Self-learning for received signal strength map reconstruction with neural architecture search
Résumé
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
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Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search.pdf (398.62 Ko)
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