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

Autor:	Loïc Pauletto, Massih-Reza Amini, Benoit Denis, Aleksandra Malkova, Christophe Villien
Rok vydání:	2021
Předmět:	Set (abstract data type) Signal strength Artificial neural network Computer science RSS Deterministic simulation computer.file_format Architecture Algorithm Signal computer Interpolation
Zdroj:	Lecture Notes in Computer Science ISBN: 9783030863821 ICANN (5)
DOI:	10.1007/978-3-030-86383-8_41
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a9ab818634882e2c868ce17b2e36c53f https://doi.org/10.1007/978-3-030-86383-8_41 Zobrazit plný text záznamu