Low-complexity Volterra-inspired neural network equalizer in 100-G band-limited IMDD PON system

One of the most promising solutions for 100 Gb/s line-rate passive optical networks (PONs) is intensity modulation and direct detection (IMDD) technology together with a digital signal processing-(DSP-) based equalizer for its advantages of system simplicity, cost-effectiveness, and energy-efficiency. However, due to restricted hardware resources, the effec-tive neural network (NN) equalizer and Volterra nonlinear equalizer (VNLE) have the drawback of high implementa-tion complexity. In this paper, we incorporate an NN with the physical principles of a VNLE to construct a white-box low-complexity Volterra-inspired neural network (VINN) equalizer. This equalizer has better performance than a VNLE at the same complexity and attains similar perfor-mance with much lower complexity than a VNLE with optimized structural hyperparameter. The effectiveness of the proposed equalizer is verified in 1310 nm band-limited IMDD PON systems. A 30.5-dB power budget is achieved with the 10-G-class transmitter.(c) 2022 Optica Publishing Group

Automated summary

This paper presents a novel low-complexity Volterra-inspired neural network (VINN) equalizer, which outperforms traditional Volterra nonlinear equalizer (VNLE) technology in terms of system simplicity and performance. The effectiveness of the proposed equalizer is verified through implementation in a 1310 nm band-limited IMDD PON system.