64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import numpy as np
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import scipy
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channel_response = np.array([0, 0, 0, 1, 0, 0, 0])
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# How do I sync this across two files?
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# figure it out later
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pilot_value = 1 + 1j
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# 15dB seems to be around the minimum for error-free transmission
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snr_db = 15
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def sim(in_data):
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out_data = np.ndarray((len(in_data), len(in_data[0])), dtype=np.csingle)
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# noise stuff is straight copied from the DSP illustrations article
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for i in range(len(in_data)):
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convolved = np.convolve(channel_response, in_data[i], mode='same')
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signal_power = np.mean(abs(convolved**2))
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noise_power = signal_power * 10**(-snr_db/10)
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noise = np.sqrt(noise_power / 2) * (np.random.randn(*convolved.shape) + 1j*np.random.randn(*convolved.shape))
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out_data[i] = convolved + noise
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return out_data
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# Again, most of this is stolen from the guide
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# this sort of stuff I had no idea about before I read the guide
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def estimate(in_data, pilots=0):
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all_carriers = np.arange(len(in_data))
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if pilots > 0:
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pilot_carriers = all_carriers[::(len(all_carriers)) // pilots]
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pilot_carriers = np.delete(pilot_carriers, 0)
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# start averaging
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H_est = 0
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for i in range(len(in_data)):
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H_est_pilots = in_data[i][pilots] / pilot_value
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H_est_abs = scipy.interpolate.interp1d(pilot_carriers, abs(H_est_pilots), kind='linear')(all_carriers)
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H_est_phase = scipy.interpolate.interp1d(pilot_carriers, np.angle(H_est_pilots), kind='linear')(all_carriers)
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H_est += H_est_abs * np.exp(1j*H_est_phase)
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H_est = H_est / len(in_data)
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return H_est
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else:
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return 1
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def equalize(in_data, H_est):
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out_data = np.ndarray((len(in_data), len(in_data[0])), dtype=np.csingle)
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for i in range(len(in_data)):
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out_data[i] = in_data[i] / H_est
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return out_data
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