Insufficient problem-solution parts
Gaps in labeled cases make robust learning harder.
Few curated examples linking real faults to best fixes reduce the signal for supervised training and validation.
Strongly contextual
Solutions depend on traffic mix, topology, policy.
Models must encode local operational context (e.g., busy-hour patterns, vendor features, SLAs) to avoid misguiding actions.
Geographical sensitivity
Terrain, climate, and demographics shift the optimum.
Hills, floods, urban canyons, and rural sparsity each alter RF behavior, rollout economics, and maintenance windows.
Knowledge SME dependency
Expert judgement required for edge cases.
SME feedback loops are needed to validate anomalies, tune thresholds, and codify tacit know-how into the model.