hc
ced50ea2b0
Root cause: test-runner was giving overly optimistic results due to: 1. Context bias - knew the implementation, tended to defend it 2. No actual visual comparison - just wrote 'ACCEPTABLE' without looking 3. No structural validation - accepted 35x scale differences as 'acceptable' Solution: - New result-verifier agent that performs blind visual comparison - Strict pass/fail criteria for structural validation - Updated test-runner to use result-verifier for each figure - Clear guidelines: structural mismatches = FAIL, not ACCEPTABLE Test result: verifier correctly identified Fig3 as FAIL with 7 specific issues: - Wrong X-axis variable (channels vs power) - Wrong Y-axis scale (5x difference) - Wrong curve count (5 vs 4) - etc.
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
"""
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src/models/baselines.py
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Implements Module 4: Transform Method & Baselines
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"""
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import numpy as np
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from typing import Tuple
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class BaselineModels:
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"""
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Implements 4G/5G baselines and Ideal Shannon Limit using the transform method.
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"""
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def __init__(self, mu: float = 19.0, L: int = 12):
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self.mu = mu # Transforming factor (bits/word)
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self.L = L # Average words per sentence
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def _cqi_mapping(self, snr_db: np.ndarray, generation: str = "5G") -> np.ndarray:
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"""
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Approximate 3GPP CQI mapping from SNR to Spectral Efficiency (bps/Hz)
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"""
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if generation == "5G":
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# 5G supports higher modulation schemes (e.g. 256 QAM)
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max_se = 7.4 # up to ~7.4 bps/Hz
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shift = 15.0
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scale = 4.0
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else: # 4G
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# 4G supports up to 64 QAM typically in this context
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max_se = 4.8 # up to ~4.8 bps/Hz
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shift = 18.0
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scale = 5.0
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# Sigmoid approximation of discrete CQI steps
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se = max_se / (1 + np.exp(-(snr_db - shift) / scale))
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# Ensure minimum SE for low SNR
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se = np.maximum(se, 0.1)
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return se
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def calculate_baseline_sse(
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self, snr_linear: np.ndarray, model_type: str
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) -> np.ndarray:
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"""
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Calculate equivalent Semantic Spectral Efficiency for baselines.
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"""
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snr_db = 10 * np.log10(np.maximum(snr_linear, 1e-10))
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if model_type.lower() == "ideal":
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# Ideal Shannon capacity W / B = log2(1 + SNR)
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spectral_efficiency = np.log2(1 + snr_linear)
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elif model_type.upper() == "5G":
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spectral_efficiency = self._cqi_mapping(snr_db, "5G")
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elif model_type.upper() == "4G":
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spectral_efficiency = self._cqi_mapping(snr_db, "4G")
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else:
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raise ValueError(f"Unknown model_type: {model_type}")
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# Transform method: equivalent S-SE = SE / mu
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# Because S-SE is measured in words/sec/Hz, and SE is bits/sec/Hz
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# mu is bits/word
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sse = spectral_efficiency / self.mu
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return sse
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