Digital Predistortion Based Experimental Evaluation of Optimized Recurrent Neural Network for 5G Analog Radio Over Fiber Links

• Published in: IEEE access Vol. 12; pp. 19765 - 19777
• Main Authors: Hadi, Muhammad Usman, Danyaro, Kamaluddeen Usman, AlQushaibi, Alawi, Qureshi, Rizwan, Alam, Tanvir
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Complexity; Costs; Digital predistortion; Digital systems; error vector magnitude; fiber nonlinearity; Linearization; Machine learning; Neural networks; Optical fiber amplifiers; Optical fiber dispersion; Optical fiber networks; Polynomials; Predistortion; Radio; radio over fiber; recurrent neural network; Recurrent neural networks; Remote regions; Signal quality; Signal transmission; Training data; Waveforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3360298

In the context of Enhanced Remote Area Communications (ERAC), Radio over Fiber (RoF) technology plays a crucial role in extending reliable connectivity to underserved and remote areas. This paper explores the significance of fifth-generation (5G) Digital Predistortion (DPD) role in mitigating non-linearities in Radio over Fiber (RoF) systems for enhancing communication capabilities in remote regions. The seamless integration of RoF and 5G technologies requires robust linearization techniques to ensure high-quality signal transmission. In this paper, we propose and exhibit the effectiveness of a machine learning (ML)-based DPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. The study investigates the use of an optimized recurrent neural network (ORNN) based DPD experimentally on a multiband 5G new radio (NR) A-RoF system while maintaining low complexity. The ORNN model is evaluated using flexible-waveform signals at 2.14 GHz and 5G NR signals at 10 GHz transmitted over a 10 km fiber length. The proposed ORNN-based machine learning approach is optimized and is compared with conventional generalized memory polynomial (GMP) model and canonical piecewise linearization (CPWL) methods in terms of Adjacent Channel Power Ratio (ACPR), Error Vector Magnitude (EVM), and in terms of computation complexity including, storage, time and memory consumption. The findings demonstrate that the proposed ORNN model reduces EVM to below 2% as compared to 12% for non-compensated cases while ACPR is reduced by 18 dBc, meeting 3GPP limits.