An Experimental Receiver Design For Diffuse IR Channels Based on Wavelet Analysis & Artificial Intelligence

1. An Experimental Receiver DesignFor Diffuse IR Channels Based on Wavelet Analysis & Artificial Intelligence R J Dickenson and Z Ghassemlooy Optical Communication Research Group Sheffield Hallam University www.shu.ac.uk/ocr

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

3. Diffuse IR System - Major Performance Limiting Factors • Inter Symbol Interference • Noise • Power Limitations

4. Rx Tx Rx Rx Rx Rx Rx Compensating Methods • Modulation Schemes • DH-PIM • DPIM • PPM • Diversity • Angle • Multi-beam

5. Traditional Receiver Concepts • ZFE • DFE • Coding - Block - Convolutional - Turbo Normalised optical power requirements Vs. normalised delay spread for various modulation schemes

6. Alternative Techniques - Wavelet Analysis & Artificial Intelligence • De-noising • Image Compression • Earthquake • Electrical Fault Detection • Mechanical Plant Fault Prediction • Apple Ripeness • Communications

7. 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

8. Fourier Analysis And The Wavelet Transform Frequency spectrum The peaks will remain statically located regardless of where in time the frequencies occur 3 sine waves at different frequencies and times.

9. Fourier Analysis And The Wavelet Transform Wavelet results In the wavelet domain we have both a representation of frequency (scale), and also an indication of where the frequency occurs in time.

10. Neural Networks • Loosely based on biological neuron • Neural networks come in many flavours • Used extensively as classifiers • Supervised and unsupervised learning

11. Channel Model & Receiver Structure • Input data format: OOK NRZ • Channel: Carruthers & Kahn Channel Model, with impulse response of: where u(t) is the unit step function

12. Simulation Flow Chart • CWT: • - 5 bit sliding window • - coif1 mother wavelet • - Operating scales of 60, • 80, 100 and 120 using • 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

13. 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.

14. 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

15. Any Questions? • Thank you for your kind attention. • I will attempt to answer any questions you have.