Machine Learning-Based Digital Pre-Distortion Scheme for RoF Systems and Experimental 5G mm-waves Fiber-Wireless Implementation

Autor(es)

Luiz A. M. Pereira ^[1] ; Eduardo S. Lima ^[2] ; Luciano L. Mendes ^[3] ; Arismar Cerqueira S. Jr. ^[4]

Afiliação do(s) autor(es)

^[1] National Institute of Telecommunications - Brasil

^[2] National Institute of Telecommunications - Brasil

^[3] National Institute of Telecommunications - Brasil

^[4] National Institute of Telecommunications - Brasil

• Tipo de documento: Artigo Científico
• Fonte: J. Microw. Optoelectron. Electromagn. Appl.; v. 22, n. 1, p. 172-183, 2023-03-13.

Resumo

Abstract The advent of the 5th generation of mobile networks brought a large number of new use case and applications to be supported by the physical layer (PHY), which must be more flexible than all previous radio access networks (RAN). The concept of the centralized RAN (C-RAN) allows all the baseband processing to be performed in the central office, simplifying the network deployment and also allowing the operators to dynamically control the PHY according with the applications requirements. The radio-frequency (RF) signal generated by the C-RAN can be transported to the remote radio unit (RRU) by using a radio over fiber (RoF) system. In this paper, we propose two RoF approaches for composing the transport and access networks of the next-generation systems. The first investigation relies on the implementation of a machine learning-based digital pre-distortion (DPD), designed for RoF systems. In the second approach, we implement an RoF system and characterize the optical and electrical power levels aiming to reduce the RoF non-linear distortions. The overall link performance is evaluated by measuring the error vector magnitude (EVMRMS) and 590 Mbit/s is achieved with EVMRMS as low as 4.4% in a 10 m reach cell. (AU)

• Processo FAPESP: 20/05127-2 - SAMURAI: núcleo 5G inteligente e integração de múltiplas redes de acesso
• Beneficiário: Aldebaro Barreto da Rocha Klautau Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático