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ICEE08, Tehran, Iran Signal Detection and Adaptive Equalization Using Discrete Wavelet Transform - Artificial Neural Network for OOK Indoor Optical Wireless.

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Presentation on theme: "ICEE08, Tehran, Iran Signal Detection and Adaptive Equalization Using Discrete Wavelet Transform - Artificial Neural Network for OOK Indoor Optical Wireless."— Presentation transcript:

1 ICEE08, Tehran, Iran Signal Detection and Adaptive Equalization Using Discrete Wavelet Transform - Artificial Neural Network for OOK Indoor Optical Wireless Links Z. Ghassemlooy, S Rajbhandari and M Angelova School of Computing, Engineering & Information Sciences, University of Northumbria, Newcastle upon Tyne, UK

2 ICEE08, Tehran, Iran Outline  Optical Wireless – Key issues  Digital Signal Detection  Equalization  Wavelet ANN Based Receiver  Results and Conclusion

3 ICEE08, Tehran, Iran Indoor Optical Wireless Links  The key issues: -Eye safety -shift from 900 nm to 1550 nm - eye retina is less sensitive to optical radiation -power efficient modulation techniques -Mobility and blocking -diffuse configuration instead of line of sight, but at cost of: -reduced data rate -increased path loss -multipath induced inter-symbol-interference (ISI)

4 ICEE08, Tehran, Iran Digital Signal Detection - The Classical Approach  The discrete-time impulse response of the cascaded system optical channel (ceiling bounce)

5 ICEE08, Tehran, Iran Digital Signal Detection - The Classical Approach  OOK - the average probability of error: the probability of error for the penultimate bit in a i : where  opt is the optimum threshold level, set to the midway value of RP ave (T b ) 0.5.

6 ICEE08, Tehran, Iran Digital Signal Detection - The Classical Approach  Matched filter is difficult to realized when channel is time varying.  Maximising the SNR based on the assumption that noise statistics is known.  SNR is sensitive to the sampling instants. -In non-dispersive channel, the optimum sampling point is at the end of each bit period. -In dispersive channel, the optimum sampling point changes as the severity of ISI changes.

7 ICEE08, Tehran, Iran Digital Signal Detection - The Classical Approach  For higher values of normalized delay spread (> 0.52) - bit error rate cannot be improved simply by increasing the transmitter power  To mitigate the ISI, optimum solutions are: - Maximum likelihood sequence detector - Equalizers A practical solution (i) Inverse filter problem -The frequency response of the equalizing filter is the inverse of the channel response. -Adaptive equalization is preferred if the channel conditions are not known in advance. -Two classes : linear and decision feedback equalizer. (ii) Classification problem 1- J. M. Kahn and J. R. Barry, Proceedings of IEEE, 85 (2), pp , G. W. Marsh and J. M. Kahn, IEEE Photonics Technology letters, 6(10), pp , D. C. Lee and J. M. Kahn, IEEE Transaction on Communication, 47(2), pp , 1999

8 ICEE08, Tehran, Iran Equalization - A Classification Problem  Dispersion induced by channel is nonlinear in nature  Received signal at each sampling instant may be considered as a nonlinear function of the past values of the transmitted symbols  Channel is non-stationary - overall channel response becomes a nonlinear dynamic mapping

9 ICEE08, Tehran, Iran Equalization: A Classification Problem  Classification capability of FIR filter equalizer is limited to a linear decision boundary (a non-optimum classification 1)  FIR bases equalizers suffer from severe performance degradation in time varying and non-linear channels 2  The optimum strategy - to have a nonlinear decision boundary for classification - ANN - with capability to form complex nonlinear decision regions - In fact both the linear and DFE are a class of ANN 3. - Wavelet 4 1- L.Hanzo, et al, Adaptive wireless transceivers: Wiley-IEEE Press, 2002, pp C. Ching-Haur, et al, Signal Processing,vol. 47, no. 2, pp S. Haykin, Communications Magazine, IEEE, vol.38, no.12, pp , Dec D. Cariolaro et al, IEEE Intern. Conf. on Communications, New York, NY, USA, pp , 2000.

10 ICEE08, Tehran, Iran Wavelet Transform Neural Network Receiver - Classification Based Optical Receiver Feature Extraction Pattern Classification Post- Processing Optical Signal Modular based receiver:  Feature extraction (wavelet transform) - for efficient classification  Pattern classification (ANN). WT-ANN based receiver outperforms the traditional equalizers R. J. Dickenson and Z. Ghassemlooy, International Journal of Communications Systems, Vol. 18, No. 3, pp , 2005.

11 ICEE08, Tehran, Iran Fourier Transform Wavelet Transform Feature Extraction Tools Time-Frequencies Mapping Short-time Fourier Transform No time- frequency localization Fixed time-frequency resolution: Uncertainty problem No resolution problem: ultimate transform

12 ICEE08, Tehran, Iran CWT vs. DWT  CWT - Infinite scale - but with redundant coefficients  DWT - no redundancy as in CWT - easier to implement using filter banks (high pass and low pass) - reduced computational time - possibility signal denoising by thresholding the wavelet coefficient

13 ICEE08, Tehran, Iran Discrete Wavelet Transform x[n]x[n] h[n]h[n] 2 g[n]g[n] 2 h[n]h[n] 2 g[n]g[n] 2 Level 1 DWT coefficients Level 2 DWT coefficients... Signal Filtering Down- sampling  DWT coefficient - obtained by successive filtering and down sampling  Signal is decomposed: - using high pass h[n] and a low pass g[n] filters filters are related to each other and are known as the quadrature mirror filter. - down sampling by 2 cD 1 cA 1 cD 2 cA 2

14 ICEE08, Tehran, Iran WT- ANN Based Receiver Model  8-sample per bit  Signal is decimated into W-bit discrete sliding window. (i.e. each window contains a total of 8W-bit discrete samples )  Information content of the window is changed by one bit  3-level DWT for each window is determined  DWT coefficients are denoised by: i) Thresholding : A threshold is set and ‘soft’ or ‘hard’ thresholding are used for detail coefficients ii) Discarding coefficients: detail coefficients are completely discarded  Denoised coefficient are applied to ANN  ANN is trained to classify signal into two binary classed based on DWT coefficients

15 ICEE08, Tehran, Iran Denoising Signal using DWT  Hard thresholding  Soft thresholding The threshold level for universal threshold scheme:  : variance of the wavelet coefficient  Denoised signal where  -1 is the inverse WT

16 ICEE08, Tehran, Iran Simulation Parameters 16 ParametersValue Data rate R b 155 Mbps Channel RMS delay spread D rms 10 ns No. of samples per bit8 Mother waveletDiscrete Meyer ANN typeFeedforward back propagation No. of neural layers2 No. of neurons in 1 st layer4 No. of neurons in 2 nd layer1 ANN activation functionlog-sigmoid, tan-sigmoid ANN training algorithmScaled conjugate gradient algorithm ANN training sequence400 bits Minimum error1 -30 Minimum gradient1 -30 DWT levels3

17 ICEE08, Tehran, Iran Results – BER for 150 Mb/s  Maximum performance of ~6 dB compared to linear equalizer.  Performance depends on the mother wavelets.  Discrete Meyer gives the best performance and Haar the worst performance among studied mother wavelet. Figure: The Performance of OOK at 150Mbps for diffused channel with D rms of 10ns

18 ICEE08, Tehran, Iran Results - BER for 150 & 200 Mb/s  The DWT-ANN based receiver showed a significant improvement compared to linear equalizer  SNR gain of ~6 dB at BER of for W = 3  3-bit window is the optimum  Reduced complexity compared to CWT based receiver without any degradation in performance Figure: The BER performance of OOK linear and DWT- ANN base receiver at 155 and 200 Mbps for diffused channel with D rms of 10ns

19 Conclusions  The traditional tool for signal detection and equalization is inadequate in time-varying non-linear channel.  Digital signal detection can be reformulated as feature extraction and pattern classification.  Both discrete and continuous wavelet transform is used for feature extraction.  Artificial Neural Network is trained for classify received signal into binary classes.  3-bit window size is adequate for feature extraction.  Enhance performance compared to the traditional FIR equalizer ( a gain of ~ 6dB at BER of  Reduced complexity using DWT compared to CWT based receiver with identical perfromance.

20 ICEE08, Tehran, Iran Questions? Thank you!


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