L1 ⚛ L1-253 ⊙ Testnet

Ray Tracing (EM) — wireless propagation model

Electromagnetics · Urban propagation ray tracing (5G/6G)

⬇ Download bundle 🚀 Submit to mainnet — earn 100 PWM + 5% royalty

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-253.md

# ⚛ L1 Principle — Ray Tracing (EM) — wireless propagation model **ID:** `L1-253` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Electromagnetics — *Urban propagation ray tracing (5G/6G)* > **🎯 Problem class:** linear inverse · **🧮 Solution space:** signal strength map > **📡 Carrier:** em · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41554001 --- ## 🧠 1. Introduction **Ray Tracing (EM) — wireless propagation model** is a **linear inverse problem** whose unknown lives in **signal strength map** space, within the **Urban propagation ray tracing (5G/6G)** sub-domain of **Electromagnetics**. Measurements consist of electromagnetic field measurements via a **rx power map** sensing mechanism. The forward operator applies, in order: isotropic point-source emission model; L · reflect diffract operator; L · gather paths operator; pixel-level spatial averaging on the detector. Observations are corrupted by additive Gaussian noise. Conditional stability; mismatch parameters dominate at Omega bounds. ## ⚙ 2. Forward Model Physical chain: **x** → Source emission → L · reflect diffract → L · gather paths → Spatial integration → **y** (detector). ``` y = ∫_A dA `L.gather_paths` `L.reflect_diffract` δ_source x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.source_emit` | Isotropic point-source emission model | | `L.reflect_diffract` | L · reflect diffract operator | | `L.gather_paths` | L · gather paths operator | | `int.spatial` | Pixel-level spatial averaging on the detector | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Electromagnetics | | Sub domain | Urban propagation ray tracing (5G/6G) | | Carrier | em | | Problem class | linear_inverse | | Solution space | signal_strength_map | | Noise model | gaussian | | Integration axis | spatial | | Difficulty delta | 3 | | L dag | 3.3 | ## 📡 4. Measurement Model Conditional stability; mismatch parameters dominate at Omega bounds. | Metric | Value | |---|---| | Metric | RSSI_error_dB | | Secondary | SSIM | ## 📏 5. Operating Range (Ω) **Center problem class:** `ray_tracing_em` · **Forward operator:** `ray_tracing_em_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | N rx | — | 64 | | N rays | — | 100000 | | Snr db | dB | 30 | | N walls | — | 1000 | | Freq ghz | GHz | 3.5 | | Geometry error | — | 0 | | Material eps error | — | 0 | | Antenna pattern error | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N rx | — | 1 – 10000 | | N rays | — | 1000 – 100000000 | | Snr db | dB | 0 – 40 | | N walls | — | 1 – 100000 | | Freq ghz | GHz | 0.1 – 300 | | Geometry error | — | 0.0 – 0.1 | | Material eps error | — | 0.0 – 0.3 | | Antenna pattern error | — | 0.0 – 0.3 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 3.0 | Metric | Range | |---|---| | Rssi db | 0.5 – 20.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **RSSI_error_dB**, with κ = `360` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x2b80902bf2d6782d0d99478ed531b8b769ff810669e7d7cf69b8d96afd5cf23b` - **Chain tx hash:** `0xcc15c77e404ff48d63f9bcf9cfa621aeb3922cbe82ed7190eef7fc9a87fd25b2` - **Chain block:** `41554001` --- ## File Mapping This bundle consists of: `L1-253.md`, `L1-253.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-253.md` | Source of truth — edit this | Human or LLM | | `L1-253.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-253`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

📝 Polish this artifact in your LLM

Download the files on the right.
Paste the Markdown into ChatGPT / Claude / Gemini and edit freely.
Send your edited Markdown back with the prompt below.
Save the regenerated JSON, then submit via /submit.

Prompt to give your LLM

Read the attached Markdown (L1-253.md).
Regenerate L1-253.json so every field matches the Markdown.
Preserve the schema documented in the File Mapping footer at the bottom of the MD.
Output each file in its own fenced code block tagged with the filename.

⌥ Hierarchy

Upstream lineage and downstream children

↓ L2 Spec 1 child ▾

L2-253-001 Ray Tracing (EM) — wireless propagation model Mismatch-Only Spec

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-253",
  "chain_block": 41554001,
  "chain_hash": "0x2b80902bf2d6782d0d99478ed531b8b769ff810669e7d7cf69b8d96afd5cf23b",
  "chain_tx_hash": "0xcc15c77e404ff48d63f9bcf9cfa621aeb3922cbe82ed7190eef7fc9a87fd25b2",
  "domain": "Electromagnetics",
  "hardness_fn": {
    "delta": 3,
    "kappa": 360,
    "metric": "RSSI_error_dB",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "RSSI_error_dB",
    "regime": "Conditional stability; mismatch parameters dominate at Omega bounds.",
    "secondary": "SSIM"
  },
  "physics_fingerprint": {
    "L_DAG": 3.3,
    "carrier": "em",
    "difficulty_delta": 3,
    "domain": "Electromagnetics",
    "integration_axis": "spatial",
    "noise_model": "gaussian",
    "primitives": [
      "L.source_emit",
      "L.reflect_diffract",
      "L.gather_paths",
      "int.spatial"
    ],
    "problem_class": "linear_inverse",
    "sensing_mechanism": "rx_power_map",
    "solution_space": "signal_strength_map",
    "sub_domain": "Urban propagation ray tracing (5G/6G)",
    "title": "Ray Tracing (EM) \u2014 wireless propagation model"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "ray_tracing_em_forward"
    ],
    "allowed_omega_dimensions": [
      "N_rays",
      "N_walls",
      "N_rx",
      "freq_GHz",
      "SNR_dB",
      "material_eps_error",
      "geometry_error",
      "antenna_pattern_error",
      "material_eps_error",
      "geometry_error",
      "antenna_pattern_error"
    ],
    "allowed_problem_classes": [
      "ray_tracing_em"
    ],
    "center_spec": {
      "epsilon_fn_center": "3.0",
      "forward_operator": "ray_tracing_em_forward",
      "input_format": "measurement_only",
      "omega": {
        "N_rays": 100000,
        "N_rx": 64,
        "N_walls": 1000,
        "SNR_dB": 30,
        "antenna_pattern_error": 0.0,
        "freq_GHz": 3.5,
        "geometry_error": 0.0,
        "material_eps_error": 0.0
      },
      "problem_class": "ray_tracing_em"
    },
    "epsilon_bounds": {
      "RSSI_dB": [
        0.5,
        20.0
      ]
    },
    "omega_bounds": {
      "N_rays": [
        1000,
        100000000
      ],
      "N_rx": [
        1,
        10000
      ],
      "N_walls": [
        1,
        100000
      ],
      "SNR_dB": [
        0,
        40
      ],
      "antenna_pattern_error": [
        0.0,
        0.3
      ],
      "freq_GHz": [
        0.1,
        300
      ],
      "geometry_error": [
        0.0,
        0.1
      ],
      "material_eps_error": [
        0.0,
        0.3
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Urban propagation ray tracing (5G/6G)",
  "title": "Ray Tracing (EM) \u2014 wireless propagation model"
}

🎁

Earn PWM by promoting this to mainnet

• 100 PWM one-time grant on mainnet promotion
• 5% upstream royalty on every downstream L4 mining event, forever
• Your wallet is enrolled the moment the founder accepts

🚀 Submit to mainnet →

How: Download the bundle above → polish the Markdown in ChatGPT / Claude / Gemini → submit your version → founder reviews → mainnet.