Channel optimization in mode division multiplexing using neural networks

Mode division multiplexing (MDM) has emerged as a new multiplexing paradigm for enhancing the bandwidth by leveraging the orthogonal modes as a parallel channel for transferring information. Although capacity gains theoretically increase in relation to the number of modes in MDM, mode coupling inevitably causes modes to interchange power randomly, leading to channel degradation from different arrival mode delay and inter-symbol interference (ISI). Hence, this paper demonstrates a new neural network feed-forward and back propagation equalizer to mitigate pulse broadening caused by mode-coupling.