Robust Cell-Load Learning with a Small Sample Set
This work addresses the challenge of efficient cell-load estimation for radio access networks, but it is incremental as it builds on existing learning frameworks with added robustness features.
The paper tackles the problem of learning cell-load in radio access networks with limited training data by incorporating prior knowledge, such as monotonicity and properties of the feasible rate region, to ensure robustness. Simulations show the method achieves better accuracy and robustness than standard techniques, particularly with small sample sets.
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.