# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 6 Conclusions  
In this paper, we presented a CTI-aware rate adaptation framework to address the performance degradation caused by CTI in time-triggered WLANs. The proposed framework includes an intelligent model for CTI signal identification and two algorithms supporting CTI-aware rate adaptation: M-CARA and P-CARA. CTINet, built on a lightweight architecture, can estimate the average power levels of heterogeneous wireless technologies used by CTI signals in systems with constrained computing resources. The M-CARA and P-CARA algorithms mitigate the performance degradation of existing algorithms and minimize network overhead by projecting and responding to interference occurrence times based on CTI identification results and their spectrum usage patterns.  
Our signal identification experiments showed that the model in the CARA framework that takes 128 \(\times\) 128 STFT images as input can distinguish heterogeneous wireless technologies used by CTI signals and estimate the average power of those types within an error of \(\pm\)1.414 dBm at an SNR of 0 dB. This result is significant in practical terms because it comes from a model that can perform identification within 1 ms on an AP equipped with a processor offering 16 GFLOPS performance.  
According to comparative experiments on interference mitigation performance, the application of the CARA framework showed performance improvements over existing rate adaptation algorithms in all aspects of goodput, PER, and latency. In particular, the CARA framework exhibited better synergy with measurement-based algorithms that perform explicit interference mitigation based on channel conditions, improving goodput by up to 20.67%, reducing the PER by up to 2.38%, and decreasing the probability of packets exceeding 1 ms latency by 0.932% compared to existing algorithms.  
We expect that the CARA framework will contribute to enhancing the communication performance of real-time applications using time-triggered WLANs by identifying CTI caused by devices employing heterogeneous wireless technologies in unlicensed bands and performing corresponding mitigation strategies.  
Figure 8: Rate selection ratios along with PERs.  
**Author Contributions:** Conceptualization, H.K.; methodology, H.K.; software, H.K.; validation, H.K. and Y.-J.K.; formal analysis, H.K.; investigation, H.K.; resources, H.K.; data curation, H.K. and Y.-J.K.; writing--original draft preparation, H.K. and Y.-J.K.; writing--review and editing, H.K., Y.-J.K. and W.-T.K.; visualization, H.K.; supervision, W.-T.K.; project administration, W.-T.K.; funding acquisition, W.-T.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partly supported by the Technology Innovation Program (RS-2024-00507388, Development of SDF-based AI autonomous manufacturing core technology to advance the automobile industry) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and the Star Professor Research Program of Korea University of Technology and Education in 2024.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.