Benchmarking Multivariate Time-Series Imputation in 6G Networks

Abstract

Sixth-Generation (6G) telecommunications rely on high-frequency millimeter-wave (mmWave) bands for massive data rates, but their physical fragility makes them highly susceptible to line-of-sight blockages. These blockages cause contiguous telemetry outages, creating a single point of failure for edge routing and orchestration protocols demanding continuous system data. To address this, we introduce an evaluation pipeline benchmarking five time-series imputation architectures, from statistical baselines (Nearest Neighbor, Kalman Filter) to complex deep learning models (BRITS, CSDI, TimesNet). Utilizing an open-source microservice dataset, the pipeline dynamically injects simulated blockages across a 24-scenario grid, escalating from minor drops to 60-second outages. Performance is evaluated across an accuracy-latency Pareto frontier. Results demonstrate that the recurrent architecture, BRITS, achieves the highest overall reconstruction fidelity. However, Nearest Neighbor emerges as the optimal low-latency baseline, maintaining competitive accuracy while consistently executing in under 250 milliseconds. Finally, contextualizing these findings reveals a critical limitation: the architectures achieving peak accuracy inherently rely on offline, bidirectional processing to reconcile telemetry gaps. This highlights a significant research opportunity, emphasizing the need to evaluate deep learning models in strictly online, forward-only forecasting configurations to meet the split-second streaming realities of live 6G edge deployment.