# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 5 Results and Analysis  
### Performance of CTI Identification  
Table 5 presents a comparison of the MAE according to the input STFT image size of the CTI identification model (with \(\alpha=1\)), with each model's FLOPs indicated in parentheses. The MAE of identification models decreases as the image size increases. The model that takes 128 \(\times\) 128 images as input achieves an MAE of 1.322 even at 0 dB, meaning it can estimate the average power within \(\pm\)1.322 dBm. However, as the input size increases, the number of convolution operations using the sliding window approach also grows, leading to a higher FLOPs due to the larger input. In contrast, the model that takes 32 \(\times\) 32 STFT images has a low FLOPs of 3.232 M but shows poor performance, with an MAE of 6.424 even at an SNR of 10 dB. This could directly lead to degraded rate adaptation performance in the CARA framework, as the error in the SINR calculation may affect the determination of the MCS index during rate adaptation. Regarding the model size, all comparative models have the same size of 0.976 \(\times\) 10\({}^{6}\) parameters due to the characteristics of model, which mainly consists of convolution and global average pooling operations; thus, the model size was not affected by the input size [41; 42].  
MobileNetV3 supports hyperparameters for model lightweighting. Among these, \(\alpha\), also known as the depth multiplier, adjusts the model's width (i.e., the number of filters) by a certain ratio. The performance comparison of CTINet according to \(\alpha\) is shown in Table 6.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Simulation time & 10 s \\ Number of APs & 1 \\ Number of STAs & 3 \\ Tx to Rx distance & 10 m \\ \(T_{slot}\) & 1 ms \\ Inter-frame space & 16 μs \\ Payload size & 500 bytes \\ Ack size & 14 bytes \\ MCS feedback size & 20 bytes \\ Feedback interval (MB) & 1 ms \\ Success threshold (PE) & 20 \\ Failure threshold (PE) & 5 \\ Packet loss threshold & 10\({}^{-5}\) bit error \\ \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
\begin{table}
\begin{tabular}{l l l l} \hline
**SNR** & \multicolumn{2}{c}{**STFT 32 \(\times\) 32**} & \multicolumn{2}{c}{**STFT 64 \(\times\) 64**} & \multicolumn{1}{c}{**STFT 128 \(\times\) 128**} \\  & **(3.232 MFLOPs)** & **(9.821 MFLOPs)** & **(36.178 MFLOPs)** \\ \hline
0 dB & 6.956 & 3.041 & 1.322 \\
5 dB & 6.751 & 3.035 & 1.308 \\
10 dB & 6.424 & 2.780 & 1.294 \\
15 dB & 5.680 & 2.624 & 1.034 \\
20 dB & 5.669 & 2.525 & 0.999 \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparison of CTINet based on input size.  
The computational complexity of the models mentioned in Section 3.2 is compared using the metric called FLOPs. As \(\alpha\) decreases, the FLOPs and model size (number of parameters) of CTINet-128 decrease from 36.178 M and 0.976 M at \(\alpha=1\) to 7.787 M and 0.109 M at \(\alpha=0.25\). However, we observe that the MAE increases as the model becomes more lightweight from \(\alpha=1\) to \(\alpha=0.25\). Some commercial WiFi APs are equipped with high-performance processors, such as the Broadcom BCM4916, which has four cores running at 2 GHz. The theoretical peak performance of these APs can reach at least 16 GFLOPS (Giga Floating-point Operations Per Second), assuming a minimum of 2 operations per cycle. Based on this, the processing speed comparison results calculated with the model's FLOPs are shown in the CPU (ms) column of Table 6. Considering the model's performance (MAE of 2 or less) and the required processing speed in the network (within \(T_{\text{slot}}\)), we selected CTINet-128-0.5 as the model to use in the rate adaptation of the next experiments. When designing models, it is important to consider the trade-off between computational complexity and performance according to network requirements.