pith:ZJA3GKGX
GeoGS-CE: Learning Delay--Beam Channel Priors with 3D Gaussians for High-Mobility Scenarios
A 3D Gaussian model of scene geometry supplies a stable delay-beam prior that reconstructs full channel responses from sparse pilots in high-mobility settings.
arxiv:2605.16094 v1 · 2026-05-15 · cs.IT · cs.AI · math.IT
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Record completeness
Claims
The geometric prior substantially improves CFR reconstruction over pilot-only and non-geometric baselines in simulations based on channels generated from a segment of the Guangshen high-speed railway.
High-mobility environments such as high-speed railways exhibit scheduled trajectories, predictable velocities, and a limited number of dominant propagation paths that induce a delay-beam power spectrum more stable than the instantaneous CFR.
GeoGS-CE models NLoS scattering with 3D Gaussians and uses differentiable rendering to generate delay-beam power spectrum priors that improve full CFR reconstruction via MMSE in sparse-pilot high-mobility settings.
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Formal links
Receipt and verification
| First computed | 2026-05-20T00:01:52.373966Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ca41b328d786ac174e81b62430d8b6d22947bf0d6ddcb1adaca77fcd888d6ac4
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZJA3GKGXQ2WBOTUBWYSDBWFW2I \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ca41b328d786ac174e81b62430d8b6d22947bf0d6ddcb1adaca77fcd888d6ac4
Canonical record JSON
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