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gradient_descent_E312_0213.py
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gradient_descent_E312_0213.py
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import numpy as np
import timeit
import os
import struct
from numpy import fft
import time
import matplotlib.pyplot as plt
def read_last_pulse(filename, N):
nsamples = N
channels = 2
rubish = 0
# Read latest pulse
data_ch0, data_ch1 = last_pulse(filename, nsamples * channels, rubish)
rx_error = np.array(data_ch0[0:N])
return rx_error
def upsampling(x, upsamp_rate):
# https://dsp.stackexchange.com/questions/14919/upsample-data-using-ffts-how-is-this-exactly-done
# FFT upsampling method
N = x.shape[0]
D = x.shape[1]
# To frequency domain
X = fft.fft(x,axis = 0)
# Add taps in the middle
A1= X[0:N/2,:]
A2= np.zeros([(upsamp_rate-1)*N,D])
A3=X[N/2:N,:]
XX = np.concatenate((A1,A2,A3))
# To time domain
xx = upsamp_rate * fft.ifft(XX,axis = 0)
#plt.plot(np.linspace(0,1, xx.shape[0]), xx)
##plt.plot(np.linspace(0,1, xx.shape[0]), xx ,'ko')
#plt.plot(np.linspace(0,1, N), x, 'y.')
#plt.show()
x_upsamp = xx
return x_upsamp
def downsampling(x, downsamp_rate):
N = x.shape[0]
D = x.shape[1]
x_downsamp = x[::downsamp_rate]
#plt.plot(np.linspace(0,1, x_downsamp.shape[0]), x_downsamp)
#plt.plot(np.linspace(0,1, x_downsamp.shape[0]), x_downsamp ,'ko')
#plt.plot(np.linspace(0,1, N), x, 'y.')
#plt.show()
return x_downsamp
def sample_cov(X):
# Check the math here https://www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htm
# Estimation of covariance matrices: https://en.wikipedia.org/wiki/Covariance_matrix
N = X.shape[0] # rows of X: number of observations
D = X.shape[1] # columns of X: number of variables
mean_col = 1j*np.ones(D) # has to define a complex number for keeping the imaginary part
for col_indx in range(D):
mean_col[col_indx] = np.mean(X[:,col_indx])
Mx = X - mean_col # Zero mean matrix of X
S = np.dot(Mx.H,Mx) / (N-1) # sample covariance matrix
return np.conj(S), Mx # add a np.conj() because when I compare to the numpy.cov() the result only be the same when adding the conjucate... strange.
def zca_whitening_matrix(X0):
"""
Function to compute ZCA whitening matrix (aka Mahalanobis whitening).
INPUT: X0: [N x D] matrix.
Rows: Observations
Columns: Variables
ZCAMatrix: [D x D] transformation matrix
OUTPUT: Y = (X0 -X_mean)W. Its covariance matrix is identity matrix
"""
N = X0.shape[0]
# Sample Covariance matrix [column-wise variables]: Sigma = (X-mu)' * (X-mu) / (N-1)
sigma0 = np.cov(X0, rowvar=False) # [D x D]
sigma, Mx = sample_cov(X0)
XhX = np.dot(Mx.H, Mx) # (N-1)*sigma should be the same but there is a conjugate difference. Don't know why...
# Singular Value Decomposition. X = U * np.diag(S) * V
U,S,Vh = np.linalg.svd(XhX)
# U: [D x D] eigenvectors of sigma.
# S: [D x 1] eigenvalues of sigma.
# V: [D x D] transpose of U
# Whitening constant: prevents division by zero
epsilon = 0 #1e-1000
ZCAMatrix = np.sqrt(N-1) * np.dot(Vh.H, np.dot(np.diag(1.0 / np.sqrt(S + epsilon)), Vh)) # [M x M]
Y = np.dot(Mx, ZCAMatrix)
cov_Y = np.cov(Y, rowvar=False)
plt.imshow(abs(cov_Y))
plt.show()
print(np.diag(cov_Y))
return Y
def array2tuple(array, D):
theta_tuple = ()
for i in range(D):
arr = [(np.real(array[i]), np.imag(array[i]))]
theta_tuple = theta_tuple + tuple(map(tuple,arr))
return theta_tuple
def cal_cost(rx_error):
m = len(rx_error)
A = rx_error.reshape([m,1]) # y_prediction - y
cost = 1.0 / (2 * m) * np.dot(np.conj(A).T, A)[0][0]
return cost
def readbin1(filename, nsamples, fsize, rubish):
bytespersample = 4
samplesperpulse = nsamples
total_samples = fsize/bytespersample
total_pulse = total_samples / samplesperpulse
file = open(filename,'rb')
file.seek((total_pulse-1) * bytespersample*samplesperpulse+ bytespersample*rubish) # find the last pulse
x = file.read( bytespersample*samplesperpulse)
file.close()
fmt = ('%sh' % (len(x) /2)) # e.g. '500h' means 500 shorts
x_sig = np.array(struct.unpack(fmt, x)).astype(float) # convert to complex float
rx_sig = -x_sig[0::2] + 1j*x_sig[1::2] # Important! Here I added a negtive sign here for calibrate the tx rx chain. There is a flip of sign somewhere but I cannot find.
rx_sig = rx_sig / 32767.0
return rx_sig
def get_slice(data, pulselenth):
data_ch0 = []
data_ch1 = []
for i in range(0,len(data), 2*pulselenth):
data_ch0[i:i+pulselenth] = data[i:i+pulselenth]
data_ch1[i:i+pulselenth] = data[i+pulselenth:i + 2*pulselenth]
return data_ch0, data_ch1
def last_pulse (filename, nsamples, rubish):
fsize = int(os.stat(filename).st_size)
signal = readbin1(filename, nsamples, fsize, rubish)
signal_ch0, signal_ch1 = get_slice(signal, 256)
return signal_ch0, signal_ch1
def gd(theta, rx_error_sim, X, D):
# gradient decent
eta = 1.0/D# 1 # learning rate
m = len(rx_error_sim)
c2 =1#0.45 # this is a amplitude calibration coefficient = Rx/Tx, this will also make the convergence faster.
theta = theta -(1.0 / m) * eta * np.dot(np.conj(X.T), rx_error_sim / c2)
#theta = theta - (1.0 / m) * eta * np.dot(np.real(X.T), np.real(rx_error_sim)/ c2)
y_hat = np.dot((X), theta) # y_hat = prediction for cancellation signal
cost_history = 10*np.log10(cal_cost(rx_error_sim))
return theta, y_hat, cost_history
def tx_template(N, D, upsamp_rate):
j = 1j
fs = 28e6*upsamp_rate # Sampling freq
tc = upsamp_rate * N / fs # T=N/fs#Chirp Duration
t = np.linspace(0, tc, upsamp_rate*N)
f0 = -14e6 # Start Freq
f1 = 14e6 # fs/2=1/2*N/T#End freq
K = (f1 - f0) / tc # chirp rate = BW/Druation
# win = np.blackman(N)
# win=np.hamming(N)
win=1
# Sine wave
#x0 = 1.0 * np.sin(2 * np.pi * fs / N * t)
#xq0 = 1.0 * np.sin(2 * np.pi * fs / N * t - np.pi / 2)
# Chirp
x0 = 1 * np.sin(2 * np.pi * (f0 * t + K / 2 * np.power(t, 2))) # use this for chirp generation
xq0 = 1 * np.sin(2 * np.pi * (f0 * t + K / 2 * np.power(t, 2)) - np.pi / 2) # use this for chirp generation
# Square Wave
#x0 = np.concatenate((np.zeros(2048),np.ones(2048)))
#xq0 = np.concatenate((np.zeros(2048),np.ones(2048)))
x_cx = x0 + j * xq0
x_cx = np.multiply(x_cx, win)
#x_cx = np.reshape(x_cx,[N,1])
#x_cx_upsamp = upsampling(x_cx, upsamp_rate)
# Dimension of X matrix.Total_delay_tap
x_cx_delay = j*np.ones([upsamp_rate * N,D])
for i in range (D):
#x_basis = np.roll(x_cx_upsamp, 0 + i) # here
#if i > 0:
# x_basis[:i] = 0
#x_cx_delay[:,i] = np.roll(x_cx[:,0], 1*i) # here #x_cx_delay[:,i] = np.roll(x_cx, 20*i) # here
x_cx_delay[:, i] = np.roll(x_cx[:], -1 * i)
#x_cx_delay[:, 0] = np.roll(x_cx_upsamp, 0)#np.zeros(N)
#x_cx_delay[:, 1] = np.roll(x_cx_upsamp, 100) # x_cx
X = np.matrix(x_cx_delay)
return X
def save_tx_canc(filename, N,y_hat):
# save y_hat
#y_hat = np.concatenate((y_hat[N - 2028+2048:], y_hat[:N - 2028+2048]), axis=0) # fixed delay for tx chain from FPGA to RF out which need to measured for cancellation path
y_hat = np.concatenate((y_hat[N:], y_hat[:N]),axis=0) # fixed delay for tx chain from FPGA to RF out which need to measured for cancellation path
y_cxnew = np.around(32767 * y_hat ) # numpy.multiply(y_cx,win) 6.9
yw = np.zeros(2 * N)
for i in range(0, N):
yw[2 * i + 1] = np.imag(y_cxnew[i]) # tx signal
yw[2 * i] = np.real(y_cxnew[i]) # tx signal
yw = np.append(yw, yw[-2]) # Manually add one more point at the end of the 4096 points pulse to match the E312 setup
yw = np.append(yw, yw[-2])
yw = np.int16(yw) # E312 setting --type short
# filetime = str(time.gmtime().tm_sec)
# data = open('usrp_samples4097_chirp_28MHz'+filetime+'.dat', 'w')
data = open(filename, 'w')
data.write(yw)
data.close()
def rx_sim(N):
j = 1j
fs = 28e6 # Sampling freq
tc = N / fs # T=N/fs#Chirp Duration
t = np.linspace(0, tc, N)
f0 = -14e6 # Start Freq
f1 = 14e6 # fs/2=1/2*N/T#End freq
K = (f1 - f0) / tc # chirp rate = BW/Druation
#win = np.blackman(N)
# win=np.hamming(N)
win=1
Amp = 1
phi_init1 = 0/180#-105.0 / 180.0 * np.pi
phi_init2 = 0/180#-105.0 / 180.0 * np.pi
dc_offset = 0
# Sine wave
#y0 = Amp * np.sin(2 * np.pi * fs / N * t)
#yq0 = Amp * np.sin(2 * np.pi * fs / N * t - np.pi / 2)
#y0 = Amp * np.sin(2 * np.pi * fs / N * t + phi_init1) + dc_offset # + numpy.sin(4*numpy.pi*fs/N*t)# just use LO to generate a LO. The
#yq0 = Amp * np.sin(2 * np.pi * fs / N * t - np.pi / 2 + phi_init2) + dc_offset # + numpy.sin(4*numpy.pi*fs/N*t-numpy.pi/2)
# Chirp
y0 = Amp * np.sin(phi_init1 + 2 * np.pi * (f0 * t + K / 2 * np.power(t, 2))) + dc_offset # use this for chirp generation
yq0 = Amp * np.sin(phi_init2 + 2 * np.pi * (f0 * t + K / 2 * np.power(t, 2)) - np.pi / 2) + dc_offset # use this for chirp generation
# Square
#y0 = np.concatenate((np.zeros(2048),np.ones(2048)))
#yq0 = np.concatenate((np.zeros(2048),np.ones(2048)))
y = y0 + j * yq0
y = np.multiply(y, win)
y_cx_delay = np.concatenate((y[N-0:], y[:N-0]), axis=0)
y = np.reshape(y_cx_delay, [N, 1])
return y
def main(theta, N=4096, D=2, upsamp_rate=10, rx_error_sim = np.zeros([4096, 1])):
print('D = ', D)
start = timeit.default_timer()
###################################################
# Generate the X matrix and read rx residual signal
###################################################
# Generate the X matrix
X0 = tx_template(N, D, upsamp_rate) # including upsampling
# whitening X matrix
X_whitening = zca_whitening_matrix(X0)
# up-sampling X matrix
X_upsamp = X0# X_whitening # template signal up-sampling
X = X_upsamp
# read rx residual signal
rx_error0 = np.reshape(read_last_pulse("usrp_samples_loopback.dat",N), [N,1])
# up-sampling rx residual signal
rx_error_upsamp = upsampling(rx_error0,upsamp_rate) # residual signal up-sampling
rx_error = rx_error_upsamp
# Read the parameter vector history from function input
theta_cx_in = np.zeros([ D,1])
for i in range( D):
theta_cx_in[i] = np.array(theta)[i][0] + 1j*np.array(theta)[i][1]
##################
# Gradient Decent
##################
#theta_cx_out, y_hat_upsamp, cost_history = gd(theta_cx_in, rx_error, X)
theta_cx_out, y_hat_upsamp, cost_history = gd(theta_cx_in, rx_error_sim, X, D) #uncomment this line out when using simulated received signal
#######################
# Prepare data for FPGA
#######################
y_hat = downsampling(y_hat_upsamp, upsamp_rate) # cancellation signal down-sampling for DAC fs
save_tx_canc('usrp_samples4096_chirp_28MHz_fixed_delayed.dat', N, y_hat) # add FGPA tx delay inside this function
theta_real_out = []
theta_imag_out = []
theta_tuple_out = ()
for i in range(D):
theta_real_out.append(float(np.matrix.tolist(np.real(np.matrix(theta_cx_out.T)))[0][i]))
theta_imag_out.append(float(np.matrix.tolist(np.imag(np.matrix(theta_cx_out.T)))[0][i]))
arr = [(theta_real_out[i],theta_imag_out[i] )]
theta_tuple_out = theta_tuple_out + tuple(map(tuple, arr))
stop = timeit.default_timer()
#print('Time: ', stop - start)
print('cost_history:', cost_history)
#print('Theta_cx_out:', theta_cx_out)
#return theta_tuple_out
return theta_tuple_out, y_hat #uncomment this line out when using simulated received signal
if __name__ == "__main__": # Change the following code into the c++
# Parameters
mu, sigma = 0, 0.05
np.random.seed(0)
N = 4096 # This also limit the bandwidth. And this is determined by fpga LUT size.
D = 1
upsamp_rate = 10
# Initial values for theta, y and y_hat
theta0 = 0.0#+0.1j# *np.random.randn(1, 1) + 0.1j # parameter to learn
theta = np.repeat(theta0, upsamp_rate * D) # this should be a D * 1 column vector
theta_tuple = ()
theta_tuple = array2tuple(theta, theta.shape[0])
s = np.random.normal(mu, sigma, [N*upsamp_rate, 1])
y0 = rx_sim(N)
y = upsampling(y0, upsamp_rate) # received signal up-sampling
y_withnoise = y + s
y = y#y_withnoise
y_hat = np.zeros([N, 1])
# the main loop
start = timeit.default_timer()
for itt in range(30):
y_hat = upsampling(y_hat, upsamp_rate) # cancelation signal up-sampling
#plt.plot(y, '*-')
#plt.plot(y_hat,'*-')
#plt.show()
rx_error_sim = y_hat + y # uncomment this line out when using simulated received signal
theta_tuple, y_hat = main( theta_tuple, N, upsamp_rate * D, upsamp_rate, rx_error_sim) # uncomment this line out when using simulated received signal
#theta_tuple = main(theta_tuple, N, upsamp_rate* D)
#print(itt)
#mport matplotlib.pyplot as plt
#plt.plot(rx_error_sim,'*-')
stop = timeit.default_timer()
print('Time: ', stop - start)