kaira.models.image.Yilmaz2023DeepJSCCNOMAEncoder

Inheritance diagram of Yilmaz2023DeepJSCCNOMAEncoder — Inheritance diagram for Yilmaz2023DeepJSCCNOMAEncoder

class kaira.models.image.Yilmaz2023DeepJSCCNOMAEncoder(N=64, M=16, in_ch=4, csi_length=1, *args: Any, **kwargs: Any)[source]

Bases: Tung2022DeepJSCCQ2Encoder

DeepJSCC-NOMA Encoder Module [Yilmaz et al., 2023].

This encoder transforms input images into latent representations. This class extends the Tung2022DeepJSCCQ2Encoder class with parameter adaptation as used in the paper Yilmaz et al. [2023].

Methods

`__init__`	Initialize the DeepJSCCNOMAEncoder.
`forward`	Forward pass through the encoder.

Attributes

bandwidth_ratio

Calculate the bandwidth ratio of the model.

__init__(N=64, M=16, in_ch=4, csi_length=1, *args: Any, **kwargs: Any)[source]

Initialize the DeepJSCCNOMAEncoder.

Parameters:

N (int, optional) – Number of channels in the network.
M (int, optional) – Latent dimension of the bottleneck representation.
in_ch (int, optional) – Number of input channels. Defaults to 4.
csi_length (int, optional) – The number of dimensions in the CSI data. Defaults to 1.
*args – Variable positional arguments passed to the base class.
**kwargs – Variable keyword arguments passed to the base class.

property bandwidth_ratio: float

Calculate the bandwidth ratio of the model.

Returns:: The bandwidth ratio.
Return type:: float

forward(x: Tensor, csi: Tensor, *args: Any, **kwargs: Any) → Tensor

Forward pass through the encoder.

Parameters:

x (torch.Tensor) – The input image tensor.
csi (torch.Tensor) – Channel State Information tensor.
*args – Additional positional arguments.
**kwargs – Additional keyword arguments.

Returns:

The encoded latent representation.

Return type:

torch.Tensor