Experimental Demonstration of Nonlinear Frequency Division Multiplexing Transmission With Neural Network Receiver

Nonlinear frequency division multiplexing (NFDM) communication systems that are based on the nonlinear Fourier transform (NFT), have seen a rapid improvement in performance and transmission reach over just a few years. However, such an improvement is now being slowed down by fundamental challenges such as fiber loss and noise. As the NFT theory is defined over a lossless transmission fiber, a strong research focus has been dedicated to either improve the lossless assumption for practical fibers, by adapting the theory to approximately account for the fiber loss, or by devising encoding schemes that increase the robustness of the NFT to the fiber attenuation. However, the proposed solutions provide only minimal benefits to the system performance, especially for long fiber spans as in deployed links. Alternatively, a detection strategy based on replacing a conventional NFT receiver with a time-domain Neural network (NN)-based symbol decisor has been numerically proposed. Here, we extend such an idea by validating it experimentally. In order to apply the method in an experimental environment, the impact of phase noise, and receiver frequency offset needs to be addressed. We, therefore, propose a novel time-domain receiver architecture that combines a two-stage iterative carrier recovery with a NN-based symbol decisor. The carrier recovery, itself based on aNN for phase estimation, is numerically, and experimentally characterized. The proposed receiver has been evaluated for single-polarization two-eigenvalue transmission at 1 GBd. A two-fold increase in the transmission reach is enabled by the NN receiver (≈1600 km) compared to a conventional NFT receiver (≈560 km) for a practical link using 80-km spaced erbium-doped fiber amplifier (EDFAs).