Self-learning for received signal strength map reconstruction with neural architecture search

Aleksandra Malkova (1, 2, 3) , Loïc Pauletto (1, 3) , Christophe Villien (2) , Benoît Denis (2) , Massih-Reza Amini (1, 3)

1 LIG - Laboratoire d'Informatique de Grenoble
2 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information
3 APTIKAL - Algorithms, Principles and TheorIes for collaborative Knowledge acquisition And Learning

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.

Mots clés: Neural Networks (NN); Neural Architecture Search (NAS); Received Signal Strength (RSS); Radio Mapping; Radio Cartography; Map Interpolation; Internet of Things (IoT); Long Range Wide Area Networks (LoRaWAN); Self-learning; Neural Architecture Search Self-learning Received Signal Strength Radio Mapping; Neural Architecture Search; Received Signal Strength

Domaines: Traitement du signal et de l'image [eess.SP]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Réseau de neurones [cs.NE]; Réseaux et télécommunications [cs.NI]

Aleksandra Malkova, Loïc Pauletto, Christophe Villien, Benoît Denis, Massih-Reza Amini. Self-learning for received signal strength map reconstruction with neural architecture search. Lecture Notes in Computer Science, 12895, pp.515-526, 2021, ⟨10.1007/978-3-030-86383-8_41⟩. ⟨cea-04571066⟩