Learning-based nonlinear compensation with physics-informed neural network for data access

Abstract

A system and a computer-implemented method using physics-informed neural network (PINN) are provided for data communications. At a transmitter, the method is configured to acquire source data to be transmitted, encode the source data to codewords based on forward error correction (FEC) codes, map the codewords to amplitude symbols, modify the mapped amplitude symbols to pre-equalized symbols including pre-distort channel impairments symbols based on a predetermined physical model, and transmit digital data of the pre-equalized symbols over a channel as channel data. At a receiver, the method is configured to receive and demodulate the channel data from the channel to produce an initial estimate of bits of the received channel data, mitigate channel noise and waveform distortions in the channel data based on the initial estimate, convert the channel data consisting of shaped non-uniform symbols into a deshaped bit sequence as a uniform data sequence, decode the deshaped bit sequence to correct residual errors in the converted channel data, and store the corrected channel data to a data sink.