49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
#!/usr/bin/python
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import numpy as np
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from libs.consts import NOISE_FLOOR
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from libs.consts import RANDOM_SEED
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from libs.consts import GAUSSIAN_NOISE_STD
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from libs.consts import GAUSSIAN_NOISE_MEAN
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np.random.seed(RANDOM_SEED)
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def log_gamma_loc(
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rx_loc: np.ndarray, # dtype=float, avoid int
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tx_loc: np.ndarray, # dtype=float, avoid int
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pwr: float,
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gamma: float,
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loss: float = 0.0,
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gaussian_noise: bool = False
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):
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'''
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'''
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dist_squared = np.nansum((rx_loc - tx_loc) * (rx_loc - tx_loc), axis=1)
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dist_squared[dist_squared < 0.02] = 0.02
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noise = normal(GAUSSIAN_NOISE_MEAN, GAUSSIAN_NOISE_STD) if gaussian_noise else 0.0
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rss = pwr - 10.0 * gamma / 2 * np.log10(dist_squared) + noise + loss
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rss[rss < NOISE_FLOOR] = NOISE_FLOOR
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rss[rss > pwr] = pwr
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return rss
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def log_gamma_dist(
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dist: np.ndarray,
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pwr: float,
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gamma: float,
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loss: float = 0.0,
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gaussian_noise: bool = False,
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is_squared: bool = False
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):
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'''
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'''
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factor = 0.5 if is_squared else 1.0
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noise = normal(GAUSSIAN_NOISE_MEAN, GAUSSIAN_NOISE_STD) if gaussian_noise else 0.0
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rss = pwr - 10.0 * gamma * factor * np.log10(dist) + noise + loss
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rss[rss < NOISE_FLOOR] = NOISE_FLOOR
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rss[rss > pwr] = pwr
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return rss
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