kumanna

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

def get_dominant_paths(Y_DD):
    threshold = 0.1 * np.max(np.abs(Y_DD))
    M, N = Y_DD.shape
    K_list = []

kumanna

import numpy as np

M = 4
N = 4

GAMMA = np.diag(np.exp(-1j * np.arange(M) * 2 * np.pi / M))
DELTA = np.diag(np.exp(1j * np.arange(N) * 2 * np.pi / N))

def gamma_deriv(GAMMA, n):
    return np.diag((-np.arange(M) * 1j * 2 * np.pi / M))**n @ GAMMA

kumanna

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(-10, 10, 0.01)
f = lambda x : x*x/4
y = f(x)

x0 = np.abs(np.random.randn() * 5) # initial point
x_vals = []
y_vals = []

kumanna

import numpy as np
import matplotlib.pyplot as plt

Ts = 0.0001
fs = 1 / Ts
t = np.arange(0, 1, Ts)
V_pi = 5
V_bias = 2.5
f_dither = 1000

kumanna

import numpy as np
import matplotlib.pyplot as plt

Ts = 0.0001
fs = 1 / Ts
t = np.arange(0, 1, Ts)
V_pi = 5
V_bias = 2.5
f_dither = 1000

kumanna

import numpy as np

NFFT = 16
NCP = 4
# Generate QPSK symbols

symbols = np.sign(np.random.randn(NFFT)) + 1j * np.sign(np.random.randn(NFFT))

# LTI channel
# Generate h according to a power delay profile

kumanna

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import freqz

def solve(omega1, omega2, phi1, phi2):
    # Let's formulate the equations accordingly
    A = np.array([
        [-np.sin(phi1 + omega1), 0, np.sin(omega1), 0],
        [-np.cos(phi1 + omega1), 0, np.cos(omega1), 1],
        [0, -np.sin(phi2 + omega2), np.sin(omega2), 0],

kumanna

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import freqz

INPUT_OMEGAS = np.array([np.pi / 4, np.pi / 3])
INPUT_PHIS = np.array([np.pi / 6, 2 * np.pi / 3])

def solve(omega1, omega2, phi1, phi2):
    # Let's formulate the equations accordingly
    A = np.array([

kumanna

kumanna

import numpy as np
import matplotlib.pyplot as plt

# We always send +1
delta_omega = 0.01 * np.pi
n = np.arange(100)
received_symbols = np.exp(1j * delta_omega * n) # AWGN here
#plt.scatter(np.real(received_symbols), np.imag(received_symbols))
#plt.show()
quantized_points = np.array([1, -1, 1j, -1j])