# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 3 CTI-Aware Rate Adaptation Framework
### CTI Identification  
The primary objective of CTI identification is to estimate the power levels of interfering signals within the target spectrum, considering the types of signals causing the interference. Traditional filter-based detection methods struggle to accurately estimate the power levels of each signal, and decode signal methods are limited to estimating only specific signals. To overcome these limitations, we adopted an ML-based approach capable of accurately estimating the average power of CTI signals present in the received RF waveform. More specifically, it estimates the average power for each type of wireless technology used by these signals. The proposed MobileNet [38]-based CTI identification model, CTINet, is illustrated in Figure 4.  
Figure 3: Time-triggered WLAN scenarios under CTI. The signal received by the AP is down-converted and digitized into a set of raw I/Q samples. During the time duration \(T_{scan}\), the received signal includes both WLAN signals and CTI signals. This raw signal sample can be directly input into the model or preprocessed for signal identification purposes. We use the STFT to effectively capture the time-frequency domain characteristics of CTI signals and to facilitate the estimation of power density within the sample. Since the window size of the STFT determines the time and frequency resolution, an appropriate window size should be selected based on the characteristics of the wireless technology used by the target CTI signals [24]. If other characteristics of CTI signals, such as modulation schemes, need to be considered for interference mitigation, a multi-task learning-based approach that can derive these features simultaneously along with power estimation can be employed. Interested readers are referred to our previous work [20]. The transformed STFT sample is converted into a grayscale image and input into the MobileNet backbone. In our framework, we assume that the power of WLAN signals and noise are estimated using the preamble fields of WLAN [34], rather than by the model itself. As a result, CTINet focuses on identifying the types of wireless technologies causing CTI in the input sample and estimating the average power for each:  
\[\hat{P}=f_{\theta}(I), \tag{1}\]  
where:  
\(\hat{P}=[\hat{P}^{(1)},\hat{P}^{(2)},\dots,\hat{P}^{(M)}]^{\top}\): Predicted average power vector for the \(M\) CTI signal types; \(f_{\theta}\): CTI identification model parameterized by \(\theta\); \(I\): An image representation obtained by applying the STFT to the received signal over the duration \(T_{\text{scan}}\). The proposed CTINet uses MobileNetV3 [38] as the backbone, providing high performance in resource-constrained environments. The model takes a grayscale STFT image as input and extracts various features of CTI signals in the time, frequency, and power dimensions through multiple convolutional layers. A Flatten layer then converts the multi-dimensional feature maps into a one-dimensional vector, which is passed to a Dense layer. The Dense layer effectively combines the time-frequency features using this vector, learning key patterns necessary to distinguish each signal's type and predict their respective power levels. Finally, the learned vector is connected to the output layer, performing multi-output regression to simultaneously predict the average power of each signal's type. To train the model, a loss function \(L(\theta)\) is defined to minimize the difference between the actual and predicted power levels of the CTI signals:  
\[L(\theta)=\frac{1}{N}\sum_{i=1}^{N}\sum_{m=1}^{M}\left|P_{i}^{(m)}-\hat{P}_{i} ^{(m)}\right|, \tag{2}\]  
where:  
Figure 4: Cross-technology interference signal identification model. \(N\) is the total number of training samples;  
\(P_{i}^{(m)}\) is the actual average power of CTI signal type \(m\) in sample \(i\);  
\(\tilde{P}_{i}^{(m)}\) is the predicted average power of CTI signal type \(m\) in sample \(i\).