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This paper focuses on predicting downlink (DL) traffic volume in mobilenetworks while minimizing overprovisioning and meeting a given service-levelagreement (SLA) violation rate. We present a multivariate, multi-step, andSLA-driven approach that incorporates 20 different radio access network (RAN)features, a custom feature set based on peak traffic hours, and handover-basedclustering to leverage the spatiotemporal effects. In addition, we propose acustom loss function that ensures the SLA violation rate constraint issatisfied while minimizing overprovisioning. We also perform multi-stepprediction up to 24 steps ahead and evaluate performance under both single-stepand multi-step prediction conditions. Our study makes several contributions,including the analysis of RAN features, the custom feature set design, a customloss function, and a parametric method to satisfy SLA constraints.

Multivariate and Multi-step Traffic Prediction for NextG Networks with SLA Violation Constraints