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Open AccessArticle

Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes

1
Department of Computer Science and Industry, Faculty of Engineering Science, Universidad Católica del Maule, Talca 3480112, Chile
2
Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile
3
Department of Electricity, Universidad Tecnológica Metropolitana, Santiago 7800002, Chile
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2019, 8(9), 921; https://doi.org/10.3390/electronics8090921
Received: 7 July 2019 / Revised: 11 August 2019 / Accepted: 14 August 2019 / Published: 22 August 2019
(This article belongs to the Section Microwave and Wireless Communications)
Radio-over-fiber (RoF) orthogonal frequency division multiplexing (OFDM) systems have been revealed as the solution to support secure, cost-effective, and high-capacity wireless access for the future telecommunication systems. Unfortunately, the bandwidth-distance product in these schemes is mainly limited by phase noise that comes from the laser linewidth, as well as the chromatic fiber dispersion. On the other hand, the single-hidden layer feedforward neural network subject to the extreme learning machine (ELM) algorithm has been widely studied in regression and classification problems for different research fields, because of its good generalization performance and extremely fast learning speed. In this work, ELMs in the real and complex domains for direct-detection OFDM-based RoF schemes are proposed for the first time. These artificial neural networks are based on the use of pilot subcarriers as training samples and data subcarriers as testing samples, and consequently, their learning stages occur in real-time without decreasing the effective transmission rate. Regarding the feasible pilot-assisted equalization method, the effectiveness and simplicity of the ELM algorithm in the complex domain are highlighted by evaluation of a QPSK-OFDM signal over an additive white Gaussian noise channel at diverse laser linewidths and chromatic fiber dispersion effects and taking into account several OFDM symbol periods. Considering diverse relationships between the fiber transmission distance and the radio frequency (for practical design purposes) and the duration of a single OFDM symbol equal to 64 ns, the fully-complex ELM followed by the real ELM outperform the pilot-based correction channel in terms of the system performance tolerance against the signal-to-noise ratio and the laser linewidth. View Full-Text
Keywords: extreme learning machines; orthogonal frequency division multiplexing; phase noise; radio over fiber systems extreme learning machines; orthogonal frequency division multiplexing; phase noise; radio over fiber systems
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Zabala-Blanco, D.; Mora, M.; Azurdia-Meza, C.A.; Dehghan Firoozabadi, A. Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes. Electronics 2019, 8, 921.

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