목록Wireless Comm. (94)

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
이거 성능 왜 높게 나옴?

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 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 torch.utils.data import DataLoader, Dataset def sobel_filter(img,..
Wireless Comm./CISL 2023. 8. 7. 17:24
Custom Dataset(Gaussian Filtered Cifar 10)

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 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 torch.utils.data import DataLoader, Dataset import os os.environ[..
Wireless Comm./CISL 2023. 8. 4. 15:43
AWGN_cifar10_20230804

######################## Library ######################## import cv2 import math import torch import torchvision import numpy as np 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 ######################## GPU Check ######################## device = torch.d..
Wireless Comm./CISL 2023. 8. 4. 14:53
DeepJSCC

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 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 torch.utils.data import DataLoader, Dataset device = torch.device..
Wireless Comm./CISL 2023. 8. 2. 22:06
AWGN(pi=1024 si=0)

Wireless Comm./CISL 2023. 7. 28. 10:03
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