Data-Driven Spectrum Cartography via Deep Completion Autoencoders

TL;DR

This paper introduces a novel data-driven method for spectrum cartography using deep completion autoencoders, enabling accurate RF spectrum mapping with fewer measurements by learning propagation phenomena from data.

Contribution

It is the first to apply deep neural networks to spectrum cartography, leveraging autoencoders to learn spatial propagation structures from data, reducing measurement requirements.

Findings

Achieves accurate spectrum maps with fewer measurements.

First application of deep learning to spectrum cartography.

Demonstrates the effectiveness of autoencoders in RF mapping.

Abstract

Spectrum maps, which provide RF spectrum metrics such as power spectral density for every location in a geographic area, find numerous applications in wireless communications such as interference control, spectrum management, resource allocation, and network planning to name a few. Spectrum cartography techniques construct these maps from a collection of measurements collected by spatially distributed sensors. Due to the nature of the propagation of electromagnetic waves, spectrum maps are complicated functions of the spatial coordinates. For this reason, model-free approaches have been preferred. However, all existing schemes rely on some interpolation algorithm unable to learn from data. This work proposes a novel approach to spectrum cartography where propagation phenomena are learned from data. The resulting algorithms can therefore construct a spectrum map from a significantly smaller number of measurements than existing schemes since the spatial structure of shadowing and other phenomena is previously learned from maps in other environments. Besides the aforementioned new paradigm, this is also the first work to perform spectrum cartography with deep neural networks. To exploit the manifold structure of spectrum maps, a deep network architecture is proposed based on completion autoencoders.