An Experimental Receiver DesignFor Diffuse IR Channels Based on

Wavelet Analysis & Artificial Intelligence

R J Dickenson and Z Ghassemlooy

Optical Communication Research GroupSheffield Hallam University

www.shu.ac.uk/ocr

Contents

• Diffuse IR indoor multipath channel• Compensating schemes• Traditional receivers• Wavelet and AI based receiver• Proposed receiver• Simulation results• Conclusions

Diffuse IR System - Major Performance Limiting Factors

Inter Symbol Interference

Noise Power Limitations

Tx Rx

Compensating Methods

Modulation Schemes– DH-PIM – DPIM – PPM

Diversity– Angle – Multi-beam

Tx

Rx Rx Rx

Rx Rx

Rx

Traditional Receiver Concepts

ZFE DFE Coding

- Block- Convolutional- Turbo

10-3

10-2

10-1

100

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

DT

Nor

mal

ised

opt

ical

pow

er re

quire

men

ts (d

B)

OOK-NRZ

32-DH-PIM2

32-DH-PIM1

32-DPIM

32-PPM

Normalised optical power requirements Vs. normalised delay spread for various modulation schemes

Alternative Techniques - Wavelet Analysis & Artificial Intelligence

De-noising Image Compression Earthquake Electrical Fault Detection Mechanical Plant Fault Prediction Apple Ripeness Communications

What Is A Wavelet?

Simple Description:

A finite duration waveform

Has an average value of zero

Is a basis function, just like a sine wave in Fourier analysis

Fourier Analysis And The Wavelet Transform

3 sine waves at different frequencies and times.

Frequency spectrum The peaks will remain statically

located regardless of where in time the frequencies occur

Fourier Analysis And The Wavelet Transform

Wavelet resultsIn the wavelet domain we have both a representation of frequency (scale), and also an indication of where the

frequency occurs in time.

Neural Networks

Loosely based on biological neuron

Neural networks come in many flavours

Used extensively as classifiers

Supervised and unsupervised learning

Input Layer

Hidden Layer 1

Hidden Layer 2

Output

Σ F

x 2

w 1 x 1

x n

w 2

w n

Out

Channel Model & Receiver Structure

• Input data format: OOK NRZ • Channel: Carruthers & Kahn Channel Model, with impulse

response of:

1 0 1 0... …1 0 1 0 Tx CHANNEL

NOISE

Rx Filter WAVELET ANALYSIS

NEURAL NETWORK

Feature Extraction

Pattern Recognition

Thresholder

Receiver

)(6),( 7

6

tuataath

where u(t) is the unit step function

Simulation Flow Chart

Incoming Data n bits long.

Low Pass Filter

Decimate Stream it to 5 Bit windows

CWT at 4 scales on every

window

Decimate each set of

coefficients to 100 sample

points

Pack samples into a 100xn

matrix

Offer each column to the

neuronal classifier

Threshold the output to 1 or 0

• ANN: - 4 layers with 176 neurons - 3 different activation functions, trained to detect the value of the centre bit from a 5 bit length window

• CWT:- 5 bit sliding window - coif1 mother wavelet- Operating scales of 60,

80, 100 and 120 using

Bit To Detect

5 Bit Window

Simulation Results – BER V. SNR

Data rate: 40 and 50 Mb/s Normalised delay spread: 0.44

and 0.55• for BER of 10-5 the wavelet-AI

scheme offers SNR improvement of:- ~ 8 dB at 40 Mbps - ~ 15 dB at 50 Mbps

over the filtered threshold scheme• For the wavelet-AI scheme the

penalty for increasing the data rate by 10 Mbps is ~ 5dB whilst it is around 15dB for the basic scheme.

Conclusions

A novel technique to combat multipath dispersion

Improvement of ~ 8 dB in SNR compared with the threshold based detection scheme

Promising results, however, significant further work is required.

Not intended to replace coding methods

Any Questions?

• Thank you for your kind attention. • I will attempt to answer any questions you

have.