Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search

TL;DR

This paper introduces a self-learning neural network approach utilizing neural architecture search for reconstructing received signal strength maps from sparse measurements, outperforming traditional interpolation methods.

Contribution

It proposes a novel self-learning framework with neural architecture search for RSS map reconstruction, addressing the challenge of limited data without augmentation.

Findings

Second model outperforms traditional interpolation methods.

Neural architecture search improves reconstruction accuracy.

Method effective on large-scale RSS measurement maps.

Abstract

In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of sparse single-snapshot input measurements, in the case where data-augmentation by side deterministic simulations cannot be performed. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given (RSS) map. These ground-truth measurements along with the predictions of the model over a set of randomly chosen points are then used to train a second NN model having the same architecture. Experimental results show that signal predictions of this second model outperforms non-learning based interpolation state-of-the-art techniques and NN models with no architecture search on five large-scale maps of RSS measurements.