TY  - JOUR
AV  - none
ID  - scholars20184
TI  - Digital Predistortion Based Experimental Evaluation of Optimized Recurrent Neural Network for 5G Analog Radio Over Fiber Links
SP  - 19765
KW  - 5G mobile communication systems; Complex networks; Digital radio; Digital storage; Learning systems; Linearization; Optical fibers; Radio links; Recurrent neural networks
KW  -  5g mobile communication; Digital predistortion; Digital system; Error vector; Error vector magnitude; Fiber nonlinearities; Mobile communications; Optical fiber dispersion; Optical fiber networks; Optical fibers amplifiers; Predistortions; Radio-over-fibers; Training data; Vector magnitude
KW  -  Radio-over-fiber
N1  - cited By 2
N2  - 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. © 2013 IEEE.
Y1  - 2024///
EP  - 19777
VL  - 12
JF  - IEEE Access
A1  - Hadi, M.U.
A1  - Danyaro, K.U.
A1  - Alqushaibi, A.
A1  - Qureshi, R.
A1  - Alam, T.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184322298&doi=10.1109%2fACCESS.2024.3360298&partnerID=40&md5=11a6e47d127b22f3d93058859c012427
ER  -