Constructing 4D Radio Map in LEO Satellite Networks with Limited Samples

TL;DR

This paper introduces DeepRM, a deep unsupervised learning framework that reconstructs 4D radio maps in LEO satellite networks using limited samples, combining neural compressive sensing and tensor decomposition to enable spectrum monitoring with fewer sensors.

Contribution

The paper presents a novel deep unsupervised learning approach, DeepRM, that reconstructs 4D radio maps with limited samples, reducing the need for extensive sensor deployment and high-speed converters.

Findings

DeepRM outperforms state-of-the-art baselines in error reduction.

Effective reconstruction of 4D radio maps with sparse sensor deployment.

Demonstrates feasibility of spectrum monitoring in LEO networks with limited data.

Abstract

Recently, Low Earth Orbit (LEO) satellite networks (i.e., non-terrestrial network (NTN)), such as Starlink, have been successfully deployed to provide broader coverage than terrestrial networks (TN). Due to limited spectrum resources, TN and NTN may soon share the same spectrum. Therefore, fine-grained spectrum monitoring is crucial for spectrum sharing and interference avoidance. To this end, constructing a 4D radio map (RM) including three spatial dimensions and signal spectra is important. However, this requires the large deployment of sensors, and high-speed analog-to-digital converters for extensive spatial signal collection and wide power spectrum acquisition, respectively. To address these challenges, we propose a deep unsupervised learning framework without ground truths labeling requirement, DeepRM, comprised of neural compressive sensing (CS) and tensor decomposition (TD) algorithms. Firstly, we map the CS process into the optimization of a neural networksassociated loss function, and design a sparsity-performance balance training algorithm to reconstruct a wide power spectrum under limited sub-Nquist samples. Secondly, according to the output of neural CS algorithm, we also utilize neural networks to perform TD, and construct the 3D RM for each frequency, even under very sparse sensor deployment. Extensive evaluations show that DeepRM achieves lower error than its corresponding state-of-the-art baselines, especially with limited samples.