Ubiquitous communication is an emergent feature of the future sixth generation of mobile communication (6G) networks. One of the main challenges of this upcoming mobile network is providing broadband communication wherever connectivity is necessary, including rural areas. Up to now, all previous generations of mobile networks have not satisfactorily accomplished this task, and the high cost of deploying and maintaining complex communication infrastructure in remote areas is one of the main reasons. The centralized radio access network might play an important role in overcoming it, since it enables all baseband centralization in a central office (CO), simplifying network deployment and reducing operating/maintenance costs. Additionally, in this scenario, the radio over fiber (RoF) system might be used for transporting analog signals from the CO to a simplified remote base station, composed only of an optical detector and radio-frequency front-end. However, the Mach–Zehnder modulator and power amplifier, commonly employed in RoF systems, introduce undesired nonlinear effects that can severely degrade overall system performance and prohibitively increase the out-of-band emission. We investigate the use of machine learning (ML) algorithms applied to the linearization of an electrical-amplified RoF system. Particularly, a memory recurrent neural network (RNN) linearization is proposed and compared with a memoryless multilayer perceptron linearization. The root mean square error vector magnitude, normalized mean square error, and adjacent channel leakage ratio metrics have been calculated to evaluate the performance of our ML-based approach. Numerical results demonstrate promising linearization performance when the RNN memory depth is equal to or higher than the amplified-RoF system memory depth.

Luiz Augusto Melo Pereira

Luciano Leonel Mendes

Carmelo José Albanez Bastos Filho

Arismar Cerqueira Sodré

Journal of Optical Communications and Networking

该杂志的范围包括光网络科学、技术和工程的最新进展。鼓励理论贡献（包括新技术、概念、分析和经济研究）和实践贡献（包括光网络实验、原型和新应用）。感兴趣的子领域包括光网络的架构和设计、光网络的生存能力和安全性、软件定义的光网络、弹性光网络、数据和控制平面的进步、网络管理相关的创新以及光接入网络。只有当结果显示直接影响光网络而不是简单的点对点网络时，使能技术及其应用才是合适的主题。

The Journal of Optical Communications and Networking covers the latest advancements in the science, technology, and engineering of optical networks. We encourage contributions in both theoretical aspects, including new technologies, concepts, analyses, and economic studies, as well as practical contributions, such as optical network experiments, prototypes, and new applications. Areas of interest include the architecture and design of optical networks, the resilience and security of optical networks, software-defined optical networks, elastic optical networks, advancements in data and control planes, innovations related to network management, and optical access networks. Relevant technologies and their applications are appropriate topics only when the results demonstrate a direct impact on optical networks rather than merely point-to-point networks.

《Journal of Optical Communications and Networking》涵盖光网络科学、技术和工程的最新进展。我们鼓励理论和实践方面的贡献，包括新技术、概念、分析和经济研究，以及光网络实验、原型和新应用。感兴趣的子领域包括光网络的架构与设计、光网络的生存能力与安全性、软件定义的光网络、弹性光网络、数据与控制平面的进展、网络管理相关的创新以及光接入网络。只有当研究结果对光网络产生直接影响，而不仅仅是点对点网络时，相关技术及其应用才是合适的主题。

Amplified radio-over-fiber system linearization using recurrent neural networks

This work investigates the use of machine learning (ML) algorithms applied to the linearization of an electrical-amplified RoF system, and shows promising linearization performance when the RNN memory depth is equal to or higher than the amplified-RoF system memory depth.

Category	Quartile
计算机科学	2
计算机科学, 计算机硬件	1
计算机科学, 计算机信息系统	1
计算机科学, 光学	2
计算机科学, 电信学	2

Amplified radio-over-fiber system linearization using recurrent neural networks

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