목록Wireless Comm./Python (24)

UOMOP

WSR maximization using WMMSE for MIMO-BC beamforming design using Python

import io import scipy.io as sio import numpy as np from tqdm import tqdm import time import math import matplotlib.pyplot as plt K = 4 # num of users P = 8 # Tx AnT. Q = 2 # Rx AnT. iter = 100 SNRdB = 10 SNR = 10.0 ** (SNRdB/10.0) E_tx = 10.0 u = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] def conj_tran(input) : return np.transpose(input.conj()) #np.random.seed(12334) # Hk 행렬 생성 #H = [] B = [] B_b..
Wireless Comm./Python 2023. 9. 15. 14:26
***AutoEncoder cifar10(color) 1dB, 10dB, 20dB

import math import torch import random import torchvision import torch.nn as nn from tqdm import tqdm import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.transforms as tr from torch.utils.data import DataLoader import numpy as np args = { 'BATCH_SIZE' : 64, 'LEARNING_RATE' : 0.001, 'NUM_EPOCH' : 20, 'SNR_dB' : [1, 10, 20], 'latent_dim' :..
Wireless Comm./Python 2023. 6. 8. 20:25
cifar10 HPF filter size에 따른 결과 확인

import cv2 import math import time import torch import random import torchvision import numpy as np from PIL import Image import torch.nn as nn from numpy import sqrt from tqdm import trange import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.transforms as tr from torchvision import datasets from sklearn.preprocessing import StandardScal..
Wireless Comm./Python 2023. 6. 8. 19:28
***Cifar10_AE(color)_20230608

SNRdB_list = args['SNR_dB'] learning_rate = args['LEARNING_RATE'] epoch_num = args['NUM_EPOCH'] trainloader = trainloader Rayleigh = 0 model_name_without_SNRdB = 'model_AWGN(color)'​ import math import torch import random import torchvision import torch.nn as nn from tqdm import tqdm import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.tr..
Wireless Comm./Python 2023. 6. 8. 16:40
Traditional AutoEncoder Cifar10(gray) 2023 06 07

import math import torch import torchvision import torch.nn as nn import torch.optim as optim import torch.nn.functional as f import matplotlib.pyplot as plt import torchvision.transforms as tr from torch.utils.data import DataLoader args = { 'BATCH_SIZE' : 50, 'LEARNING_RATE' : 0.001, 'NUM_EPOCH' : 80, 'SNR_dB' : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], 'latent_dim' : 100, '..
Wireless Comm./Python 2023. 6. 7. 19:05
send img with contour : 20230603

import cv2 import math import time import torch import random import torchvision import numpy as np from PIL import Image import torch.nn as nn from numpy import sqrt from tqdm import trange import torch.optim as optim import torch.nn.functional as F import matplotlib.pyplot as plt import torchvision.transforms as tr from torchvision import datasets from sklearn.preprocessing import StandardScal..
Wireless Comm./Python 2023. 6. 3. 01:56
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