Pith Number

pith:TXXTO7CI

pith:2026:TXXTO7CIU235FUBIV2SHLMDG3S

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CITYMPC: A Large-Scale Physics-Informed Benchmark and Tool for Generative Complete Multipath Wireless Channel Modeling

Amitava Ghosh, Ashwin Natraj Arun, Christopher Brinton, David J. Love, David R. Nickel, James V. Krogmeier, Jie Chen, Yaguang Zhang, Yunchou Xing

CITYMPC generates complete multipath wireless channel parameters from point-of-view imagery and terrain height maps alone.

arxiv:2605.15471 v1 · 2026-05-14 · eess.SP

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

CITYMPC predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achieving environment-aware channel generation without access to any three-dimensional scene geometry at inference.

C2weakest assumption

That point-of-view imagery and terrain height maps alone contain enough information to accurately reconstruct the full set of multipath component parameters that would otherwise require explicit 3D scene geometry.

C3one line summary

CITYMPC, a cVAE model, predicts full per-path multipath component parameters from POV images and height maps alone, matching ray-tracing accuracy with 1.29 dB power MAE and 7.25 ns delay MAE across 427k links in five cities while releasing the dataset as a benchmark.

References

56 extracted · 56 resolved · 5 Pith anchors

[1] Recommendation M.2160: Framework and Overall Objectives of the Future Devel- opment of IMT for 2030 and Beyond, 2023 2030

[2] 6G Roadmap for Vertical Industries, 2023 2023

[3] Brinton, Mung Chiang, Kwang Taik Kim, David J 2025 · doi:10.1109/mcom.002.2400150

[4] Hwanjin Kim, Junil Choi, and David J. Love. Machine-Learning Techniques for Wireless Channel Prediction: Insights and Practical Guidance. IEEE Wireless Communications, pages 1–8, 2026. doi: 10.1109/MW 2026 · doi:10.1109/mwc.2026.3678210

[5] Radio Propagation Measurements and Channel Modeling 2022

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:00.295079Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9def377c48a6b7d2d028aea475b066dc87a6f6d6c1994e4bd3f7b9c136d70fde

Aliases

arxiv: 2605.15471 · arxiv_version: 2605.15471v1 · doi: 10.48550/arxiv.2605.15471 · pith_short_12: TXXTO7CIU235 · pith_short_16: TXXTO7CIU235FUBI · pith_short_8: TXXTO7CI

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TXXTO7CIU235FUBIV2SHLMDG3S \
  | 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: 9def377c48a6b7d2d028aea475b066dc87a6f6d6c1994e4bd3f7b9c136d70fde

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "43162963cf965406d23297719a5af69788c4762dc0abd0c997b0bb496e9dbbb7",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-14T23:20:26Z",
    "title_canon_sha256": "8826147fd4f169ea923f486e7532804cd99cea14d007f5b5f86ea7c3b2aa4116"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15471",
    "kind": "arxiv",
    "version": 1
  }
}