# Onboard Processing in Satellite Communications Using AI Accelerators
## 3 Onboard AI Applications  
### Use Cases  
#### 3.1.1 Interference Detection  
In a satellite communications system, inter-system interference due to jamming, misaligned dishes, and unintentional interference from other systems has become a significant concern. Interference substantially deteriorates the signal quality, and thus degrades overall system performance. Therefore, detecting interference is the first step in the interference management chain (detection, classification, localization, and mitigation). The proliferation of the non-geostationary satellite orbit (NGSO) constellation has exacerbated the space interference scenario. With multiple satellites flying in different orbits and inclinations, in-line interference is increasingly likely to occur (see Figure 2) [18]. Figure 2: Example of an onboard interference scenario between NGSO and GSO. Interference is often monitored on the ground and minimized in satellite networks to maintain high QoS. In terms of better spectral management and customer incident prevention, the possibility of offloading a purely human task, such as power spectrum density verification, to an automated system capable of detecting the presence of unwanted signals is an exciting prospect. The introduction of AI would significantly reduce incident/degradation duration and increase cost savings from an OPEX perspective, and ultimately increase availability and QoS. Significant interference is considered a critical problem for SatCom systems and services. The SatCom industry is increasingly concerned about managing and mitigating interference effectively. Although efficient techniques exist to control substantial interference in SatCom, weak interference is not so easy to detect due to its low signal-to-interference-plus-noise ratio (SINR). To solve this problem, the work proposed in [19] offers and develops a technique that is performed on board the satellite by decoding the desired signal, removing it from the total received signal, and applying an energy detector (ED) on the remaining signal for interference detection. Unlike previous work, the authors in [19] consider imperfect signal cancellation, examining how decoding errors affect detection performance. The work presented in [20] proposes a two-step algorithm for onboard interference detection, exploiting the frame structure of the digital video broadcasting-second generation satellite extensions (DVB-S2X) standard, which employs pilot symbols for data transmission. Assuming that the pilot signal is known at the receiver, it can be removed from the total received signal. Then, an ED technique can be applied to the remaining signal to decide the presence or absence of interference. On the other hand, a receiver in which compressive signal processing is used to estimate the power spectrum in the compressed domain using a computationally lightweight algorithm is proposed in [21]. It has a significantly low detection time to detect the presence of a narrowband, high-power interference signal. It also proposes interference filtering using a novel and computationally efficient filtering method in the compressed domain. This receiver architecture results in a computationally efficient receiver with reduced detection time and is suitable for real-time applications. This will help protect a satellite transponder from saturation due to unwanted high-power narrowband interference. In this context, AI-based techniques can bring gains in identification accuracy and latency. Several AI-based decision processes can be used when mapping baseline features to a class label. Probabilistic-derived decision trees on expert modulation features were among the first to be used in this field, but for many years such decision processes have also been trained directly on datasets represented in their feature space (e.g., neural networks and support vector machines (SVM)). The accuracy of the interference detector is a critical parameter because false negatives can have a high cost in lowering the SINR, which would be reflected in the QoS.