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

To accommodate the exponential growth of network services in the five-generation (5G) and beyond wireless system, 50 Gb/s/λ 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/λ 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.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.14 July 15 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 4528-4538

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BENTUK GALAKSI
EQUALIZATION
PASSIVE OPTICAL NETWORK (PON)
DIGITAL SIGNAL PROCESSING (DSP)
INTENSITY MODULATION AND DIRECT DETECTION (IMDD)

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3165529

Pernyataan Tanggungjawab

Luyao Huang, Yongxin Xu, Wenqing Jiang, Lei Xue, Weisheng Hu, Lilin Yi

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