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Wavelet and AI-Based Receiver for Diffuse IR Communication Channels

This research presents an experimental receiver design using wavelet analysis and artificial intelligence for diffuse infrared communication channels. The proposed system aims to address major performance limiting factors such as inter-symbol interference, noise, and power limitations. Traditional receivers, compensating methods, and alternative techniques like wavelet analysis and AI are discussed. Simulation results demonstrate significant improvements in SNR compared to threshold-based schemes. The study concludes with potential areas for future research and notes that the new technique complements existing coding methods.

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Wavelet and AI-Based Receiver for Diffuse IR Communication Channels

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  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.

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