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An Experimental Receiver Design For 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 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 Rx Tx Rx Rx Rx Rx Rx Traditional Receiver Concepts 12 ZFE DFE Coding - Block - Convolutional - Turbo Normalised optical power requirements (dB) 10 8 6 4 2 OOK-NRZ 0 -2 32-DH-PIM2 -4 32-DPIM -6 -8 -10 -3 10 32-DH-PIM1 32-PPM -2 -1 10 10 0 10 DT 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 x 1 w 1 Loosely based on biological neuron Neural networks come in many flavours Used extensively as classifiers Supervised and unsupervised learning x w Σ 2 2 x F Out w n n Input Layer Hidden Hidden Layer 1 Layer 2 Output Channel Model & Receiver Structure Receiver …1 0 1 0 Tx CHANNEL Rx Filter Feature Extraction Pattern Recognition WAVELET ANALYSIS NEURAL NETWORK Thresholder 1 0 1 0... NOISE • Input data format: OOK NRZ • Channel: Carruthers & Kahn Channel Model, with impulse response of: 6a 6 h(t , a) t a where u(t) is the unit step function 7 u (t ) 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 • CWT: - 5 bit sliding window - coif1 mother wavelet - Operating scales of 60, 80, 100 and 120 using Bit To Detect Decimate each set of coefficients to 100 sample points 5 Bit Window 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 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.