Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

Autor(es)

Pereira, Luiz A. M. ; Mendes, Luciano L. ; Bastos-Filho, Carmelo J. A. ; Cerqueira S, Arismar

• Tipo de documento: Artigo Científico
• Fonte: IEEE Photonics Journal; v. 14, n. 6, p. 10-pg., 2022-12-01.

Resumo

This work proposes a novel machine learning (ML)-based linearization scheme for radio-over-fiber (RoF) systems with external modulation. The proposed approach has the advantage of not requiring new training campaigns in case the Mach-Zehnder modulator (MZM) parameters are changed over time. Our innovative digital pre-distortion (DPD) was designed to favor enhanced remote areas (eRAC) scenarios, in which the non-linearities introduced by the MZM become more severe. It employs a multi-layer perceptron (MLP) artificial neural network (ANN) to model the RoF system and estimate its post-inverse response, which is then applied to the DPD block. We investigate the ML-based DPD performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error (NMSE), resultant signal root mean square error vector magnitude error (EVMams), and complexity. Numerical results demonstrate that the intended DPD method is less complex and outperforms the orthogonal scalar feedback linearization (OSFL) scheme, which has been considered a state-of-the-art DPD technique. The proposal has the potential to effectively and efficiently compensate for the RoF nonlinear distortions, especially in a time-variant system, without needing new training campaigns. (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