A Critical Review of Hybrid RF/FSO Convergence Techniques for 5G and 6G Networks
DOI:
https://doi.org/10.70917/ijcisim-2026-2634Keywords:
Free-Space Optics (FSO), Radio Frequency (RF), Hybrid FSO/RF, Convergence, Switching Diversity, Atmospheric Turbulence, 5G, 6GAbstract
Free-Space Optical (FSO) communication offers several advantages, including potentially unlimited bandwidth and the absence of licensing costs.however, it suffers from significant constraints caused by atmospheric turbulence, fog and pointing errors. Hybrid RF/FSOsystems are consideredpromising solutions for the next-generation backhaul and fronthaul networks to achieve "five-nines" (99.999%) uptime. Although many studies have investigated hybrid RF/FSO systems, standardized evaluation metrics and practical experimental validation are still limited. In this paper, we present a critical review of the current state-of-the-art in hybrid RF/FSO convergence by critically evaluating the methodological robustness and practicality of each contribution in this field. In general, we classify convergence architectures in terms of the following three paradigms: conventional hard-switching redundancy, physical-layer soft switching through adaptive combining, and proactive cross-layer optimization schemes. The assessment summarized a significant mismatch between theoretical outage probabilities and field-deployed practices. This key finding, which arises from the log-normal turbulence assumption based on the lack of spatial-temporal correlation, revealed an improvement of up to 15% in link-logarithmic availability under dense urban environments. Additionally, there is a large gap in research focusing on latency overhead and synchronization issues in seamless switching algorithms. Furthermore, this study identifies major unresolved challenges related to latency, synchronization, and realistic channel modeling approaches. The paper concludes with some promising directions for key future work, and in particular a two-fold vision towards (i) building future physics-informed machine learning tools for predictive switching and (ii) Reinventing hybrids in the light of Reconfigurable Intelligent Surfaces (RIS). This paper will be useful to researchers and engineers involved in deploying hybrid FSO/RF systems with such transitions, bridging the gap between controlled laboratory implementations and field reports of actual weather conditions under the requirement of strict operational requirements.