Wavelet—Artificial Neural Network Receiver for Indoor Optical Wireless Communications

The multipath induced intersymbol interference (ISI) and fluorescent light interference (FLI) are the two most important system impairments that affect the performance of indoor optical wireless communication (OWC) systems. The presence of either incurs a high optical power penalty (OPP) and hence the interferences should be mitigated with suitable techniques to ensure optimum system performance. The discrete wavelet transform (DWT) and the artificial neural network (ANN) based receiver to mitigate the effect of FLI and ISI has been proposed in the previous study for the one-off keying (OOK) modulation scheme. It offers performance improvement compared to the traditional methods of employing a high pass filter (HPF) and a finite impulse response (FIR) equalizer. In this paper, the investigation of the DWT-ANN based receiver for baseband modulation techniques including OOK, pulse position modulation (PPM) and digital pulse interval modulation (DPIM) are reported. The proposed system is implemented using digital signal processing (DSP) board and results are verified by comparison with simulation data

Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation

The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported. Channel equalisation is achieved by training a multilayer perceptrons ANN. A comparative study of the unequalised `soft' decision decoding and the `hard' decision decoding along with the neural equalised `soft' decision decoding is presented for different bit resolutions for optical channels with different delay spread. We show that the unequalised `hard' decision decoding performs the worst for all values of normalised delayed spread, becoming impractical beyond a normalised delayed spread of 0.6. However, `soft' decision decoding with/without equalisation displays relatively improved performance for all values of the delay spread. The study shows that for a highly diffuse channel, the signal-to-noise ratio requirement to achieve a BER of 10−5 for the ANN-based equaliser is ~10 dB lower compared with the unequalised `soft' decoding for 16-PPM at a data rate of 155 Mbps. Our results indicate that for all range of delay spread, neural network equalisation is an effective tool of mitigating the inter-symbol interference