batch_size = 500
nepoch = 50
#-------------------------------------------#
# Here should be the data_preparing function
# It is expected to return:
# train_inp, train_out, val_inp, and val_out
#-------------------------------------------#
h_real = np.load('D://Github Repositories/mmWave Beam Management/H_Matrices FineGrid/MISO_Static_FineGrid_Hmatrices_real.npy')[:,antenna_sel]
h_imag = np.load('D://Github Repositories/mmWave Beam Management/H_Matrices FineGrid/MISO_Static_FineGrid_Hmatrices_imag.npy')[:,antenna_sel]
loc = np.load('D://Github Repositories/mmWave Beam Management/H_Matrices FineGrid/MISO_Static_FineGrid_UE_location.npy')
# h_real = np.load('/Users/yh9277/Dropbox/ML Beam Alignment/Data/H_Matrices FineGrid/MISO_Static_FineGrid_Hmatrices_real.npy')
# h_imag = np.load('/Users/yh9277/Dropbox/ML Beam Alignment/Data/H_Matrices FineGrid/MISO_Static_FineGrid_Hmatrices_imag.npy')
BS_loc = [641,435,10]
num_samples = h_real.shape[0]
gc = GaussianCenters(n_clusters=8, arrival_rate = 1000, cluster_variance = 5, seed=r_seed)
sel_samples = gc.sample()
# gc.plot_sample(sel_samples)
# sel_samples = np.arange(10000)
h_real = h_real[sel_samples,:]
h_imag = h_imag[sel_samples,:]
loc = loc[sel_samples,:]
plt.figure(figsize=(8,6))
plt.scatter(loc[:,0], loc[:,1], s=1, label='UE')
plt.scatter(BS_loc[0], BS_loc[1], s=10, marker='s', label='BS')
plt.legend(loc='lower left')
plt.xlabel('x (meter)')
plt.ylabel('y (meter)')
plt.title('UE Distribution')
plt.show()
val_h,val_y = val_batch
val_y = torch.from_numpy(val_y).float()
# val_x = torch.from_numpy(estimate_h(val_h, model, num_antenna)).float()
val_x, val_z = estimate_h(val_h, model, num_antenna, h_est_force_z)
if h_est_force_z:
val_z = torch.from_numpy(val_z).float()
loss = loss_fn(model(val_x,val_z),val_y)
return loss.item()
dataset = GaussianCenters(possible_loc=loc[:,:2],
n_clusters=n_clusters, arrival_rate = arrival_rate, cluster_variance = cluster_variance)
test_gains_maml = np.zeros((len(num_of_beams),ntest,dataset.n_clusters*dataset.arrival_rate))
test_gains_scratch = np.zeros((len(num_of_beams),ntest,dataset.n_clusters*dataset.arrival_rate))
test_gains_dft = np.zeros((len(num_of_beams),ntest,dataset.n_clusters*dataset.arrival_rate))
for i,N in enumerate(num_of_beams):
print(str(N) + '-beams Codebook')
# Model:
# ------
model = AnalogBeamformer(n_antenna = num_antenna, n_beam = N)
maml = MAML(model, lr=fast_lr, first_order=True)
# Training:
# ---------
optimizer = optim.Adam(model.parameters(),lr=meta_lr, betas=(0.9,0.999), amsgrad=False)
nepoch = 20
h_est_force_z = False #
#-------------------------------------------#
# Here should be the data_preparing function
# It is expected to return:
# train_inp, train_out, val_inp, and val_out
#-------------------------------------------#
h_real = np.load('D://Github Repositories/mmWave Beam Management/H_Matrices FineGrid/MISO_Static_FineGrid_Hmatrices_real.npy')[:,antenna_sel]
h_imag = np.load('D://Github Repositories/mmWave Beam Management/H_Matrices FineGrid/MISO_Static_FineGrid_Hmatrices_imag.npy')[:,antenna_sel]
loc = np.load('D://Github Repositories/mmWave Beam Management/H_Matrices FineGrid/MISO_Static_FineGrid_UE_location.npy')
# h_real = np.load('/Users/yh9277/Dropbox/ML Beam Alignment/Data/H_Matrices FineGrid/MISO_Static_FineGrid_Hmatrices_real.npy')
# h_imag = np.load('/Users/yh9277/Dropbox/ML Beam Alignment/Data/H_Matrices FineGrid/MISO_Static_FineGrid_Hmatrices_imag.npy')
BS_loc = [641,435,10]
num_samples = h_real.shape[0]
gc = GaussianCenters(n_clusters=10, arrival_rate = 1000, cluster_variance = 10)
sel_samples = gc.sample()
# gc.plot_sample(sel_samples)
# sel_samples = np.arange(10000)
h_real = h_real[sel_samples,:]
h_imag = h_imag[sel_samples,:]
loc = loc[sel_samples,:]
plt.figure(figsize=(8,6))
plt.scatter(loc[:,0], loc[:,1], s=1, label='UE')
plt.scatter(BS_loc[0], BS_loc[1], s=10, marker='s', label='BS')
plt.legend(loc='lower left')
plt.xlabel('x (meter)')
plt.ylabel('y (meter)')
plt.title('UE Distribution')
plt.show()