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1 Applications of Wavelet Transform and Artificial Neural Network in Digital Signal Detection for Indoor Optical Wireless Communication Sujan Rajbhandari Supervisors Prof . Maia Angelova Prof. Z. Ghassemlooy Prof. Jean-Pierre Gazeau Sujan Rajbhandari 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 Sujan Rajbhandari 2 Transmission Format Transmitted signal ‘1’ ‘0’ presence of an optical pulse absence of an optical pulse Transmitted OOK 1 Ampitude 0.8 0.6 0 1 1 0 0 0 1 0 1 1 0.4 0.2 0 0 2 4 6 Normalized Time Sujan Rajbhandari 8 10 4 Links Non-LOS LOS LOS Rx Tx Tx Rx No multipath propagation Noise and device speed Multipath Propagation are limiting factors Intersymbol interference (ISI) Possibility of blocking Difficult to achieve high data rate if ISI is not mitigated. Sujan Rajbhandari 5 Received Signal Non-LOS LOS Received signal for non-LO OOK Received OOK for LOS links 1 1 0.8 0.8 0.6 0.6 Amplitude Amplitude 1.2 0.4 0.2 0 0.4 0.2 0 -0.2 -0.2 -0.4 -0.4 0 2 4 6 Normalized Time Sujan Rajbhandari 8 10 0 2 4 6 Normalized Time 8 10 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 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. Digital signal Reception: Problem of feature extraction and pattern classification 8 Received signal ‘1’ signal + interference ‘0’ interference only (noise and intersymbol interference (ISI)) . 2.5 1.5 2 1 Amplitude Amplitude 1.5 0.5 1 0.5 0 0 -0.5 -0.5 -1 0 0.2 0.4 0.6 0.8 1 Normalized Time 0 0.2 0.4 0.6 0.8 1 Normalized Time Interference only Sujan Rajbhandari signal + interference 9 Receiver from the Viewpoint of Statistics Testing a Null Hypothesis of a) Received signal is interference only against b) Alternative Hypothesis of received signal is signal plus interference Sujan Rajbhandari Problem of Feature Extraction and Pattern Classification 10 Receiver Block diagram Optical Receiver Wavelet Transform Feature Extraction Sujan Rajbhandari Artificial Neural Network Pattern Classification Threshold Detector 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 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 Continues Wavelet Transform CWT of Signal f(t) and reconstruction is given by ( s, ) f (t ) *s , (t )dt f (t ) ( s, ) s , (t )dds Where s , (t ) are wavelets and s and τ are scale and translation. Translation time resolution scale frequency resolution Wavelets are generated from scaling and translation 1 t the Mother wavelet. (t ) ( ) s , s s , s 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 Artificial Neural Network Fundamental unit : a neuron Based on biological neuron Capability to learn b x1 w1 . . . xn n y f (b xk .wk ) k 1 Sujan Rajbhandari wn ∑ f(.) Output y 17 Artificial Neural Network Input layer , hidden layer(s) and Input Layer Hidden Hidden Layer 1 Layer 2 output layer Extensively used as a classifier Supervised and unsupervised learning. Weight are adjust by comparing actual output and target output Sujan Rajbhandari Output Feature Extraction: Discrete Wavelet Transform DWT of Interference only DWT of signal +Interference 2 a3 a3 1 0 -1 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 0 5 10 15 20 25 30 35 40 0 -1 10 20 30 40 50 60 70 80 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20 0 5 10 15 20 25 30 35 40 0 10 20 30 40 50 60 70 80 0 -0.5 0 8 0.5 d1 d1 1 6 0 -0.5 0 4 0.5 d2 d2 1 2 0 -0.5 0 0 0.5 d3 d3 0 -1 1 0 0 1 -1 18 • 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.) Sujan Rajbhandari 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 20 De-noised Signal Non-LOS Links LOS Links Denoised signal for LOS links 1.5 Denoised signal for non-LOS links 1 Received signal Denoising 0.8 (Threshold Approach) Denoised Signal (Threshold approach) 1 Denoised Signal (Coeff. Removal Approach) 0.5 Amplitude Amplitude 0.6 Denoised Signal Coeff. Removal Approach 0.4 0.2 0 Received Signal 0 -0.2 -0.4 -0.5 0 2 4 6 8 10 0 2 4 6 Normalized Time Normalized Time •Denoising effectively removes high frequency component. •Equalization is necessary for non-LOS links •Identical performance for both de-noising approaches. Sujan Rajbhandari 8 10 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. 21 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 Sujan Rajbhandari 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 25 Thank You Discussions