Experimental Validation of Nonlinearity Mitigation in Analog Radio over Fiber for upcoming 6G Networks Using MSA-Based Digital Predistortion

Muhammad Usman Hadi, Abbas Shah, Muhammad Ijaz

Research output: Contribution to conferencePaperpeer-review

Abstract

Analog Radio over Fiber (A-RoF) technology is a pivotal enabler for 5G and 6G networks due to its ability to support high data rates, ultra-low latency, and seamless connectivity by providing a cost-effective optical fronthaul solution. However, the nonlinearities inherent in A-RoF systems, including signal distortions caused by optical and electrical components, limit performance, particularly in high-frequency applications. In this study, we present an experimental setup integrating Digital Predistortion (DPD) with enhanced magnitude-selective affine (MSA) method, implemented in a hybrid fiber-wireless (Fi-Wi) architecture. This setup is tested in a supercell and femtocell scenario, using RF signals at 3 GHz and 10 GHz, transmitted over a 10 km standard single-mode fiber (SSMF) link. A 1550 nm laser-driven dual-drive Mach Zehnder Modulator (DD-MZM) is used, and post-processing includes predistortion with the MSA method to mitigate nonlinearities. Compared to traditional methods such as GMP and CPWL, our MSA method achieves the lowest Error Vector Magnitude (EVM) at 1.84% and the highest spectral regrowth suppression at -44 dBc compared to the EVM of GMP at 5.4% and CPWL at 1.85% thereby, demonstrating enhanced efficiency and robustness. Future work will focus on optimizing computational complexity and further improving system performance through a hybrid MSA approach.
Original languageEnglish
Pages1-5
DOIs
Publication statusPublished online - 16 May 2025
Event2024 26th International Multitopic Conference (INMIC) - Karachi, Pakistan
Duration: 30 Dec 202431 Dec 2024

Conference

Conference2024 26th International Multitopic Conference (INMIC)
Period30/12/2431/12/24

Keywords

  • Analog Radio over Fiber
  • 5G
  • 6G
  • Digital Predistortion
  • Magnitude Selective Affine

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