Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search
This work addresses a domain-specific challenge in signal processing for wireless communication, offering an incremental improvement over existing methods.
The paper tackles the problem of reconstructing received signal strength maps from sparse measurements without data augmentation, using a neural architecture search and self-learning approach, achieving results that outperform non-learning interpolation and standard neural networks on five large-scale maps.
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.