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:
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
ICEE08, Tehran, Iran Outline Optical Wireless – Key issues Digital Signal Detection Equalization Wavelet ANN Based Receiver Results and Conclusion
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)
ICEE08, Tehran, Iran Digital Signal Detection - The Classical Approach The discrete-time impulse response of the cascaded system optical channel (ceiling bounce)
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.
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.
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
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
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.
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.
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
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
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
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
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
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
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
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
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.