# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 2 Related Work
### Cross-Technology Interference Mitigation  
One method to prevent performance degradation caused by CTI is to directly communicate with heterogeneous devices causing interference to coordinate and reduce CTL. This approach seeks to mitigate interference through inter-technology communication between devices operating under different wireless standards [10]. However, it faces the challenge of requiring separate protocols to manage or avoid interference across different technologies [28]. While this method may be effective in environments with a limited number of CTI sources, an increase in the diversity of CTI types can significantly complicate device configuration, leading to network management difficulties due to control overhead [29] or energy consumption issues [30]. Furthermore, this approach struggles to address interference caused by non-communication devices such as microwave ovens. Although methods encoding CTI mitigation information solely within the WiFi payload while maintaining compatibility with existing WLAN standards have been proposed, these methods remain limited to specific scenarios, such as mitigating interference with ZigBee devices [11].  
In WLANs, although no specific technologies are designed for CTI mitigation, two common approaches exist: one based on packet errors [13; 14] to reduce interference and another relying on channel state measurements to adapt rates [12] for interference mitigation. Packet error-based methods utilize statistical knowledge of packet transmission success rates. In these methods, the transmitting node sends data packets and calculates the success rate based on acknowledgments received from the receiver, adjusting the transmission rate accordingly to mitigate interference. Although these methods are not dependent on specific interference types and can handle heterogeneous CTI, they are implicit mitigation techniques and thus less responsive to rapid changes in the communication environment [18]. This limitation makes it challenging to achieve effective interference mitigation in complex scenarios where multiple wireless technologies contribute to interference dynamics. Some studies have enhanced interference mitigation performance by incorporating IEEE 802.11 fine timing measurements to estimate the distance to access points (APs) [4] or by learning packet error frequency statistics [31]. However, these approaches are often tied to IEEE 802.11 and do not account for CTI characteristics during training, limiting their effectiveness in environments with diverse CTI sources.  
Measurement-based methods offer explicit mitigation by estimating communication quality through metrics like the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) at the receiver, which are then fed back to the transmitter for rate adaptation [12]. These methods are known to respond better to channel variations than packet error-based techniques because they directly measure channel conditions [32]. However, if the measurement frequency is too low relative to the dynamics of the communication environment, performance can degrade; conversely, too high a measurement frequency can reduce throughput due to control messages. To address this issue, [PERSON] and [PERSON] [33] proposed ReinRate, which can optimize throughput using reinforcement learning (RL). However, the information collected by the RL agent may not be suitable for time-triggered WLANs, or they focus solely on throughput, resulting in suboptimal performance in interference situations caused by CTI.