## 5 Dimensional Transformation Residual Network (DTRN)
The real vector is mapped to 4 vectors with a shape of 106 \(\times\) 64, and a 4-channel 5 \(\times\) 5 convolutional layer is then used to extract the structural features of the input vectors. **V** is fixed to a real vector with a shape of \(256\times 106\) after the Transformation layer. Based on the dimensional information recorded earlier, the ports-mapping layer then maps this Real vector to the input channels of the first convolutional layer based on the antennas. The Real vector is mapped to a 4-channel \(106\times 64\) convolutional layer input vector, and a \(5\times 5\) convolutional layer is then used to extract the structural features of the input vector. Following the \(5\times 5\) convolutional layer, the D-block adds a batch normalization layer (BatchNorm layer) and a max pooling layer (Maxpool layer), which serve to prevent overfitting and aggregate global information for subsequent processing of the network. D-block utilizes spatial invariance to preserve structural features, while employing a Transformation layer to modify the dimensions to meet the requirements of subsequent networks. Let \(\mathbf{V_{x}}\) represent the input vector of a \(5\times 5\) convolutional layer, where \(\mathbf{V_{x}}\in\mathbb{R}^{106\times 64\times 4}\). This can be understood as a three-dimensional vector composed of resource grids, antennas, and SRS ports. The original feature vector \(\mathbf{V}\)'s complex dimensions are unfolded into the antenna dimension of \(\mathbf{V_{x}}\), and the third dimension of \(\mathbf{V_{x}}\) can be abstractly regarded as a multi-port representation of time-frequency resources. There are similarities between the Ports mapping layer and the SRS logical port generation process. In addition, the D-block enables FPLA to have excellent scalability, as it only requires generic modifications to the Transformation layer within the D-block. This allows a single deep learning network to be utilized for authentication schemes across various channel bandwidths and network standards. The structure of DTRN is shown in Figure 5. D-block, along with four R-blocks and one Output-block, collectively forms the main structure of the DTRN network. The architectural design incorporates the concept of residual connections, which was proposed by [PERSON] et al. [33]. Residual connections effectively address issues such as gradient vanishing in deep neural networks, and they are employed in the R-Block. There are two types of R-block structures in DTRN. The first one (R-block-1) does not contain \(1\times 1\) convolutional layer, and the other one (R-block-2, R-block-3, R-block-4) contains \(1\times 1\) convolutional layer, whose function is to adjust the number of channels, ensuring that the vector of residual connection bypass and the transformed vector from the C-block belong to the same vector space. The output-block contains a global convergence layer, a fully connected layer and a Softmax layer. Their roles are to collect information, implement classification, and output mapping, respectively. Ultimately, the DTRN outputs a probability distribution that represents the legitimacy of the UE, determining whether the UE corresponding to the input vector is legitimate. The four R-blocks in the DTRN network utilize convolutional layers with different parameters, which are listed in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Block** & **D-Block** & **R-Block-1** & **R-Block-2** & **R-Block-3** & **R-Block-4** \\ \hline Quantity of convs & 1 & 4 & 4 & 4 & 4 \\ Input channels & 4 & 64 & 64 & 128 & 256 \\ Output channels & 64 & 64 & 128 & 256 & 512 \\ Conv kernel & \(5\times 5\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) & \(3\times 3\) \\ Stride & 2 & 2 & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of DTRN convolutional layers. Figure 5: Architecture of DTRN. Assume that the input to the convolutional block (C-Block) is vector \(\mathbf{a}\), the objective is to train and obtain a function \(f(\mathbf{a})\) to serve as the input to the ReLU layer.