Abstract:Transmitter-resolved radio map estimation (RME) from sparse measurements is essential for obtaining source-specific received-power information in wireless networks. This paper proposes SeaGAT, a Structured Edge-Aware Graph Attention Network for transmitter-resolved pointwise RME. For each target--transmitter query, SeaGAT constructs a target-centered graph from a bounded transmitter-specific reference set. Reference representations generate messages and provide measurement context to attention scoring, whereas structured edge representations encode explicit query--reference relations and enter only the scoring procedure. Within a fixed sampled support, we derive a differential identity that decomposes local variation of the evidence aggregate into attention-weighted message changes and score-induced weight redistribution; edge-relation perturbations act only through the latter, whereas a reference-RSS perturbation can affect both pathways. The bounded star graph yields graph-side computation linear in the selected reference count $K$, while support-truncation analysis bounds deviation from the same-parameter full-support aggregate by the product of omitted attention mass and cross-support message diameter. Extensive computer simulations evaluated with ray-tracing dataset show that SeaGAT achieves the lowest mean RMSE compared with baselines; replacing learned attention with uniform weights increases RMSE by $1.58$--$2.14$~dB. It also maintains stable transfer across the tested urban-layout and carrier-frequency shifts.
From: Ang Li [view email]
[v1]
Sun, 19 Apr 2026 12:44:29 UTC (2,381 KB)
[v2]
Tue, 1 Sep 2026 05:55:56 UTC (2,748 KB)
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