Spatial Transformers for Radio Map Estimation
This work addresses the limitations of poor spatial resolution and high parameter counts in radio map estimation for cellular network operators, offering a novel solution with practical applications in minimization of drive tests.
The paper tackles the problem of radio map estimation by introducing Spatial TransfOrmer for Radio Map estimation (STORM), an attention-based estimator that outperforms existing methods with lower computational complexity, translation and rotation equivariance, and full spatial resolution, and extends it for active sensing to optimize measurement collection in cellular networks.
Radio map estimation (RME) involves spatial interpolation of radio measurements to predict metrics such as the received signal strength at locations where no measurements were collected. The most popular estimators nowadays project the measurement locations to a regular grid and complete the resulting measurement tensor with a convolutional deep neural network. Unfortunately, these approaches suffer from poor spatial resolution and require a great number of parameters. The first contribution of this paper addresses these limitations by means of an attention-based estimator named Spatial TransfOrmer for Radio Map estimation (STORM). This scheme not only outperforms the existing estimators, but also exhibits lower computational complexity, translation equivariance, rotation equivariance, and full spatial resolution. The second contribution is an extended transformer architecture that allows STORM to perform active sensing, by which the next measurement location is selected based on the previous measurements. This is particularly useful for minimization of drive tests (MDT) in cellular networks, where operators request user equipment to collect measurements. Finally, STORM is extensively validated by experiments with one ray-tracing and two real-measurement datasets.