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.
90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
"""
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src/models/environment.py
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Implements Module 1: Environment & Channel Simulator
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"""
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import numpy as np
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from typing import NamedTuple, Tuple
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class EnvironmentConfig(NamedTuple):
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"""Configuration for the channel simulator."""
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num_users: int = 10
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num_channels: int = 10
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bandwidth: float = 1e6 # Hz, 1 MHz per channel
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radius: float = 0.5 # km, cell radius
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shadow_fading_std: float = 6.0 # dB
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noise_psd_dbm: float = -174.0 # dBm/Hz
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class ChannelSimulator:
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"""
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Simulates the wireless channel environment.
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Paper Reference:
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- Pathloss: 128.1 + 37.6 lg[d(km)] dB
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- Shadow fading: 6 dB
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"""
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def __init__(self, config: EnvironmentConfig):
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self.config = config
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def _calculate_pathloss(self, distances: np.ndarray) -> np.ndarray:
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"""Calculate pathloss for given distances in km."""
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return 128.1 + 37.6 * np.log10(distances)
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def generate_channels(
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self, transmit_power_dbm: float
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) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Generate channel conditions (SNR) for all users and channels.
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Args:
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transmit_power_dbm: Transmit power in dBm
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Returns:
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Tuple of (snr_db, snr_linear) with shape (num_users, num_channels)
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"""
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N, M = self.config.num_users, self.config.num_channels
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# 1. Distances (randomly distributed between 0.05km and cell radius)
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min_dist = 0.05
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distances = np.random.uniform(min_dist, self.config.radius, size=N)
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# 2. Pathloss
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path_loss_db = self._calculate_pathloss(distances)
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# 3. Shadow fading
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shadowing_db = np.random.normal(0, self.config.shadow_fading_std, size=N)
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# 4. Total large scale fading (dB)
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large_scale_db = path_loss_db + shadowing_db
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# 5. Rayleigh fading (small scale)
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# Power of Rayleigh follows exponential distribution (mean=1)
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small_scale_power = np.random.exponential(1.0, size=(N, M))
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small_scale_db = 10 * np.log10(small_scale_power)
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# 6. Noise power
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# Noise = PSD (dBm/Hz) + 10*log10(BW)
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noise_power_dbm = self.config.noise_psd_dbm + 10 * np.log10(
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self.config.bandwidth
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)
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# 7. Calculate SNR
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# SNR(dB) = Pt(dBm) - LargeScale(dB) + SmallScale(dB) - Noise(dBm)
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snr_db = np.zeros((N, M))
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for n in range(N):
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snr_db[n, :] = (
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transmit_power_dbm
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- large_scale_db[n]
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+ small_scale_db[n, :]
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- noise_power_dbm
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)
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snr_linear = 10 ** (snr_db / 10)
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return snr_db, snr_linear
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