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S. Rajbhandari, Z. Ghassemlooy, and M. Angelova † Optical Communications Research Group

The Performance of PPM using Neural Network and Symbol Decoding for Diffused Indoor Optical Wireless Links. S. Rajbhandari, Z. Ghassemlooy, and M. Angelova † Optical Communications Research Group † Intelligent Modelling Lab School of Computing, Engineering and Information Sciences

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S. Rajbhandari, Z. Ghassemlooy, and M. Angelova † Optical Communications Research Group

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  1. The Performance of PPM using Neural Network and Symbol Decoding for Diffused Indoor Optical Wireless Links S. Rajbhandari, Z. Ghassemlooy, and M. Angelova † Optical Communications Research Group † Intelligent Modelling Lab School of Computing, Engineering and Information Sciences Northumbria University, Newcastle upon Tyne UK. 1

  2. Why Optical Wireless? • Licence free spectrum • Secure links • Free from electromagnetic interference • Low cost transmitter/receiver • Small cell size • Most importantly potential unlimited bandwidth, 10 millions times that of RF (which could solve the problem of bandwidth congestion in mobile system for an foreseeable future)

  3. Challenges in Indoor Optical Wireless • Strict link set-up for direct line-of-sight links • Shadowing effects • Lack of mobility • Power limitation : due to eye and skin safety • Intersymbol interference due to multipath propagations in diffused links • Intense ambient light noise • Large area photo-detectors - limits the data rate.

  4. Digital Modulation Schemes • On-off Keying (OOK) • Pulse position modulation (PPM) • Subcarrier modulation • Digital pulse interval modulation (DPIM) • Dual-header pulse interval modulation (DH-PIM)

  5. Received signal for non-LOS Links 1.2 1 0.8 0.6 Amplitude 0.4 0.2 0 -0.2 -0.4 0 2 4 6 8 10 Normalized Time Diffuse Links • Multipath path • ISI, which in indoor depends on the room design and size. • Delay spreadDrms is used to approximate the dispersion in optical channel. Rx Tx

  6. Techniques to Mitigate the ISI • Optimal solution - Maximum likelihood sequence detector • Sub-optimal solution - Linear or decision feedback equalizer based on the finite impulse response (FIR) filter - The impulse response of filter c(f)= 1/h(f), where h(f) is the frequency response of channel. - Need to have pre-knowledge of channel - Difficult to realize if channel isnon-linear. - Sometimethe inverse filter may not exist …. What is the alternative?

  7. ANN Based Equalization • Redefine the problem of equalization as geometric classification problem in a complex plane. • Use artificial neural network (ANN) for classification. Threshold Detector Artificial Neural Network Optical Receiver Pattern Classification

  8. Advantages of ANN Equalization • Parallel processing • Universal approximates • No assumptions are made on the channel model or modulation techniques • Adaptive processing • Channel non-linearity: not a problem

  9. f(.) ANN: Basics • Fundamental unit : a neuron • Based on biological neuron • Capability to learn • Output is function of weight inputs and a bias as given by

  10. ANN: Basics • Neuron layers 1 Input layer 1 or more hidden layer(s) 1 output layer • Learning Method Supervised or unsupervised

  11. n(t) X(t) Z(t) Xj M 0 1 0 0 Optical Transmitter PPM Encoder h(t) Optical Receiver Zj Zj Zj-1 . Zj-n . M 0 0 1 0 Yj Matched Filter Decision Device Neural Network PPM Decoder Ts = M/LRb ∑ Proposed System • A feedforward back propagation neural network . • ANN is trained using a training sequence at the operating SNR. • Trained AAN is used for equalization

  12. Impulse Response of Equalized Channel Impulse response of unequalized channel impulse response of equalized channel • Equalized response in a delta function which is equivalent to a impulse response of the ideal channel • Pulse are spread to adjust pulse . • ISI depends on pulse spread

  13. Results and Discussion (1) Slot error rate performance of 8- PPM in diffuse channel with Drms of 5ns at 50 Mbps • Adaptive linear equalizer with least mean square (LMS) algorithm is used. • The performance of ANN equalizer is almost identical to the linear equalizer.

  14. Results and Discussion (2) Slot error rate performance of 8- PPM in diffuse channel with Drms of 5ns at 100 Mbps • Unequalized performance at higher data rate is unacceptable at all SNR range • Linear and neural equalization give almost identical performance.

  15. Further Work • Using ANN as decoder and equalizer to reduce system complexity. • Practical implementation. • ANN with wavelet transform

  16. Conclusions • ANN is an effective equalizer for indoor optical wireless environment. • No need for a prior knowledge of the channel for equalization. • Performance of ANN is identical to or better than the traditional equalizer. • The advantage of ANN over the traditional equalizer is its adaptability .

  17. Acknowledgment • My PhD students, Sujan, Rob, Maryam, Popoola • Northunmbria University for the research funding

  18. Thank you!

  19. Questions and suggestions

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