High-Speed Adaptive MIMO-VLC System With Neural Network

Rooms are typically lit with multiple luminaires, which open the possibility of creating Multiple-Input Multiple-Output (MIMO) Visible Light Communications (VLC) systems. At its most complex, the luminaires in the system might transmit different data at different power levels to a terminal containing multiple receivers, allowing a substantial increase in data rates. However, crosstalk between the transmitted channels, dependent on the location and orientation of the receiver, may cause the best strategy to be to group transmitters together and transmit the same data stream. In this paper, we report a transmitter coordination algorithm determining how to use the transmitters optimally as the receiver location varies. The data rate using this approach is on average 41 per cent higher than the conventional spatial multiplexing approach. Neural networks are then employed in the coordination algorithm. It increases the speed of operation by a factor of four compared to the initial coordination algorithm while achieving the same level of performance. The neural network also gives good performance for room geometries and receiver orientations outside the scope of the training data. Finally, the neural network is benchmarked against a water-filling algorithm approach with maximum system capacity. The neural network shows a factor of three speed-up in computing time with only a 12 per cent reduction in the average data rate. These results show the potential of the approach to achieve a near-maximal data rate with a straightforward and efficient channel selection technique.