Delay-Tolerant Indoor Optical Wireless Communication Systems Based on Attention-Augmented Recurrent Neural Network

Abstract

The optical wireless communication (OWC) system has been widely studied as a promising candidate for indoor high-speed wireless communications. In particular, the spatial diversity scheme has shown to be effective to improve the performance of OWC systems. However, the delay amongst multiple channels in this scheme may result in severe inter-symbol-interference (ISI) and degradations of the system performance. Recent studies have shown that the symbol decision schemes based on recurrent neural networks (RNNs) can mitigate the impact of delays. However, their performances are limited when the channel delays are long. In this paper, we propose a delay-tolerant indoor OWC system, which utilizes an attention-augmented long-short term memory (ALSTM) RNN decision scheme to handle long channel delays. A 10 Gb/s repetition-coded indoor OWC system with the proposed ALSTM RNN decision scheme is experimentally demonstrated. Compared with traditional OWC systems which add cyclic prefix (CP) /zero-postfix (ZP) to combat the impact of channel delays, the proposed system does not reduce the effective data rate or throughput. Compared with the previous study which adopts the vanilla RNN decision scheme, the proposed ALSTM RNN decision scheme can better learn dependencies amongst received symbols with the help of attention mechanism, especially amongst non-neighboring symbols. Hence, it is more robust against the ISI induced by long channel delays. Experimental results show that compared with the previously studied vanilla RNN, over one-order-of-magnitude bit-error-rate improvement is achieved when the channel delay is more than 4.7-symbol-period.