An Experimental Receiver Design For Diffuse IR Channels Based on Wavelet Analysis & Artificial Intelligence R J Dickenson and Z Ghassemlooy O ptical C ommunication R esearch G roup Sheffield Hallam University

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

Compensating Methods  Modulation Schemes –DH-PIM –DPIM –PPM  Diversity –Angle –Multi-beam Tx Rx

Traditional Receiver Concepts  ZFE  DFE  Coding - Block - Convolutional - Turbo 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 results In 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

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

Simulation Flow Chart 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

Simulation Results – BER V. SNR  Data rate: 40 and 50 Mb/s  Normalised delay spread: 0.44 and 0.55 for BER of 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

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