A Unified Prediction Framework for Signal Maps

TL;DR

This paper introduces a unified framework for predicting cellular signal maps from limited measurements, combining quality-of-service, importance weighting, and Data Shapley methods to enhance accuracy and reduce data requirements.

Contribution

It proposes a novel integrated approach that improves cellular signal map predictions by incorporating quality functions, importance sampling, and Data Shapley valuation.

Findings

Up to 27% MSE reduction in low signal areas

20% improvement in spatial prediction accuracy

Enhanced prediction recall from 64% to 94% for coverage loss

Abstract

Signal maps are essential for the planning and operation of cellular networks. However, the measurements needed to create such maps are expensive, often biased, not always reflecting the metrics of interest, and posing privacy risks. In this paper, we develop a unified framework for predicting cellular signal maps from limited measurements. Our framework builds on a state-of-the-art random-forest predictor, or any other base predictor. We propose and combine three mechanisms that deal with the fact that not all measurements are equally important for a particular prediction task. First, we design quality-of-service functions ($Q$), including signal strength (RSRP) but also other metrics of interest to operators, i.e., coverage and call drop probability. By implicitly altering the loss function employed in learning, quality functions can also improve prediction for RSRP itself where it matters (e.g., MSE reduction up to 27% in the low signal strength regime, where errors are critical). Second, we introduce weight functions ($W$) to specify the relative importance of prediction at different locations and other parts of the feature space. We propose re-weighting based on importance sampling to obtain unbiased estimators when the sampling and target distributions are different. This yields improvements up to 20% for targets based on spatially uniform loss or losses based on user population density. Third, we apply the Data Shapley framework for the first time in this context: to assign values ($\phi$) to individual measurement points, which capture the importance of their contribution to the prediction task. This improves prediction (e.g., from 64% to 94% in recall for coverage loss) by removing points with negative values, and can also enable data minimization. We evaluate our methods and demonstrate significant improvement in prediction performance, using several real-world datasets.