# Robust Cell-Load Learning with a Small Sample Set

arXiv: https://arxiv.org/abs/2103.11467 · HTML: https://mlsanity.com/paper/2103.11467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 54 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Online Isotonic Regression](https://mlsanity.com/paper/1603.04190/) (2016)
- [Connections between spectral properties of asymptotic mappings and solutions to wireless network problems](https://mlsanity.com/paper/1903.09793/) (2019)
- [Elementary Properties of Positive Concave Mappings with Applications to Network Planning and Optimization](https://mlsanity.com/paper/1505.03006/) (2015)
- [Data Offloading in Load Coupled Networks: A Utility Maximization Framework](https://mlsanity.com/paper/1312.6918/) (2013)
- [Traffic Demand-Aware Topology Control for Enhanced Energy-Efficiency of Cellular Networks](https://mlsanity.com/paper/1503.08627/) (2015)

## Abstract (arXiv)

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.