def __init__(self, n_antenna, n_beam):
super(Channel_Encoder, self).__init__()
self.analog_codebook = PhaseShifter(in_features=2*n_antenna, out_features=n_beam, scale=np.sqrt(n_antenna))
self.dense_1 = nn.Linear(in_features = n_beam*2, out_features = n_beam*2)
self.dense_2 = nn.Linear(in_features = n_beam*2, out_features = n_beam*2)
self.relu = nn.ReLU()
self.compute_power = ComputePower(2*n_beam)
def __init__(self, n_antenna, n_beam):
super(Node, self).__init__()
self.analog_codebook = PhaseShifter(in_features=2 * n_antenna,
out_features=n_beam,
scale=np.sqrt(n_antenna))
self.n_antenna = n_antenna
self.n_beam = n_beam
self.compute_power = ComputePower(2 * n_beam)
self.parent = None
self.child = None
def __init__(self, n_antenna, n_beam):
super(Node, self).__init__()
self.analog_codebook = PhaseShifter(in_features=2*n_antenna, out_features=n_beam, scale=np.sqrt(n_antenna))
self.n_antenna = n_antenna
self.n_beam = n_beam
self.compute_power = ComputePower(2*n_beam)
self.dense_1 = nn.Linear(in_features = n_beam, out_features = n_beam)
self.relu = nn.ReLU()
self.softmax = nn.Softmax(dim=1)
self.parent = None
self.child = None
def __init__(self, n_antenna, azimuths, idx):
super(Leaf, self).__init__()
self.analog_codebook = DFT_Codebook_Layer(n_antenna=n_antenna,
azimuths=azimuths)
self.n_beam = len(azimuths)
self.azimuths = azimuths
self.n_antenna = n_antenna
self.idx = idx
self.compute_power = ComputePower(2 * self.n_beam)
self.softmax = nn.Softmax(dim=1)
self.parent = None
self.child = None
def __init__(self, n_antenna, n_wide_beam, n_narrow_beam, trainable_codebook = True, theta = None):
super(Beam_Classifier, self).__init__()
self.trainable_codebook = trainable_codebook
self.n_antenna = n_antenna
self.n_wide_beam = n_wide_beam
self.n_narrow_beam = n_narrow_beam
if trainable_codebook:
self.codebook = PhaseShifter(in_features=2*n_antenna, out_features=n_wide_beam, scale=np.sqrt(n_antenna), theta=theta)
else:
dft_codebook = DFT_codebook_blockmatrix(n_antenna=n_antenna, nseg=n_wide_beam)
self.codebook = torch.from_numpy(dft_codebook).float()
self.codebook.requires_grad = False
self.compute_power = ComputePower(2*n_wide_beam)
self.relu = nn.ReLU()
self.dense1 = nn.Linear(in_features=n_wide_beam, out_features=2*n_wide_beam)
self.dense2 = nn.Linear(in_features=2*n_wide_beam, out_features=3*n_wide_beam)
self.dense3 = nn.Linear(in_features=3*n_wide_beam, out_features=n_narrow_beam)
self.softmax = nn.Softmax()
def __init__(self, n_antenna = 64, n_beam = 64, theta = None):
super(AnalogBeamformer, self).__init__()
self.codebook = PhaseShifter(in_features=2*n_antenna, out_features=n_beam, scale=np.sqrt(n_antenna), theta = theta)
self.beam_selection = PowerPooling(2*n_beam)
self.compute_power = ComputePower(2*n_beam)
def __init__(self, n_antenna, n_beam):
super(Self_Supervised_AnalogBeamformer, self).__init__()
self.codebook = PhaseShifter(in_features=2*n_antenna, out_features=n_beam, scale=np.sqrt(n_antenna))
self.compute_power = ComputePower(2*n_beam)