From Data to Deployment: A Scoping Review of Empirical Tools, Techniques, and Operational Readiness in Data-Driven 5G/6G Network Optimization

Abstract

Artificial intelligence (AI) is increasingly applied to 5G/6G network optimization, yet differences in empirical evidence, evaluation methods, and computational requirements complicate comparisons of reported performance. This scop ing review synthesizes 72 peer-reviewed journal studies pub lished between 2022 and 2026, selected through a PRISMA ScR-informed process from 893 records. The review examines empirical tools, AI/ML techniques, optimization domains, and operational readiness. Evidence classification identifies 37 studies (51.4%) using real-world or physical-testbed evidence only, 25 (34.7%) combining measured and simulated evidence, and 10 (13.9%) relying exclusively on synthetic or simulated data. Offline analysis was the most common evaluation setting, accounting for 45 studies (62.5%). Three principal findings emerge. First, empirical measurements improve contextual relevance, while simulation and digital twins support scalable experimentation but introduce validation challenges. Second, algorithmic effectiveness depends on the optimization objective, data characteristics, and computational constraints rather than model complexity alone. Third, predictive accuracy is more consistently evaluated than the network-level effects of optimization decisions. The review there fore introduces the six-module Evidence Fidelity and Operational Reporting (EFOR) checklist to improve evaluation consistency, reporting transparency, and operational relevance.