Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators

TL;DR

This paper introduces a transformer-based metalearning approach for efficient channel-gain map estimation across diverse environments, significantly reducing measurement requirements by exploiting spatial patterns and physical invariances.

Contribution

It proposes a novel cross-environment transformer estimator that learns shared spatial structures, enabling accurate channel-gain predictions with fewer measurements in new environments.

Findings

Achieves up to five times fewer measurements for accurate CGME.

Outperforms existing methods in numerical experiments.

Leverages physical invariances to improve learning efficiency.

Abstract

Channel-gain maps provide the channel gain between any two locations in a geographical region. They find numerous applications, from resource allocation and interference control to path planning for autonomous vehicles. Channel-gain map estimation (CGME) is considerably more challenging than conventional radio map estimation (RME) because channel-gain maps are functions over a 6-dimensional input space. This calls for specialized methods, which currently rely on the (inaccurate) radio tomographic model or require a prohibitively large number of measurements since they do not exploit any spatial structure. This paper overcomes this issue by leveraging spatial patterns that channel-gain maps exhibit across environments, as dictated by the laws of physics and typical environmental characteristics (e.g. building materials and layouts). Adopting a metalearning perspective, a transformer-based estimator is proposed to implicitly learn this common structure from measurements collected in multiple environments. This enables CGME in new environments from significantly fewer measurements (five times less in our experiments). To maximize learning efficiency, the transformer is composed with a feature map that enforces the invariances of CGME, such as those following from reciprocity. Numerical experiments corroborate the merits of the proposed estimator relative to existing methods.