4.7 Article

Performance and Complexity Analysis of Conventional and Deep Learning Equalizers for the High-Speed IMDD PON

Journal

JOURNAL OF LIGHTWAVE TECHNOLOGY
Volume 40, Issue 14, Pages 4528-4538

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2022.3165529

Keywords

Decision feedback equalizers; Symbols; Artificial neural networks; Neurons; Passive optical networks; Deep learning; Feedforward systems; Deep learning; digital signal processing (DSP); equalization; intensity modulation and direct detection (IMDD); passive optical network (PON)

Funding

  1. National Key R&D Program of China [2019YFB1803803]
  2. National Natural Science Foundation of China [62025503]

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This paper develops a 50 Gb/s/lambda passive optical network (PON) for mobile xhaul applications in order to accommodate the exponential growth of network services in the 5G and beyond wireless system. The study compares different equalizers in terms of performance, complexity, optimization difficulty, and generalization ability, and finds that the Volterra DFE equalizer is the most efficient option. It enables C-band 50 Gb/s PAM-4 signal transmission in a 10G optics based IMDD system with a 3 dB bandwidth of 6.11 GHz and a power budget up to 38 dB.
To accommodate the exponential growth of network services in the five-generation (5G) and beyond wireless system, 50 Gb/s/lambda passive optical network (PON) is developed for mobile xhaul applications. Intensity modulation and direct detection (IMDD) technology together with digital signal procession (DSP) is being considered as the promising solution for 50 Gb/s/lambda PON due to its low cost, low power consumption, and compact footprint. Different DSP algorithms with varied structures are proposed for linear and nonlinear impairments compensation in the high-speed PON, while the performance and complexity analysis of these algorithms is still missing. To find the most efficient equalizers, in this paper, four conventional equalizers, including feed-forward equalizer, decision feedback equalizer (DFE), Volterra equalizer (Vol) and Volterra DFE equalizer (Vol-DFE), together with two deep learning equalizers namely fully-connected neural network, and long short-term memory equalizer are experimentally compared in a 10G optics based 50G-PON system in terms of the equalization performance, computation complexity, optimization difficulty, and generalization ability. After the evaluation of our proposed fair comparison algorithm, we consider Vol-DFE is the most efficient one considering both performance and complexity. Attributes to the strong and efficient equalization capability of Vol-DFE, C-band 50 Gb/s PAM-4 signal transmission can be supported in a 10G optics based IMDD system with a 3 dB bandwidth of 6.11 GHz, and a power budget up to 38 dB can be achieved.

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