, the reference signal level was amplified by 40%, while the

parameters of all the algorithms were kept constant. It can be seen that the LMSalgorithm gets unstable. The NLMS has fewer problems, but it still suffers frommeasurement noise amplification occasionally. The KLMS and SIKLMS still givegood results.

To note that in the conditions of Figures 3.4 and 3.5, namely N=1, the IKLMSresults in the KLMS.

8/7/2019 10.1.1.113.5204

38/49


Fig. 3.5. Mean Square error convergence of the LMS, NLMS, KLMS and

SIKLMS algorithms, with a 3 times higher reference signal level than in Fig. 3.4but with the same algorithms parameters.

Fig. 3.6 provides a comparison of the convergence of the mean square error ofthe IKLMS, Kalman Filter, KLMS, and NLMS for a 10 real coefficient filter. Thedesired signal was equal to the reference signal filtered by a bandpass filter, with unitgain at the center frequency. The reference signal had unit power and the RMS of themeasurement error was 0.03. The adaptive algorithms were only started after teniterations, when the input buffer was full. The step size of the NLMS was optimizedfor best performance while in the KLMS, the algorithms parameters were chosennaturally. As one can see, the performance of the Kalman filter is the best, but at theexpenses of a much heavier computational effort. All the others have a similar

performance. The SIKLMS, although not shown in this figure, present a behaviorsimilar to that of the KLMS.As a conclusion, new versions of the NLMS algorithm based on the Kalman

filter (the KLMS, the IKLMS and the SIKLMS) were derived. The new algorithms arestable since they were derived from the Kalman filter. They allows faster convergenceand much higher noise immunity when the reference signal vector norm takes lowvalues, namely in the case of low order filters (like in OFDM systems). In the NLMSalgorithm, q, prevents division by zero. In the new algorithms accurate formulas forqgive it good noise immunity properties. The simplified versions of the KLMS, namelythe IKLMS and the SIKLMS, although provide a slightly worse performance as theoriginal KLMS, they require a lighter computational effort, being good performance-complexity compromises.

8/7/2019 10.1.1.113.5204

39/49


10 20 30 40 500

0.1

0.2

0.3

0.4

0.5

Iterations

RootMeanSqu

areError

KLMS

IKLMS

NLMS

Kalman Filter

Fig. 3.6. Mean Square error convergence of the Kalman Filter, KLMS,

IKLMS and NLMS algorithm, for a 10 real coefficient filter. The NLMS step washand optimized.

From the results obtained with this Task two papers have been submitted: one tothe ISCASSP'07 [8] and other to the ISCAS07 [9].

3.2 The Crossing Information Adaptive Equalizer

The PLC channel is a time-variable response channel, susceptible to high noiselevels, due to the very different kinds of loads connected to the power grid. The model

used was proposed by Gotz, Rapp and Dostert [10]. Figures 3.7, 3.8 and 3.9 show itsamplitude response, phase response and impulse response, respectively.The full system, simulated in MATLAB Simulink, has the architecture

represented in Fig. 3.10. From left to right, (top to bottom), the data in the systemflows from a random data source, arrives at the transmitter modem, where it isencoded, modulated and sent through the PLC channel. At the receiver modem, afterdemodulation and decoding the data is recovered. The remaining bottom blocksimplement performance monitoring.

8/7/2019 10.1.1.113.5204

40/49


Modulus(dB)

Fig. 3.7. PLC channel amplitude response.

Phase(rad)

Fig. 3.8. PLC channel phase response.

8/7/2019 10.1.1.113.5204

41/49


Amplitude

Fig. 3.9. PLC channel impulse response.

8/7/2019 10.1.1.113.5204

42/49


8/7/2019 10.1.1.113.5204

43/49


A more detailed functional block-level representation is presented in Fig 3.11.

Fig. 3.11. PLC system block-level architecture.

Simulation ResultsTwo of the constraints that were expected to greatly impact the outcome system

performance were channel equalization, and frequency offset between the transmitterand receiver.

Channel equalization allows the receiver to compensate for some attenuation inspecific frequencies PLC channel, effectively trying to invert the PLC channel time-varying transfer function.

Three different simulations results for channel equalization are shown in Fig.3.12. Blue line depicts the results for no-equalizer and no-PLC channel scenario. Redline depicts the results for equalizer without PLC channel scenario. Finally, green linedepicts the results for equalizer and PLC channel scenario.

For both no-PLC channel scenarios, the symbol probability error is always lowerwhen the equalizer is used. Both lines converge to probability 0.75, which is theintrinsic system symbol probability error, for very high noise levels. For the PLCchannel scenario and since the line noise is larger, the equalization processconvergence is worst, and the symbol probability error is higher.

Frequency offset between transmitter and receiver can cause degradation in thesymbol decoding process and in severe cases no synchronization between transmitterand receiver. Figures 3.13 and 3.14 depict two different scenarios, with probability ofsymbol error (symbol error rate) of 10-2 and 10-4, respectively. The offset (X axis) ismeasured in symbols length. The type of channel used is AWGN. An important detailfrom both figures, is that they both show a bias (static error), when compared to theideal channel (Fig. 3.13).

8/7/2019 10.1.1.113.5204

44/49


OFDM carrier frequency desviation

Fig. 3.12. Performance evaluation of the equalizer.


Fig. 3.13. Symbol error rate with ideal channel.

8/7/2019 10.1.1.113.5204

45/49



Fig. 3.14. Performance evaluation for a symbol error rate of 10-2.


Fig. 3.15. Performance evaluation for a symbol error rate of 10-4.

8/7/2019 10.1.1.113.5204

46/49


From both figures, one can conclude that there must be a correlation betweenhigh frequency offsets and high SNRs at the channel, in order to have low probabilityof symbol error.

As a conclusion, according to the so far obtained results in the OFDM simulatedPLC system, the following recommendations were confirmed:

It would be advantageous to implement an adaptive OFDM control in the

transmitter to detect and avoid the channel frequencies that lead to more errors, inorder to reduce the error probability of the transmitted signal; Some sort of forward error correction (FEC) coding should be

implemented, in order to attempt extracting the most possible information of areceived sequence, instead of just discarding it, with the added benefit of increasingthe transmission bit rate.

Task 3 References[1] S. HAYKIN, Adaptive Filter Theory. Prentice-Hall, Inc., 1996.[2] J. HOMER, Quantifying the convergence speed of LMS adaptive FIR filter

with autoregressive inputs, Electronics Letters, vol. 36, no. 6, pp. 585586,March 2000.[3] Y. GU, K. TANG, H. CUI, and W. DU, Modifier formula on mean square

convergence of LMS algorithm, Electronics Letters, vol. 38, no. 19, pp.1147 1148, September 2002.

[4] M. CHAKRABORTY and H. SAKAI, Convergence analysis of a complexLMS algorithm with tonal reference signals, IEEE Trans. Speech AudioProcess., vol. 13, no. 2, pp. 286 292, March 2005.

[5] A. SAYED and T. KAILATH, A state-space approach to adaptive RLSfiltering, IEEE Signal Process. Mag., vol. 11, no. 3, pp. 18 60, July 1994.

[6] B. D. O. ANDERSON, Optimal Filtering. Dover Publications, 2005.

[7 J. A. C. BINGHAM, Multicarrier modulation for data transmission: an ideawhose time has come, IEEE Commun. Mag., vol. 28, no. 5, pp. 514, May1990.

[8] P. LOPES, G. TAVARES, J. GERALD, A New Type of Normalized LMSAlgorithm Based on the Kalman Filter, ICASSP07.

[9] P. LOPES, J. GERALD, New Normalized LMS Algorithms Based on theKalman Filter, ISCAS07.

[10] M. GOTZ, M RAPP and K. DOSTERT, Power Line Channel Characteristicsand Their Effect on Communication System Design, IEEE CommunicationsMagazine, pp. 0163-6804, Apr. 2004;

8/7/2019 10.1.1.113.5204

47/49


RELATRIO DE EXECUO FINANCEIRA

Segue em separado

8/7/2019 10.1.1.113.5204

48/49


Financiamento Recebido

Unidade: Euros

FONTES DEFINANCIAMENTO

1 ANO 2 ANO 3 ANO Total

FCT

AUTO-FINANCIAMENTO

OUTRO

TOTAL

Lista do equipamento adquirido(Equipamento de valor superior a 500 Euros)

(indicar a marca e modelo ou referncia do equipamento adquirido)DESCRIO N RECIBO DATA FORNECEDOR OBS.

8/7/2019 10.1.1.113.5204

49/49

Termo de responsabilidade

Instituio Proponente

Nome Instituto de Engenharia de Sistemas e Computadores, Investigao e Desenvolvimento emLisboa

Data 30 de Janeiro de 2007

Assinatura (com carimbo ou selo branco)

Investigador Responsvel

Nome Jos Antnio Beltran Gerald


Assinatura

Instituio 1

Nome Escola Superior de Tecnologia e Gesto


Assinatura (com carimbo ou selo branco)

Investigador Responsvel da Instituio 1

Nome Luis Miguel Gomes Tavares


Assinatura

Documents

10.1.1.113.5204