Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning

TL;DR

This paper introduces a geometry model-assisted deep learning approach that jointly reconstructs radio maps and virtual environments using RSS data, effectively capturing diffraction and scattering effects without relying on updated city maps.

Contribution

It develops a virtual obstacle model and a diffraction feature extraction method, improving radio map accuracy and reducing data and training requirements compared to existing methods.

Findings

Achieves 10%-18% higher accuracy in radio map reconstruction.

Reduces data requirements by 20% and training epochs by 50%.

Successfully reconstructs 3D virtual environments.

Abstract

Machine learning (ML) facilitates rapid channel modeling for 5G and beyond wireless communication systems. Many existing ML techniques utilize a city map to construct the radio map; however, an updated city map may not always be available. This paper proposes to employ the received signal strength (RSS) data to jointly construct the radio map and the virtual environment by exploiting the geometry structure of the environment. In contrast to many existing ML approaches that lack of an environment model, we develop a virtual obstacle model and characterize the geometry relation between the propagation paths and the virtual obstacles. A multi-screen knife-edge model is adopted to extract the key diffraction features, and these features are fed into a neural network (NN) for diffraction representation. To describe the scattering, as oppose to most existing methods that directly input an entire city map, our model focuses on the geometry structure from the local area surrounding the TX-RX pair and the spatial invariance of such local geometry structure is exploited. Numerical experiments demonstrate that, in addition to reconstructing a 3D virtual environment, the proposed model outperforms the state-of-the-art methods in radio map construction with 10%-18% accuracy improvements. It can also reduce 20% data and 50% training epochs when transferred to a new environment.