1. Prof. Z. Ghassemlooy ICEE 2006, Iran 1 DH-PIM Employing LMSE Equalisation For Indoor Optical Wireless Communications Z. Ghassemlooy, W. O. Popoola, and N. M. Aldibbiat Optical Communications Research Group, School of Engineering and Technology, The University of Northumbria, Newcastle, U.K. Web site: http://soe.unn.ac.uk/ocr

2. Prof. Z. Ghassemlooy ICEE 2006, Iran 2 Contents  Overview of Optical Wireless Communications (OWC)  Modulation Techniques  ISI and Equalisation  Simulation Results  Concluding Remarks

3. Prof. Z. Ghassemlooy ICEE 2006, Iran 3 Optical Wireless – What Does It Offer ?  High data rate (in particular line of sight)  Immunity to electromagnetic interference  Abundant unregulated bandwidth  High security compared with RF  Absence of multipath fading (due to the use of IM/DD)  Complementary to RF  Etc.

4. Prof. Z. Ghassemlooy ICEE 2006, Iran 4 OWC Links – Types  Diffuse  Uses single or multiple source and detector - No requirement for alignment between them  Robust to blocking and shadowing  Allows roaming  Multiple paths (reflections) -Result in inter-symbol interference (ISI).  Limited bandwidth - Due to large capacitance of the large area detectors  Line of sight  Uses single or multiple source and detector - Requires alignment between them  High bandwidth and no multipath induced ISI  Allows roaming  Suffers from blocking and shadowing

5. Prof. Z. Ghassemlooy ICEE 2006, Iran 5 Diffuse Systems – How to Combat Noise and Dispersion  Noise Filtering: Optical or electrical  Match Filtering: Maximises SNR, the optimum detection method in the presence of noise. (Time reversed copy of received pulse convolved with received data stream)  Coding: Block codes, convolution codes (MLSD), turbo codes. Increase performance by adding redundant data!  Equalisation: Channel distortion compensating filters: - Zero Forcing Equaliser (ZFE) - Minimum Mean Square Equaliser (MMSE) - Decision Feedback Equaliser (DFE)

6. Prof. Z. Ghassemlooy ICEE 2006, Iran 6 Modulation Tree DifPAM Pulse Modulation AnalogueDigital Pulse TimePulse ShapePulse Time Isochronous AnisochronousIsochronousAnisochronous PSM PAM PIM PIWM PFM SWFM PWM PPM DPPM MPPM DPWM PCM DPIM DPIWM difPPM DH-PIM RZ RB AMI Manchester NRZ NRZ(L) NRZ(I) Miller code

7. Prof. Z. Ghassemlooy ICEE 2006, Iran 7 Digital PTM Schemes Symbol 1 2 3 OOK PPM PIM DH-PIM Time b T s T2 s T A 00 0 0 0 1111111 H2H1 Redundant space M = 4 bits L = 2 4 =16 slots (  = 2) Info. L 1 (2) (10)(15)

8. OOK  Simple to implement  High average power requirement  Suitable for Bit Rate greater tha 30Mb/s  Performance detoreaites 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 Digital PTM Schemes

9. DH-PIM- Frame Structure Nawras,2005

10. Prof. Z. Ghassemlooy ICEE 2006, Iran 10 DH-PIM – Characteristics

11. Prof. Z. Ghassemlooy ICEE 2006, Iran 11 DH-PIM – Characteristics Synchronisation Higher packet transmission throughput

12. Prof. Z. Ghassemlooy ICEE 2006, Iran 12 DH-PIM System DH-PIM encoder Transmitter filter p(t)p(t) Multipath channel h(t)h(t) n(t) n-slot DH-PIM sequence Unit energy filterr(t) (matched to p(t)) R Equaliser Compute P s in (n-1)th slot Optimum threshold detector

13. Prof. Z. Ghassemlooy ICEE 2006, Iran 13 OWC - Channel Channel (ceiling bounce Model) t -2T –T 0 T 2T 3T 4T 5T 6T 7T 8T y(t)y(t) ISI constituent Developed by Carruthers and Kahn - Channel impulse response is fixed for a given position of Tx, Rx and intervening reflectors RMS delay spread H(0) = path loss a = 2H/c, H = height of ceiling above Tx and Rx, c is the speed of light u(t) = unit step function

14. Prof. Z. Ghassemlooy ICEE 2006, Iran 14 OWC – Equalisation  Linear  Lattice  Transversal -Zero Forcing -LMS -Fast RLS -Square-root RLS  Non-Linear  DFE  ML Symbol detector  MLS Linear Equaliser: Traversal filter structure that has a computational complexity which is a linear function of the channel dispersion length.

15. Prof. Z. Ghassemlooy ICEE 2006, Iran 15 OWC - Equalisation – contd. Tx Filter Multipath channel Rx Filter Equaliser C j ykyk  Equaliser output (estimate) Noise n k IkIk  Discrete equivalent of the convolution of the Tx filter, channel and Rx filter with the information sequence I k ISI Noise Where {c j } are the 2K +1 complex-valued tap weight/coefficients of the filter.

16. Prof. Z. Ghassemlooy ICEE 2006, Iran 16 Linear Zero Forcing Equaliser -With a frequency response = h(t) -1. -Able to reduce ISI term at sampling points {q n } is simply the convolution of {c n } and {f n }. SignalISINoise Equaliser with infinite number of taps, tap weights could be selected such that the ISI component is reduced to zero. For practical case: j = k Simple to implement, but not effective with noise. Compensate for the channel distortion at the expense of noise due a large gain in the frequency range where attenuation is high Simple to implement, but not effective with noise. Compensate for the channel distortion at the expense of noise due a large gain in the frequency range where attenuation is high

17. Prof. Z. Ghassemlooy ICEE 2006, Iran 17 Least Mean Square Error Equaliser  Relaxing the zero ISI by selecting C j such that the combined power of the residual ISI and additive noise at the equaliser output is minimised. I.e. minimising the mean error square: The MSE for the equaliser 2K+1 taps is The LMSE solution is obtained by dJ(k)/d{cj}. Autocorrelation matrix cross-correlation vector y T is the transpose of matrix y k-j and I represents the training signal.

18. Prof. Z. Ghassemlooy ICEE 2006, Iran 18 LMSEE – contd.  In contrast to zero-forcing equaliser, the LMSEs solution depend on the statistical properties of the noise as well as the channel induced ISI Autocorrelation matrix cross-correlation vector y T is the transpose of matrix y k-j and I represents the training signal. Where

19. Prof. Z. Ghassemlooy ICEE 2006, Iran 19 Simulation Process Enter R b and D rms 1<D rms <15ns Last ? Stop Out=C m *h k *I + N o Enter No_symb Gen L-DH-PIM Evaluate C m, h k ; No Error = Error + 1 Error = Error Out k = I k ? Last slot ? Last Drms ? SER = Error No of slots Yes No Yes Gen. Rand. {OOK} Start Enter ; SNR -70<<-30 dBm

20. Prof. Z. Ghassemlooy ICEE 2006, Iran 20 Simulation Parameters ParameterValue Average optical power-70 (dBm) ≤ ≤-30 (dBm) Photodetector responsivity R1 Threshold factor0.5 Normalised delay spread D T 0.001 to 1.5 Bit rate R b 1, 10, 50, 100, &150 Mbps Alpha α1 and 2 Number of OOK bits/symbol M3 and 4 Background light current I b 200 µA Number of equaliser filter taps3 No of OOK symbols300,000

21. Prof. Z. Ghassemlooy ICEE 2006, Iran 21 UnequalisedEqualised Results – Eye Diagrams 8-DH-PIM 2 at R b = 100 Mbps, D rms = 15 ns and = -30 dBm.

22. Prof. Z. Ghassemlooy ICEE 2006, Iran 22 Results – Slot Error Rate vs SNR 10 -4 Significant improvement -37.5 and -35.5 dBm of average optical power @ SER of 10 -4

23. Prof. Z. Ghassemlooy ICEE 2006, Iran 23 -46-44-42-40-38-36-34 10 -4 10 -3 10 -2 10 Average optical power requirement (dBm) Probability of slot error (SER) 8-DH-PIM 2 R b =100Mbps; 3-Tap LMSE D rms =2ns D rms =4ns D rms =6ns D rms =10ns D rms =2ns D rms =4ns D rms =6ns D rms =10ns Unequalised Equalised Results – Slot Error Rate vs Avg. Optical Power

24. Prof. Z. Ghassemlooy ICEE 2006, Iran 24 Results – Slot Error Rate vs SNR

25. Prof. Z. Ghassemlooy ICEE 2006, Iran 25 DT = D rms R b Results – Power Penalty vs D T

26. Prof. Z. Ghassemlooy ICEE 2006, Iran 26 Results – Power Penalty vs D T similar performance in very dispersive environment LMSE is better in less dispersive channels (DT < 0.2). LMSE compensates for both dispersion and noise (dominant) DT = D rms R b 3-taps

27.  Employing equalisation in DH-PIM leads to: - Reduced optical power level requirement at high data rate and highly dispersive channels - Improve error performance in a dispersive channel  LMSE offered similar performance to LZEF, at highly dispersive channel, but better performance in less dispersive channels (line of sight)  DH-PIM with equalisation is an attractive modulation scheme for OWC where there is a need for high throughput Concluding Comments

28. Prof. Z. Ghassemlooy ICEE 2006, Iran 28 Thank you!

29. Prof. Z. Ghassemlooy ICEE 2006, Iran 29 OWC – Issues + Solutions  Shadowing in non-line of sight links - Diversity schemes  Limited power (safety reason) - Power efficient modulation techniques  Noise due to the ambient light sources -Optical/electrical filtering -Modulation scheme with no or very little frequency components at the low frequency bands  Dispersion (due to multipath) -Equalisation  SNR variation with the distance and ambient noise