DISCRETE CHANNEL SIMULATION OF BLUETOOTH PICONETS Beatriz Bardón Rodríguez Matilde P. Sánchez...

DISCRETE CHANNEL SIMULATION OF BLUETOOTH

PICONETS

DISCRETE CHANNEL SIMULATION OF BLUETOOTH

PICONETS

Beatriz Bardón Rodríguez

Matilde P. Sánchez Fernández

Ana García Armada

Department of Signal Theory and Communications

Carlos III University of Madrid, Spain

e-mail: {beatriz, mati, agarcia}@tsc.uc3m.es

Beatriz Bardón Rodríguez

Matilde P. Sánchez Fernández

Ana García Armada

Department of Signal Theory and Communications

Carlos III University of Madrid, Spain

e-mail: {beatriz, mati, agarcia}@tsc.uc3m.es

Workshop on Broadband Wireless Ad-Hoc Networks and Services

12th - 13th September 2002, ETSI, Sophia Antipolis, France

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Carlos III University of Madrid. Dept. Signal Theory and Communications

Introduction and motivationIntroduction and motivation

After the success of Wireless Local Area Networks (WLAN), Bluetooth has come out as an initiative to build Wireless Personal Area Network (WPAN) systems: Idea: To connect every device that we are used to carry with us (cellular

phones, PDAs, laptops, printers, …)

The success of Bluetooth depends on the massive use of the standard Development of applications that respond to the user’s needs

Bluetooth devices transmit in ISM band

Worldwide availability of frequencies

Rapid introduction to the market

Bands also used by many other devices

Degradation in throughput and quality

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Introduction and motivation (II)Introduction and motivation (II)

The coexistence of a high number of devices in the same frequency band has a great impact in the applications

Design of Applications - two options: Conservative to ensure a quick market introduction Optimum in the sense of being capable of making fuller use of the

possibilities of the communication

Simulation techniques To analyse the different choices for new services

To characterise in detail every significant effect that influences the system performance

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Introduction and motivation (III)Introduction and motivation (III)

To maintain a good characterisation of the system usually implies long simulation runs

The low bit error rates involved in the case of inclusion of some kind of channel coding imply a great computational cost in simulation

Simulations must be efficient in order to be feasible

DISCRETE CHANNEL MODELS Useful tool for simulating communication systems operating over

fading channels The bursty nature of errors generated is reproduced by means of a

state diagram that avoids the simulation of the whole physical channel

5


OutlineOutline

Bluetooth Interference Inmunity and Multiple Access Scheme Ad Hoc Networks

Discrete Channel Models for Wireless Communications Parameters of a Markov Model Estimating the parameters of the HMM

Discrete channel simulation of Bluetooth piconets

Conclusions

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BluetoothBluetooth

Provides ad-hoc connections via radio using portable devices characterized by Low cost Small size Low power comsumption

0 dBm for most applications Specifications allow to transmit up to 20 dBm

This wireless technology must support both voice and data to be transmitted over a short range distance (up to 10 meters typically)

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Bluetooth: Interference Inmunity and Multiple Access Scheme

Bluetooth: Interference Inmunity and Multiple Access Scheme

Bluetooth uses the ISM band Interferences coming from other devices (microwave ovens, WLANs) and other Bluetooth devices

To obtain the desired interference inmunity Two options Interference suppression DSSS Interference avoidance FHSS

Multiple Access Scheme FH-CDMA

(79 separate 1 MHz channels)

TDD (Time Division Duplex)

A

B

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Bluetooth: Ad Hoc NetworksBluetooth: Ad Hoc Networks

No difference between radio units (Peer communications) One unit has the ‘master’ role governing the synchronization of the

FH communication

A master and one or several slaves (8 max)

PICONET

M

S

M

SS

STwo or more piconets overlapped

in time and space

SCATTERNET

M

S

M

SS

S

S

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Bluetooth: system block diagramBluetooth: system block diagram

ACCESS CODE HEADER PAYLOAD

72 bits 54 bits 0-2745 bits

FRAME CONFORMING

SCRAMBLING

(whitening)

HEADER PAYLOAD

CODING

•FEC 1/3 •NO CODE

•FEC 1/3

•FEC 2/3

GFSK MODULATOR

BT = 0.5

0.28<h<0.35

FREQUENCY HOPPING

Radio Channel

•Multipath

•Interferences (piconets, scatternets, other devices, ...)

FREQUENCY HOPPING GFSK DEMODULATOR

DECODINGSCRAMBLING

(whitening)

FRAME EXTRACTING

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Discrete Channel Models for Wireless Communications

Discrete Channel Models for Wireless Communications

Idea: To reproduce, by means of a state diagram, the bursty nature of errors generated in that type of channels

In order to obtain efficient models the simulation is structured in several levels

signals (t)

Modulator

Demodulator

Filters, equalizers, ...

physical channel

a

a’


symbols signals (t)

Modulator

Demodulator


physical channel

a

a’


LEVEL 1

symbols signals (t)

Modulator

Demodulator


physical channel

a

a’


signals (t)

Modulator

Demodulator


physical channel

a

a’


symbols

LEVEL 1

Discrete Channel ModelGenerate symbols sequences with the same bursty characteristics

signals (t)

Modulator

Demodulator


physical channel


LEVEL 1


Interleaver, encoder,...

b’

b a

a’

symbols

Deinterleaver, decoder,...

signals (t)

Modulator

Demodulator


physical channel


LEVEL 1



b’

b a

a’

symbols


LEVEL 2

signals (t)

Modulator

Demodulator


physical channel


LEVEL 1

DCM 1

b’

b a

a’

symbols

LEVEL 2

DCM 2Applications

voice,multimedia, ...

signals (t)

Modulator

Demodulator


physical channel


LEVEL 1

DCM 1

b’

b a

a’

LEVEL 2

DCM 2

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Modelling the behaviour of the black box

Modelling the behaviour of the black box

Applicationsvoice,multimedia, ...


b’

b a

a’


physical channel

Demodulator Filters, equalizers, ...

Modulator Filters, equalizers, ...

LEVEL 1LEVEL 2

Channel state Describes the behaviour of the channel (a-a’) along time

•Good conditions

•Fading

•Noise

•Jamming, ..,

Finite state channel We visualize the channel as being in one of a set of limited and identifiable conditions or statesHow is the finite state channel model obtained?

Simple cases Derived analytically from the models of the underlying components between a-a’

Hidden Markov Models (HMM)

Most cases Derived from simulated or measured error patterns between a-a’

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Set of states {1, 2, ...N} State at time t: St

Set of state probabilities: i(t) = probability of being in state i at time t

Set of state transition probabilities: aij(t) = probability of going from state i at time t to state j at time t+1

Set of input to output transition probabilities for each state: bi(ek) = probability of obtaining the error symbol ek when St = i (from the possible ones {e1, e2, ..., eM})

Parameters of a Markov ModelParameters of a Markov Model

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abbagg

Parameters of a Markov Model (II)Parameters of a Markov Model (II)

0 0

1 1

0.9

0.9

0.10.1

0 0

1 1

0.3

0.3

0.70.7

agb

abg

Error symbols = { 0, 1 }bg0 = 0.9

bg1 = 0.1

bb0 = 0.3

bb1 = 0.7

Two statesTransition probabilities

Input to output

transition probabilities

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Under certain conditions all the parameters can be inferred from the estimation of two matrices

Problem to solve Estimate A and B Data to start Error sequence obtained from the lowest level of

simulation Tool to use Well-known iterative procedure Baum-Welch

algorithm A pair of matrices A, B that generate error sequences with the same

characteristics that the one used to train the algorithms are obtained

Estimating the parameters of the HMM

Estimating the parameters of the HMM

State transition matrix A

NNN

M

aa

aa

....

.

.

....

1

111

Input to output transition probabilities matrix

NMN

M

bb

bb

....

.

.

....

1

111

B

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Radio Channel

•Multipath (Channel models for HIPERLAN/2 in different indoor scenarios)

•Interferences ( scatternets, microwave ovens, ...)

FRAME CONFORMING

SCRAMBLING

(whitening)CODING GFSK MOD

Simulations with different coding schemes

GFSK DEMODDECODINGFRAME

EXTRACTINGSCRAMBLING

(whitening)

GFSK signal

Error sequence to train the algorithm

1 = Erroneous decision

0 = Correct decision

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Discrete channel simulation of Bluetooth piconets (II)

Discrete channel simulation of Bluetooth piconets (II)

The direct application of the Baum-Welch algorithm requires great amount of computations, specially when the error sequence contains long chains of identical symbols

K. S. Shanmugan et al. have proposed a modified version of the BW algorithm that involves great saving in computation

Once the parameters (A,B matrices) have been obtained it is indispensable to validate them for ensuring the use of the discrete channel model in upper levels of simulation

Comparison between the error sequence arising from the original physical layer and the one generated by our HMM Cross-correlation between the two sequences Histograms characterising error free intervals for the different guard times are

obtained and compared (chi-square goodnes-of-fit test)

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ConclusionsConclusions

A Discrete Channel simulation method for the efficient evaluation of Bluetooth radio system has been proposed .

Structuring the simulation in several levels and modelling them by means of Hidden Markov Models allows a great saving in computational resources.

It will be very useful to obtain models for different design options and environments.

Need for standardisation:It would be very convenient to have these Discrete Channel Models standardised for WPAN (as it has been done with GSM) in order to be able to evaluate the performance of new applications.

DISCRETE CHANNEL SIMULATION OF BLUETOOTH PICONETS Beatriz Bardón Rodríguez Matilde P. Sánchez...

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