  This study presents a spatiotemporal traffic prediction approach for NextG
mobile networks, ensuring the service-level agreements (SLAs) of each network
slice. Our approach is multivariate, multi-step, and spatiotemporal. Leveraging
20 radio access network (RAN) features, peak traffic hour data, and
mobility-based clustering, we propose a parametric SLA-based loss function to
guarantee an SLA violation rate. We focus on single-cell, multi-cell, and
slice-based prediction approaches and present a detailed comparative analysis
of their performances, strengths, and limitations.
  First, we address the application of single-cell and multi-cell training
architectures. While single-cell training offers individual cell-level
prediction, multi-cell training involves training a model using traffic from
multiple cells from the same or different base stations. We show that the
single-cell approach outperforms the multi-cell approach and results in test
loss improvements of 11.4% and 38.1% compared to baseline SLA-based and
MAE-based models, respectively.
  Next, we explore slice-based traffic prediction. We present single-slice and
multi-slice methods for slice-based downlink traffic volume prediction, arguing
that multi-slice prediction offers a more accurate forecast. The slice-based
model we introduce offers substantial test loss improvements of 28.2%, 36.4%,
and 55.6% compared to our cell-based model, the baseline SLA-based model, and
the baseline MAE-based model, respectively.
