set up torch AoA estimation
This commit is contained in:
Christos Falas
parent
66e041cf73
commit
a2f94737f8
1
Pipfile
1
Pipfile
@ -11,6 +11,7 @@ pytest = "*"
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matplotlib = "*"
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scipy = "*"
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scipy-stubs = "*"
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torch = "*"
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[dev-packages]
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110
src/aoa.py
110
src/aoa.py
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import numpy as np
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import numpy.typing as npt
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import torch
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import torch.linalg
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from . import config
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from datetime import datetime
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@ -7,16 +8,19 @@ import logging
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logger = logging.getLogger(__name__)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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torch.set_default_device(device)
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class AoA:
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def __init__(self):
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self.historical_autocorr = np.array([])
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self.historical_autocorr = torch.tensor([], dtype=torch.complex64)
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self.N_subcarriers = -1
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self.N_rx = -1
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self.timestamp = datetime.now()
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pass
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def smooth(self, data: npt.NDArray[np.complex128]):
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def smooth(self, data: torch.Tensor):
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assert len(data.shape) == 3
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M = data.shape[0] # Number of subcarriers
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@ -31,32 +35,32 @@ class AoA:
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# This only works with 1 TX antenna (i.e. no MIMO) - see #4 for more details
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assert T == 1, "The current implementation only supports 1 TX antenna"
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H_n = np.zeros((N, M // 2, M // 2 + 1), dtype=np.complex128)
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H_n = torch.zeros((N, M // 2, M // 2 + 1), dtype=torch.complex64)
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for i in range(N):
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for j in range(M // 2):
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H_n[i, j] = data[j : j + M // 2 + 1, i, 0]
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H_sm_rows = [np.hstack(H_n[i : i + N // 2 + 1]) for i in range(N // 2)]
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H_sm = np.vstack(H_sm_rows)
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H_sm_rows = [torch.hstack(list(H_n[i : i + N // 2 + 1])) for i in range(N // 2)]
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H_sm = torch.vstack(H_sm_rows)
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logger.debug(f"Smoothed: {H_sm.shape}")
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return H_sm
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def update(self, data: npt.NDArray[np.complex128]):
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def update(self, data: torch.Tensor):
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self.timestamp = datetime.now()
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H_sm = self.smooth(data)
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auto_corr = np.matmul(H_sm, np.conj(H_sm).T)
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auto_corr = H_sm @ torch.conj(H_sm).T
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# This matrix is by definition Hermitian.
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# Therefore, all of its eigenvectors are orthogonal.
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if self.historical_autocorr.size == 0:
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self.historical_autocorr = np.expand_dims(auto_corr, 0)
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if len(self.historical_autocorr.shape) <= 1:
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self.historical_autocorr = torch.unsqueeze(auto_corr, 0)
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else:
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self.historical_autocorr = np.append(
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self.historical_autocorr, np.expand_dims(auto_corr, 0), axis=0
|
||||
self.historical_autocorr = torch.cat(
|
||||
(self.historical_autocorr, torch.unsqueeze(auto_corr, 0))
|
||||
)
|
||||
|
||||
WINDOW_SIZE = config.AOA_SLIDING_WINDOW_SIZE
|
||||
@ -64,72 +68,78 @@ class AoA:
|
||||
self.historical_autocorr = self.historical_autocorr[-WINDOW_SIZE:]
|
||||
|
||||
# Is the moving average also Hermitian?
|
||||
R = np.mean(self.historical_autocorr, axis=0)
|
||||
R = torch.mean(self.historical_autocorr, dim=0)
|
||||
|
||||
# The smallest eigenvectors span the noise subspace,
|
||||
# and the largest span the signal subspace.
|
||||
eigvals, eigvecs = np.linalg.eigh(R)
|
||||
self.E_n = eigvecs[:, np.abs(eigvals) < config.EIGVAL_THRESHOLD]
|
||||
|
||||
omega_base = np.exp(-2j * np.pi * config.DELTA_F)
|
||||
phi_base = np.exp(
|
||||
2j * np.pi * config.CENTRAL_FREQUENCY_HZ * config.ANTENNA_SPACING / config.C
|
||||
)
|
||||
eigvals, eigvecs = torch.linalg.eigh(R)
|
||||
logging.debug(f"Eigenvalues: {eigvals}")
|
||||
self.E_n = eigvecs[:, torch.abs(eigvals) < config.EIGVAL_THRESHOLD]
|
||||
|
||||
def steering_vector(self, theta: float, tof: float):
|
||||
omega_t = np.exp(-2j * np.pi * config.DELTA_F * tof)
|
||||
phi_theta = np.exp(
|
||||
2j
|
||||
* np.pi
|
||||
* config.CENTRAL_FREQUENCY_HZ
|
||||
* config.ANTENNA_SPACING
|
||||
* (1 - np.cos(theta))
|
||||
/ config.C
|
||||
omega_t = torch.exp(
|
||||
torch.tensor([-2j * torch.pi * config.DELTA_F * tof], dtype=torch.complex64)
|
||||
)
|
||||
phi_theta = torch.exp(
|
||||
torch.tensor(
|
||||
[
|
||||
2j
|
||||
* np.pi
|
||||
* config.CENTRAL_FREQUENCY_HZ
|
||||
* config.ANTENNA_SPACING
|
||||
* (1 - np.cos(theta))
|
||||
/ config.C
|
||||
],
|
||||
dtype=torch.complex64,
|
||||
)
|
||||
)
|
||||
|
||||
omega_t = np.expand_dims(omega_t, axis=-1)
|
||||
phi_theta = np.expand_dims(phi_theta, axis=-1)
|
||||
omega_t = torch.unsqueeze(omega_t, dim=-1)
|
||||
phi_theta = torch.unsqueeze(phi_theta, dim=-1)
|
||||
|
||||
antenna_v = omega_t ** np.arange(self.N_subcarriers // 2)
|
||||
phis = phi_theta ** np.arange(self.N_rx // 2)
|
||||
antenna_v = np.expand_dims(antenna_v, axis=-1)
|
||||
steering = antenna_v * phis
|
||||
antenna_v = omega_t ** torch.arange(self.N_subcarriers // 2)
|
||||
phis = phi_theta ** torch.arange(self.N_rx // 2)
|
||||
antenna_v = torch.unsqueeze(antenna_v, dim=-1)
|
||||
print(antenna_v.shape, phis.shape)
|
||||
steering = antenna_v[0] * phis
|
||||
print(steering.shape)
|
||||
return steering.T.reshape(-1)
|
||||
|
||||
def evaluate(self, theta: float, tof: float):
|
||||
try:
|
||||
steering = self.steering_vector(theta, tof)
|
||||
steering_h = np.conj(steering).T
|
||||
steering_h = torch.conj(steering).T
|
||||
except Exception as e:
|
||||
logger.exception(e)
|
||||
return 0
|
||||
|
||||
assert isinstance(self.E_n, torch.Tensor)
|
||||
E_n = self.E_n
|
||||
E_n_H = np.conj(E_n).T
|
||||
E_n_H = torch.conj(E_n).T
|
||||
c = 1 / (0.001 + (steering_h @ E_n @ E_n_H @ steering))
|
||||
return np.abs(c.real)
|
||||
return torch.abs(c.real)
|
||||
|
||||
|
||||
def test_smoothing():
|
||||
row, col = np.indices((4, 2))
|
||||
row, col = torch.indices((6, 4))
|
||||
data = row + 1j * col
|
||||
data = np.expand_dims(data, axis=2)
|
||||
np.set_printoptions(linewidth=200)
|
||||
print(data.shape)
|
||||
aoa = AoA()
|
||||
aoa.N_subcarriers = 6
|
||||
aoa.N_rx = 4
|
||||
smoothed = aoa.smooth(data)
|
||||
H_0 = np.array([[0 + 0j, 0 + 1j, 0 + 2j], [0 + 1j, 0 + 2j, 0 + 3j]])
|
||||
H_01 = np.vstack([H_0, H_0 + 1])
|
||||
H_12 = np.vstack([H_0 + 1, H_0 + 2])
|
||||
expected = np.hstack([H_01, H_12])
|
||||
print(expected)
|
||||
assert np.allclose(smoothed, expected)
|
||||
print(smoothed)
|
||||
|
||||
pass
|
||||
|
||||
|
||||
def test_steering_vector():
|
||||
aoa = AoA()
|
||||
aoa.N_subcarriers = 10
|
||||
aoa.N_rx = 2
|
||||
print(aoa.omega_base)
|
||||
print(aoa.phi_base)
|
||||
tau = 1
|
||||
theta = 0
|
||||
aoa.N_subcarriers = 6
|
||||
aoa.N_rx = 4
|
||||
tau = 1e-8
|
||||
theta = np.pi / 2
|
||||
print(aoa.steering_vector(theta, tau))
|
||||
assert False
|
||||
|
||||
@ -94,13 +94,13 @@ class CSIHeader:
|
||||
class CSI:
|
||||
@staticmethod
|
||||
def parseCsiData(data: bytes, header: CSIHeader):
|
||||
csi_matrix: npt.NDArray[np.complex128] = np.zeros(
|
||||
csi_matrix: npt.NDArray[np.complex64] = np.zeros(
|
||||
(
|
||||
header.num_subcarriers,
|
||||
header.num_rx,
|
||||
header.num_tx,
|
||||
),
|
||||
dtype=np.complex128,
|
||||
dtype=np.complex64,
|
||||
)
|
||||
pos = 0
|
||||
for j in range(header.num_rx):
|
||||
|
||||
@ -3,6 +3,7 @@ import time
|
||||
import socket
|
||||
import multiprocessing as mp
|
||||
import struct
|
||||
import torch
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
|
||||
@ -12,6 +13,7 @@ from .preprocess import Preprocessor
|
||||
from . import config
|
||||
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
Host = tuple[str, int]
|
||||
|
||||
|
||||
@ -123,8 +125,10 @@ class CSIProcessor:
|
||||
self.logger.info(f"Got final CSI data with shape {all_data.shape}")
|
||||
|
||||
processed = self.preprocess.preprocess(all_data)
|
||||
visualise.add_data(all_data, processed)
|
||||
self.aoa.update(processed)
|
||||
processed_tensor = torch.tensor(processed, device=device)
|
||||
# visualise.add_data(all_data_tensor, processed)
|
||||
self.aoa.update(processed_tensor)
|
||||
self.logger.info("Processed data")
|
||||
if not self.webserver.full():
|
||||
self.webserver.put(self.aoa)
|
||||
|
||||
|
||||
@ -15,9 +15,9 @@ np.seterr(invalid="ignore")
|
||||
|
||||
class Preprocessor:
|
||||
def __init__(self):
|
||||
self.prev_entries: Queue[npt.NDArray[np.complex128]] = Queue(maxsize=100)
|
||||
self.short_term_avg = np.zeros((1,), dtype=np.complex128)
|
||||
self.long_term_avg = np.zeros((1,), dtype=np.complex128)
|
||||
self.prev_entries: Queue[npt.NDArray[np.complex64]] = Queue(maxsize=100)
|
||||
self.short_term_avg = np.zeros((1,), dtype=np.complex64)
|
||||
self.long_term_avg = np.zeros((1,), dtype=np.complex64)
|
||||
self.filter = butter(
|
||||
5,
|
||||
[
|
||||
@ -29,7 +29,7 @@ class Preprocessor:
|
||||
output="sos",
|
||||
)
|
||||
|
||||
def preprocess(self, h: npt.NDArray[np.complex128]) -> npt.NDArray[np.complex128]:
|
||||
def preprocess(self, h: npt.NDArray[np.complex64]) -> npt.NDArray[np.complex64]:
|
||||
# CSI data is not available for pilot subcarriers.
|
||||
h_hat = np.where(
|
||||
np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
|
||||
@ -49,7 +49,7 @@ class Preprocessor:
|
||||
|
||||
# Assume that all csi matrices will have the same shape
|
||||
if self.long_term_avg.shape != h_hat.shape:
|
||||
self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex128)
|
||||
self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex64)
|
||||
|
||||
self.long_term_avg = (
|
||||
self.long_term_avg * (1 - config.PREPROCESSING_LONG_TERM_ALPHA)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user