Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators
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 map...
| Authors | Prasenjit Dhara, Daniel Romero |
|---|---|
| Primary category | eess.SP |
| Published | 6 May 2026 |
| Last updated at source | 30 August 2026 |
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