1. S. Rajbhandari, Prof. Z. Ghassemlooy, Prof. M. Angelova School of Computing, Engineering & Information Sciences, University of Northumbria, Newcastle upon Tyne, UK. sujan.rajbhandari@unn.ac.uk http://soe.unn.ac.uk/ocr

2. Content  Introduction to indoor optical wireless system (OWS)  Challenges in OWS.  Artificial light interference, its effect in indoor OWS links and techniques to mitigate.  DWT based denoising.  Realization of the propose system.  Future works  Conclusion

3. History of Optical Communication  The very first form of wireless speech communication was achieved at optical wavelengths in 1878 by Alexander Graham Bell, more than 25 years before Reginald Fessenden did the same thing with radio 1. 1 Alexander Graham BELL, American Journal of Sciences, Third Series, vol. XX, no.118, Oct. 1880, pp. 305- 324. 2 F. R. Gfeller and U. Bapst, Proceedings of the IEEE, vol. 67, pp. 1474- 1486, 1979. Diagram of photophone from Bell paper 1  Development of LASER in 60’s, optical fibre and semiconductor has made the modern communication possible.  The modern era of indoor wireless optical communications was proposed in 1979 by F.R. Gfeller and U. Bapst 2. In fact it was the first LAN proposed using any medium.

4. Optical Wireless System (OWS): Overview 1 M. Kavehrad, Scientific American Magazine, July 2007, pp. 82-87. Typical optical wireless system components Optical wireless connectivity 1  Communication system using light beams (visible and infrared) propagated through the atmosphere or space to carry information.  Optical transmitter Light Emitting Diodes (LED) Laser Diodes (LD)  Optical receiver p-i-n Photodiodes. Avalanche Photodiodes.  Links Line-of-sight(LOS) Non-LOS Hybrid

5. What OWS offers  Abundance bandwidth  High data rate  License free operation  High Directivity  small cell size  can support multiple devices within a room  Free from electromagnetic interference  suitable for hospital and library environment.  cannot penetrate opaque surface like wall  Spatial confinement  Secure data transmission  Compatible with optical fibre (last mile bottle neck?)  Low cost of deployment  Quick to deploy  Small size, low cost component and low power consumptions.  Simple transceiver design.  No multipath fading

6. Challenges (Indoor) ChallengesCauses(Possible ) Solutions Power limitationEye and skin safety. Power efficient modulation techniques, holographic diffuser, transreceiver at 1500ns band NoiseIntense ambient light (artificial/ natural) Optical and electrical band pass filters, Error control codes Intersymbol interference (ISI) Multipath propagation (non-LOS links) Equalization, Multi-Beam Transmitter No/Limited mobility Beam confined to small area. Wide angle optical transmitter, MIMO transceiver. Shadowing Blocking LOS linksDiffuse links/ Cellular System/ wide angle optical transmitter Limited data rateLarge area photo- detectors Bandwidth-efficient modulation techniques /Multiple small area photo- detector. Strict link set-upLOS linksDiffuse links/ wide angle transmitter

7. Common Baseband Digital Modulation Techniques OOK  Simple to implement  High average power requirement  Suitable for Bit Rate greater than 30Mb/s  Performance detiorates at higher bit rates PPM  Complex to implement  Lower average power requirement  Higher transmission bandwidth  Requires symbol and slot synchronisation DPIM  Higher average power requirement compared with PPM  Higher throughput  Built in symbol synchronisation  Performance midway between PPM and OOK. DH-PIM  The highest symbol throughput  Lower transmission bandwidth than PPM and DPIM  Built in symbol synchronisation  Higher average power requirement compared with PPM and DPIM.  Complex decoder

8. Artificial Light Interference (ALI)  Dominant noise source at low data rate.  Spectral overlapping of signal and interference produce by fluorescent lamp driven by electronic ballasts  can cause serious performance degradation as the interference amplitude can be much higher than signal amplitude.  The effect of noise is minimised using combination of the optical band pass filter and electrical low pass filter. Optical power spectra of common ambient infrared sources. Spectra have been scaled to have the same maximum value. Wavelength (  m) Normalised power/unit wavelength 0 0.2 0.4 0.6 0.8 1 1.2 0.30.40.50.6 0.70.80.91.0 1.1 1.21.31.41.5 SunIncandescent x 10 1 st window IR Fluorescent 2 nd window IR P ave)amb-light >> P ave)signal (Typically 30 dB with no optical filtering)

9. Fluorescent Light Interference Model 1  m high (t)  high frequency component.  m low (t)  low frequency component. A. J. C. Moreira, R. T. Valadas, and A. M. d. O. Duarte, IEE Proceedings -Optoelectronics, vol. 143, pp. 339-346. Low frequency component High frequency component Optical power penalty due to FLI

10. ALI-Possible Solutions  Differential receiver 1  Differential optical filtering 2  Electrical high pass filter 3,4  Polarisers 5  Angle diversity receiver 6,7  Discrete wavelet transform based denoising 8,9 1 J. R. Barry, PhD Dissertation, University of California at Berkeley, 1992 2 A.J.C Moreira, R. T. Valadas, A. M. De Oliveira Duarte, Optical Free Space Communication Links, IEE Colloquium on, vol., no., pp.5/1-510, 19 Feb 1996. 3 R. Narasimhan, M. D. Audeh, and J. M. Kahn, IEE Proceedings - Optoelectronics, vol. 143, pp. 347-354, 1996. 4 A. R. Hayes, Z. Ghassemlooy, N. L. Seed, and R. McLaughlin, IEE Proceedings - Optoelectronics vol. 147, pp. 295- 300, 2000. 5 S. Lee, Microwave and Optical Technology Letters, vol. 40, pp. 228-230, 2004. 6 R. T. Valadas, A. M. R. Tavares, and A. M. Duarte, International Journal of Wireless Information Networks, vol. 4, pp. 275-288, 1997. 7 J. M. Kahn, P. Djahani, A. G. Weisbin, K. T. Beh, A. P. Tang, and R. You, IEEE Communications Magazine, vol. 36, pp. 88-94, 1998. 8 S. Rajbhandari; Z. Ghassemlooy; and M. Angelova, IJEEE, Vol. 5, no. 2,pp102-111. 2009. 9 S. Rajbhandari; Z. Ghassemlooy; and M. Angelova, Journal of Lightwave Technology, on print.

11. Feature Extraction Tools Time-Frequencies Mapping Fourier Transform Short-Time Fourier Transform Wavelet Transform No time- frequency Localization Fixed time-frequency resolution: Uncertainty problem No resolution problem :Ultimate Transform

12. Discrete Wavelet Transform Level 1 DWT coefficients Level 2 DWT coefficients x[n]x[n] h[n]h[n] 2 g[n]g[n] 2 y1hy1h y1ly1l h[n]h[n] 2 g[n]g[n] 2 y2hy2h y2ly2l Signal Filtering Down- sampling  Coefficient can efficiently be obtained by successive filtering and down sampling.  The two filter are related to each other and are known as a quadrature mirror filter.  Reconstruction is inversion of decomposition process  filter, up sample and combine. (14)

13. DWT based Denoising Multiresolutional analysis tree  DWT is a multiresolutional analysis (MRA) tool  signals are divided into half-frequency bands at each level of the decomposition.  Separate the received signal into different frequency bands.  Remove the frequency band that corresponding to interference.  Reconstruct the signal using inverse DWT.  Challenge: spectral overlap between the signal and interference (both signals have high PSD at a low frequency region).  The denoising should be carried out to ensure that information lost is minimum.

14. System Descriptions  FLI is a low frequency band signal, the approximation coefficients need to be manipulated.  For denoising proposes, the approximation coefficients corresponding to the FLI are made equal to zero so that reconstructed signal is free from FLI.  The signal is then reconstructed using the inverse DWT.

15. DWT based Denoising Received OOK signal in the presence of the FL interference, The eye-diagram of received signal corrupted by ALI The eye diagram of received signal with wavelet denoising.  Complete closer of eye in the eye- diagram of the signal corrupted by ALI  high BER.  Wide opening of the eye with wavelet denoising.  The number of decomposition level for DWT is calculated using: where is the ceiling function  Approximate cut-off frequency of 0.5 MHz is used as it provide near optimum performance.

16. DWT based Denoising PSD of the OOK with FLI and DWT denoising at 2 Mbps PSD of the OOK with FLI and DWT denoising at 200 Mbps  No significant changes in PSD at frequency > 0.5 MHz.  Significant portion of the spectral content at < 0.3 MHz is removed with no DC contents.  Spectral overlap between signal and interference  power penalty.

17. Performance of OOK with DWT The normalized OPP to achieve a error rate of 10 -6 for OOK, 8-PPM and 8-DPIM for ideal and interfering channels and with DWT denoising at data rates of 10 - 200 Mbps.  DWT based receiver reduces the optical power requirement significantly.  Above data rate of 40 Mbps, the optical power penalty for OOK-NRZ is less than 1.5 dB.  Optical power penalty is the highest for OOK due to a high DC content.  Optical power penalty for PPM and DPIM is ~0.5 dB.  Since the PPM has zero spectral component near DC value, PPM offers improved performance.

18. DWT vs. HPF DWTHPF PerformanceDisplays similar or better performance compared to the best achieved with the HPF. Significantly inferior performance at high data rate compared to DWT. OptimizationOptimization is not necessary as decomposition level can only be positive integer. Optimization is necessary to obtain best performance. ComplexityReduced complexity compared to HPF. Example, the maximum number operation for ‘db8’ wavelet is 60n, n length of input signal. High. Example, for a HPF of order L, the total number of floating point operations is nL/2. L=2148 at data rate of 200 Mbps. RealizationEasy as repetitive structure is used. Realization becomes difficult with increasing in order.

19. Implementation- DWT

20. Implementation- TI DSP Using TI DMS320C6713 DSP board + Matlab/Simulink DSP Board

21. Conclusion  Indoor optical wireless systems will have a major role in future indoor personal communication.  A number of key challenges needs to be address before fully potential can be realized.  Artificial light interference is a dominant noise source that impair the link performance.  Artificial light interference can be reduced effectively by using the discrete wavelet transform.  Discrete wavelet transform provide improved performance with reduced complexity compared to the high pass filter.  Discrete wavelet transform based denoising can easily be realized using DSP.

22. Acknowledgement  Northumbria university for providing an studentship.  My supervisors: Prof. Maia Angelova and Prof. Fary Ghassemlooy.  All my colleagues.  Finally my family members.