# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 1 Introduction  
The global availability and the license-free policy of the unlicensed spectrum have drawn various Internet of Things (IoT) devices into the spectrum [1, 2]. Consequently, IoT devices employing heterogeneous wireless technologies, even non-communication devices like microwave ovens, now coexist and mutually interfere within the same spectrum [3]. This cross-technology interference (CTI) is expected to intensify, highlighting the need for careful countermeasures in IEEE 802.11-based wireless local area networks (WLANs) [4]. Particularly in the 2.4 GHz industrial, scientific, and medical (ISM) band, WLAN devices are susceptible to interference from devices using ZigBee, Bluetooth, and other wireless technologies that share the same frequency spectrum [5]. Such interference degrades communication quality by causing bit errors and packet losses, leading to reduced throughput [6]. Moreover, with the recent trend of adopting time-triggered transmission methods in IEEE 802.11-based WLANs for real-time services in applications like industrialIoT (IIoT) and extended reality (XR), this interference can also impact latency, necessitating appropriate countermeasures [7; 8; 9]. Existing interference mitigation strategies that can be used to alleviate interference caused by CTI include the following: (1) reducing interference through direct coordination between communication devices [10; 11], (2) performing explicit interference mitigation by measuring the channel state from received signals [12], and (3) conducting implicit interference mitigation based on the success or failure of packet transmissions [13; 14]. However, these strategies have several limitations. First, direct coordination increases system complexity and complicates overall network management, especially as the number of devices using different wireless technologies grows [15]. Additionally, this method is ineffective against interference from non-communication devices like microwave ovens [16]. Second, measurement-based explicit interference mitigation offers good performance but often requires increased measurement frequency to select appropriate rates when CTI occurrence is unpredictable. This leads to more control messages, causing a trade-off that can reduce overall network throughput [17]. Third, packet error-based, also referred to statistics-based, implicit interference mitigation is simple to implement but may suffer from delayed responses or incorrect rate selections in complex scenarios where multiple devices cause interference simultaneously with different patterns, degrading mitigation performance [18]. Interference from devices using different wireless technologies manifests in various patterns depending on their spectrum usage characteristics in the frequency, time, and power domains [19]. While these diverse characteristics complicate interference dynamics, they also provide an opportunity to identify the presence of specific wireless technologies in the spectrum [20]. This identification enables rate adaptation that responds to CTI without direct communication with the interfering devices. By analyzing the signal patterns emitted by devices, we can perform CTI-aware interference mitigation, allowing on-demand responses and improving performance by tailoring adaptations to each specific CTI. In this paper, we propose a CTI-aware rate adaptation (CARA) framework that supports interference mitigation in time-triggered WLANs. The main contributions of this study are as follows:  
1. We propose a framework that effectively identifies CTI by leveraging the distinct spectrum usage characteristics of various wireless technologies and performs interference-aware rate adaptation accordingly. This framework addresses two key challenges in time-triggered WLANs for mitigating interference from cross-technology wireless devices without requiring protocol designs specific to each device. These challenges involve identifying signals in each slot and using this information to select an appropriate transmission rate for the next time slot. 2.