Data quality improvement through data cleaning and augmentation methods

Abstract

Sixth-generation (6G) wireless systems depend on data-hungry machine-learning pipelines, yet datasets collected from heterogeneous sources frequently contain missing values that bias models and degrade simulation reliability. Tabular imputation has been studied extensively— from statistical baselines (mean, kNN) through model-based methods (MICE, SoftImpute) to recent deep approaches (HyperImpute, GRAPE, DiffPuter)—but no prior work systematically compares this range on 6G data under realistic missingness. We benchmark seven methods on DeepSense 6G datasets across four mechanisms and three missingness rates, evaluating reconstruction accuracy, statistical fidelity, and downstream beam-prediction performance. Our benchmarks show that no single imputation method consistently dominates; performance depends on the missingness mechanism. Under cell-wise missingness, deep methods such as HyperImpute achieve the highest reconstruction fidelity, though downstream beam prediction remains robust to these localised corruptions. In contrast, row-wise missingness degrades all learned and deep approaches by breaking cross-feature dependencies. Here, kNN is the only method that consistently preserves the downstream label signal. Overall, our results provide guidance for 6G pipeline defaults and highlight the limitations of applying purely tabular imputation to temporal wireless data.