# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
## 3 ResNet with SLAB and CLAB  
### ResNet for Positioning Tasks  
ResNets are designed to address the challenges arising from deeper architectures, particularly the vanishing gradient problem. ResNet introduces residual connections that allow the network to learn the identity function, making it easier to train deeper networks by mitigating gradient issues. For this paper, a modified ResNet architecture, as shown in Figure 2, is employed for position prediction based on the CIR and time-domain data. The ResNet architecture is composed of a feature input layer followed by several residual convolution blocks, each designed to refine the feature representations progressively. The structure is described as follows:  
1. Input Layer: The input layer is set up to handle complex inputs. It receives features from the data and then processes them through the 3 \(\times\) 3 convolutional layer with 64 filters, a batch normalization layer, and a Rectified Linear unit (ReLU);
2. Residual Blocks: The network consists of four residual blocks, each containing: 1. Convolution Layer: A kernel size of 3 \(\times\) 3 with 64 filters is employed to extract features; 2. Batch Normalization: Applied to stabilize training; 3. ReLU Activation: Introduced to add non-linearity; 4. Dropout: Regularization to prevent overfitting. The dropout rate is 0.2; 5. Addition Layer: This layer enables the network to learn residuals by adding the input of the block to its output.
3. Fully Connected Layers: After the residual blocks, the output is processed through a series of fully connected layers that reduce the dimensionality before producing the final 2-output layer, corresponding to the x- and y-coordinates.
4. Output Layer: The final output layer, with 2 neurons, predicts the position.  
The residual convolution block function defines the structure of each residual block, where the key feature is the addition layer that allows the network to learn both the transformed features and the residuals, facilitating deeper network training.  
One of the primary objectives of this paper is to evaluate the noise and interference resilience of ResNet models in the context of indoor positioning. Since real-world positioning tasks often involve noisy data and interference, it is crucial to assess how well these models perform in such conditions. ResNet has good noise resilience due to its residual connections, which allow the network to maintain stable performance even in the presence of noise. By incorporating residual blocks, the ResNet architecture is designed to learn both the underlying patterns and the noise, improving its ability to generalize in noisy environments. Additionally, the inclusion of dropout and batch normalization in both architectures helps prevent overfitting and stabilizes training, which is particularly important when dealing with noisy inputs. Note that only using the average strategy could weaken the salient features; thus, we use max-pooling to gather other important information about distinctive object features to infer attention maps. The sigmoid activation layer is used to obtain the probability of which channel to focus on. In other words, CLAB is designed to focus on which channel is important for the given input feature map.