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

2. Optical Wireless Communication Sujan Rajbhandari 2  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 Sujan Rajbhandari 0 110 0 0 1 1 01

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

5. Received Signal Sujan Rajbhandari 5 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. Sujan Rajbhandari 6

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 8 Received signal  ‘1’ signal + interference  ‘0’ interference only (noise and intersymbol interference (ISI)). Interference only signal + interference Sujan Rajbhandari

9. Receiver from the Viewpoint of Statistics 9  Testing a Null Hypothesis of a)Received signal is interference only against b)Alternative Hypothesis of received signal is signal plus interference Sujan Rajbhandari

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

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. Sujan Rajbhandari 11

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 Sujan Rajbhandari 12

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) Sujan Rajbhandari 13

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

15. Discrete Wavelet Transform Dyadic scales and positions DWT coefficient can efficiently be obtained by filtering and down sampling 1 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. Artificial Neural Network  Fundamental unit : a neuron  Based on biological neuron  Capability to learn Sujan Rajbhandari 16 b wnwn y x1x1 f(.)f(.) ∑ w1w1 Output xnxn...

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 Sujan Rajbhandari 17

18. Feature Extraction: Discrete Wavelet Transform Sujan Rajbhandari 18 DWT of Interference only DWT of signal +Interference Significant difference in approximation coefficient,a 3. No difference in other details coefficients. (Details coefficient are due to the high frequency component of signal, mainly due to noise.) Significant difference in approximation coefficient,a 3. 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 1. Threshold approach in which the detail coefficients are suppressed by either ‘hard’ or ‘soft’ thresholding. 2. Coefficient removal approach in which detail coefficients are completely removed by making it zero. Sujan Rajbhandari 19

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

21. 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 Sujan Rajbhandari 22  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 D rms 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. Sujan Rajbhandari 24

25. 25 Thank You Discussions