Machine Learning in Optical Access Networks: A Systematic Mapping Study

This paper presents a Systematic Mapping Study of Machine Learning techniques applied to Optical Access Networks, with an emphasis on Passive Optical Networks. From 605 Scopus-indexed publications, 112 primary studies were selected and analyzed according to learning model, application domain, dataset characteristics, validation maturity, reported limitations, and future research directions. The results show that research is strongly concentrated on resource management, particularly Dynamic Bandwidth Allocation, traffic prediction, and performance optimization, whereas security, resilience, and operational robustness remain comparatively less represented. A substantial reproducibility and maturity gap is also identified: only four primary studies (3.57%) use publicly accessible datasets, 94.64% of the corpus remains at the simulation-based or experimental level, and no operational deployment was identified. To complement the systematic mapping, we present an illustrative proof of concept that evaluates ML-based traffic classification as a potential support component for traffic-aware DBA. The originality of this mapping lies in jointly examining ML application domains, dataset accessibility, reproducibility, and technological maturity within the specific context of optical access networks. The findings provide a structured basis for identifying deployment barriers and prioritizing representative datasets, standardized benchmarks, and operational validation in future PON research. Using a controlled synthetic dataset, three supervised classifiers achieved weighted F1-scores close to 0.80, with Random Forest exhibiting the lowest measured inference latency. These results provide descriptive evidence under the evaluated conditions and do not constitute validation of an end-to-end DBA mechanism or an operational PON deployment. Overall, the findings highlight the importance of public and representative datasets, standardized benchmarks, validation under realistic network conditions, lightweight and explainable models, and integration with programmable SDN/NFV-enabled PON control architectures for advancing ML applications toward practical deployment in optical access networks.