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 ∼10dB lower compared with the unequalised 'soft' decoding for 16-PPM at a data rate of 155Mbps. Our results indicate that for all range of delay spread, neural network equalisation is an effective tool of mitigating the inter-symbol interference. © 2009 The Institution of Engineering and Technology.