234 lines
8.5 KiB
Python
234 lines
8.5 KiB
Python
import logging
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from typing import Any, Callable
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import numpy as np
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import numpy.typing as npt
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from scipy.signal import butter, correlate, sosfilt, sosfilt_zi
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from where_fi.collection import CSIMatrix
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from where_fi.collection.csi_frame import CSI
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from where_fi.collection.protocols import CSIHost
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from where_fi.config import config
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from ..visualise import server as visualise
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logger = logging.getLogger(__name__)
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np.seterr(invalid="ignore")
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class Preprocessor:
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def __init__(self) -> None:
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self.short_term_avg = np.zeros((1,), dtype=np.complex64)
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self.long_term_avg = np.zeros((1,), dtype=np.complex64)
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self._last_sample = None
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self.denoising_samples = int(
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config.preprocessing.denoising_period * config.collection_sample_rate
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)
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self.circular_buffer = np.zeros(
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(
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self.denoising_samples, # Number of samples
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config.subcarriers, # Number of subcarriers
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config.antennas.count, # Number of RX antennas
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1, # Number of TX antennas
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),
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dtype=np.complex64,
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)
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self.sample_index = 0
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@property
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def last_sample(self) -> None | CSIMatrix:
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"""
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The last sample of the preprocessor. This is used for low frequency processing
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"""
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match config.preprocessing.denoising:
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case "none":
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return self._last_sample
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case "median":
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# Return the median of the last samples
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median_abs = np.median(np.abs(self.circular_buffer), axis=0)
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median_angle = np.median(np.angle(self.circular_buffer), axis=0)
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ans = median_abs * np.exp(1j * median_angle)
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return ans
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case "mean":
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# Return the mean of the last samples
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return np.mean(self.circular_buffer, axis=0)
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case _:
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raise ValueError(
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f"Invalid denoising method: {config.preprocessing.denoising}"
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)
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def remove_sto(self, csi: CSIMatrix) -> CSIMatrix:
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"""
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Remove sampling time offsets caused by:
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- Sampling frequency offset
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- Packet detection delay
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This is done by multiplying the CSI matrices of consecutive antennas in the
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array.
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According to [1]:
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> Conjugate multiplication and division are the only two methods to
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> eliminate the SFO and PDD.
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No citation or explanation is provided, so not sure why/whether it works.
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Something similar is also done in [2] without explanation.
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[1] - https://tns.thss.tsinghua.edu.cn/wst/docs/sanitization
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[2] - https://doi.org/10.1109/ICC51166.2024.10623053
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"""
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csi_remove_sto = np.zeros_like(csi)
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for antenna in range(csi.shape[1]):
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antenna_nxt = (antenna + 1) % csi.shape[1]
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csi_remove_sto[:, antenna, :] = np.multiply(
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csi[:, antenna, :], csi[:, antenna_nxt, :].conj()
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)
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return csi_remove_sto
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def remove_sfo(
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self,
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csi: CSIMatrix,
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visualiser: None
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| Callable[[npt.NDArray[Any], visualise.figures.Figure], None] = None,
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) -> CSIMatrix:
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"""
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Remove sampling frequency offsets by linear regression.
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This is caused by the difference in sampling frequency between the transmitter
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and receiver, and this is linear in frequency. We can estimate it using linear
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regression and compensate for it.
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"""
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N_st, N_rx, _ = csi.shape
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unwrapped = np.unwrap(np.angle(csi[:, :, 0]), axis=0).reshape(N_st, N_rx, 1)
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if visualiser:
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visualiser(unwrapped, visualise.figures.Figure.UNWRAPPED_PHASE)
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X = np.vstack([np.arange(N_st), np.ones(N_st)]).T
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for antenna in range(csi.shape[1]):
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tau, rho = np.linalg.lstsq(X, unwrapped[:, antenna, 0])[0]
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csi[:, antenna, 0] = np.abs(csi[:, antenna, 0]) * np.exp(
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1j
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* (np.angle(csi[:, antenna, 0]) - (tau * np.arange(csi.shape[0]) + rho))
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)
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return csi
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def remove_agc(self, csi: CSIMatrix, frames: dict[CSIHost, CSI]) -> CSIMatrix:
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"""
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Normalise the magnitude of the CSI data to compensate for the effect of the
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Automatic Gain Control (AGC) of the receiver
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References:
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[1] -
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"""
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rssi = [
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frames[host].header.rssi1 if antenna == 0 else frames[host].header.rssi2
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for host, antenna in config.antennas.order
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]
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rssi_linear = np.reshape(10 ** (np.array(rssi) / 10), (1, -1, 1))
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csi_power = np.sum(np.abs(csi) ** 2)
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return csi * np.sqrt(rssi_linear / csi_power)
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def skip_subcarriers(self, csi: CSIMatrix) -> CSIMatrix:
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"""
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Sometimes beccause of the large amount of processing, we need to skip some
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subcarriers for the processing to be able to run in real time.
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The subcarriers to skip are defined in the config file, per subcarrier_step. If
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subcarrier_step is set to 1 (default), no subcarriers are skipped.
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"""
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return csi[:: config.preprocessing.subcarrier_step, :, :]
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def fill_pilots(self, csi: CSIMatrix) -> CSIMatrix:
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"""
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Fill the pilot subcarriers with the average of the surrounding subcarriers.
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This is done by averaging the subcarriers before and after the pilot
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subcarriers. Pilots are detected by checking that the value is exactly 0.
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Also, it adds placeholders for the middle null subcarriers.
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"""
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num_middle = 1 if config.channel_width == 20 else 3
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assert csi.shape[0] + num_middle == config.subcarriers
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with_middle = np.zeros(
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(csi.shape[0] + num_middle, csi.shape[1], csi.shape[2]), dtype=np.complex64
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)
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with_middle[: csi.shape[0] // 2, :, :] = csi[: csi.shape[0] // 2, :, :]
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with_middle[csi.shape[0] // 2 + num_middle :, :, :] = csi[
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csi.shape[0] // 2 :, :, :
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]
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if num_middle == 3:
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with_middle[csi.shape[0] // 2 + 1, :, :] = (
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csi[csi.shape[0] // 2 - 1, :, :] + csi[csi.shape[0] // 2, :, :]
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) / 2
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return np.where(
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np.expand_dims(with_middle[:, 0, 0] == 0, axis=(1, 2)),
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correlate(with_middle, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"),
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with_middle,
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)
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def bandpass(self, csi: CSIMatrix) -> CSIMatrix:
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"""
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Apply a Butterworth bandpass filter to the CSI data.
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Remove low-frequency noise (caused by static paths) and high-frequency noise
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(measurement variance).
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"""
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if not hasattr(self, "filter"):
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assert config.preprocessing.bandpass is not None
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self.filter = butter(
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5,
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config.preprocessing.bandpass.bounds,
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fs=config.collection_sample_rate,
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btype="band",
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output="sos",
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)
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if not hasattr(self, "filter_zi"):
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self.filter_zi = (
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np.expand_dims(sosfilt_zi(self.filter), axis=(-1, -2, -3)) * csi
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)
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h_hat_filt, self.filter_zi = sosfilt(
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self.filter, [csi], zi=self.filter_zi, axis=0
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)
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return h_hat_filt[0]
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def preprocess(
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self,
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h: CSIMatrix,
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frames: dict[CSIHost, CSI],
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visualiser: None
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| Callable[[npt.NDArray[Any], visualise.figures.Figure], None] = None,
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) -> CSIMatrix:
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# CSI data is not available for pilot subcarriers.
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h_hat = h
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for step in config.preprocessing.steps:
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match step:
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case "skip_subcarriers":
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h_hat = self.skip_subcarriers(h_hat)
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case "remove_agc":
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h_hat = self.remove_agc(h_hat, frames)
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case "remove_sfo":
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h_hat = self.remove_sfo(h_hat, visualiser=visualiser)
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case "remove_sto":
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h_hat = self.remove_sto(h_hat)
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case "fill_pilots":
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h_hat = self.fill_pilots(h_hat)
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case "bandpass":
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h_hat = self.bandpass(h_hat)
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logger.debug(f"CSI shape: {h_hat.shape}")
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self._last_sample = h_hat
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if config.preprocessing.denoising != "none":
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self.circular_buffer[self.sample_index] = h_hat
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self.sample_index = (self.sample_index + 1) % self.denoising_samples
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return h_hat
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