Sujan Rajbhandari Supervisors Prof . Maia Angelova Prof. Fary Ghassemlooy

1. Applications of Wavelet Transform and Artificial Neural Network in Digital Signal Detection for Indoor Optical Wireless Communication Sujan Rajbhandari Supervisors Prof . MaiaAngelova Prof. FaryGhassemlooy Prof. Jean-Pierre Gazeau

2. Optical Wireless Communication • Light as the carrier of information • Also popularly known as free space optics (FSO) or Free Space Photonics (FSP) or open-air photonics . • Indoor or outdoor

3. Transmission Format Transmitted signal • ‘1’ presence of an optical pulse • ‘0’ absence of an optical pulse 1 0 1 1 1 1 0 0 0 0

4. Links • Non-LOS • Multipath Propagation • Intersymbol interference (ISI) • Difficult to achieve high data rate if ISI is not mitigated. • LOS • No multipath propagation • Noise and device speed are limiting factors • Possibility of blocking • LOS Rx Tx Rx Tx

5. Received Signal Non-LOS LOS

6. Classical Digital Signal Detection • Set a threshold level. • Compared the received signal with the threshold level • Set ‘1’ if received signal is greater than threshold level • Set ‘0’ is received signal is less than threshold level.

7. Classical signal detection techniques: Assumptions • The statistical of noise is known. • Maximise the signal to noise ratio for unknown noise with known statistics. • Channel characteristics are known( at least partially ) and generally assume to be linear.

8. Digital signal Reception:Problem of feature extraction and pattern classification Received signal • ‘1’ signal + interference • ‘0’ interference only (noise and intersymbol interference (ISI)) . Interference only signal + interference

9. Receiver fromthe Viewpoint of Statistics • Testing a Null Hypothesis of • Received signal is interference onlyagainst • Alternative Hypothesis of received signal is signal plus interference

10. Problem of Feature Extraction and Pattern Classification Wavelet Transform Threshold Detector Artificial Neural Network Optical Receiver Feature Extraction Pattern Classification • Receiver Block diagram

11. Time- Frequency analysis Fourier Transform • Time-frequency mapping • What frequencies are present in a signal but fails to give picture of where those frequencies occur. • No time resolution.

12. Time- Frequency analysis Windowed Fourier Transform (Short time Fourier transform) • Chop signal into equal sections • Find Fourier transform of each section Disadvantages • Problem how to cut a signal • Fixed time and frequency resolution

13. Time- Frequency analysis Continuous Wavelet Transform (CWT) • Vary the window size to vary resolution (Scaling). • Large window for precise low-frequency information, and shorter window high-frequency information • Based on Mother wavelet. • Mother Wavelet are well localised in time.(Sinusoidal wave which are the based of Fourier transform extend from minus infinity to plus infinity)

14. Continues Wavelet Transform CWT of Signal f(t) and reconstruction is given by • Whereare wavelets and s and τ arescaleand translation. • Translationtime resolution • scale frequency resolution • Wavelets are generated from scaling and translation the Mother wavelet.

15. Discrete Wavelet Transform • Dyadic scales and positions • DWT coefficient can efficiently be obtained by filtering and down sampling1 1 Mallat, S. (1989), "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Pattern Anal. and Machine Intell., vol. 11, no. 7, pp. 674-69

16. b x1 w1 . Output ∑ f(.) . . wn y xn Artificial Neural Network • Fundamental unit : a neuron • Based on biological neuron • Capability to learn

17. Artificial Neural Network • Input layer , hidden layer(s) and output layer • Extensively used as a classifier • Supervised and unsupervised learning. • Weight are adjust by comparing actual output and target output

18. Feature Extraction:Discrete Wavelet Transform DWT of Interference only DWT of signal +Interference • Significant difference in approximation coefficient ,a3. • No difference in other details coefficients. (Details coefficient are due to the high frequency component of signal , mainly due to noise.)

19. Denoising • The high frequency component can be removed or suppressed. • Two Approach Taken • Threshold approach in which the detail coefficients are suppressed by either ‘hard’ or ‘soft’ thresholding. • Coefficient removal approach in which detail coefficients are completely removed by making it zero.

20. De-noised Signal Non-LOS Links LOS Links • Denoising effectively removes high frequency component. • Equalization is necessary for non-LOS links • Identical performance for both de-noising approaches.

21. Artificial Neural Network : Pattern Classifier • Artificial Neural Network can be trained to differentiate the interference from signal plus interference. • DWT are fed to ANN. • ANN is first trained to classify by providing examples. • ANN can be utilized both as a pattern classifier and equalizer.

22. Results • The Coefficient removal approach (CRA) of denoising gives the best result. • Easier to train ANN using CRA as the DWT coefficients are removed by 8 folds if 3 level of DWT is taken. • Effective for detection and equalization. Figure: The Performance of On-off Keying at 150Mbps for diffused channel with a Drms of 10ns

23. Comparison with traditional methods • Maximum performance of • 8.6dBcompared to linear • equalizer • performance depends on the • mother wavelets. • Discrete Meyer gives the best performance and Haar the worst performance among studied mother wavelet

24. Conclusion • Digital signal detection can be reformulated as feature extraction and pattern classification. • Discrete wavelet transform is used for feature extraction. • Artificial Neural Network is trained for pattern classification. • Performance can further be enhance by denoising the signal before classifying it.

25. Thank You Discussions