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

• URL: http://arxiv.org/abs/2105.07768v1
• Date: Mon, 17 May 2021 12:19:22 GMT
• Title: Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search
• Authors: Aleksandra Malkova, Loic Pauletto, Christophe Villien, Benoit Denis, Massih-Reza Amini
• Abstract summary: We present a model based on Neural Architecture Search (NAS) and self-learning for received signal strength ( RSS) map reconstruction. The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given ( RSS) map. Experimental results show that signal predictions of this second model outperforms non-learning based state-of-the-art techniques and NN models with no architecture search.
• Score: 63.39818029362661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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