  Learning of the cell-load in radio access networks (RANs) has to be performed
within a short time period. Therefore, we propose a learning framework that is
robust against uncertainties resulting from the need for learning based on a
relatively small training sample set. To this end, we incorporate prior
knowledge about the cell-load in the learning framework. For example, an
inherent property of the cell-load is that it is monotonic in downlink (data)
rates. To obtain additional prior knowledge we first study the feasible rate
region, i.e., the set of all vectors of user rates that can be supported by the
network. We prove that the feasible rate region is compact. Moreover, we show
the existence of a Lipschitz function that maps feasible rate vectors to
cell-load vectors. With these results in hand, we present a learning technique
that guarantees a minimum approximation error in the worst-case scenario by
using prior knowledge and a small training sample set. Simulations in the
network simulator NS3 demonstrate that the proposed method exhibits better
robustness and accuracy than standard multivariate learning techniques,
especially for small training sample sets.
