## 5 Challenges of NTN-Based IIoT and the Emergence of AI
### Potential Advantages of Learning Techniques in NTN-Based IIoT  
The application of AI techniques to NTNs would enable the further exploration of fundamental and heretofore unexplored features of NTNs and the development of innovative communications and networking technologies including new architectures, sophisticated algorithms, and protocol designs. More specifically, the design of PHY can be optimized to increase flexibility in spectrum access, whereas the radio channel can be accurately estimated and modeled using a limited number of measured data and avoiding extensive measurements. In addition, the implementation of AI in NTNs would allow these networks to have seamless autonomous communication on each type of radio channel in the presence of interference, fading, and attenuation and enable the coexistence of NTNs with terrestrial networks without requiring prior mathematical analysis and modeling. Additionally,  
Figure 5: Summary of potential and powerful AI technologies for the NTN-based IIoT that intend to enable end-to-end (E2E) optimization of network operations in complex environments without human intervention via training, learning, and prediction processes.  
while optimization of performance in conventional communications systems is independently handled in each distinct stage (e.g., encoding, modulation, and detection), further improvement is expected to be achieved if E2E performance optimization is realized though AI. In the context of network operation, AI can be used for efficiently handling the optimization of resource allocation and congestion avoidance. Additionally, the management of large satellite constellations and UAV swarms can be facilitated without the need for centralized coordination. In the different cells of UDHNs, where handovers are frequent, AI methods could be applied to learn and adapt terminals to spatio-temporal changes so as to maintain low power consumption while attaining an exceptional QoS level. Moreover, intelligent spaceborne and airborne platforms would be capable of sensing their surroundings and learning the network variations, mapping out areas, tracking IIoT objects, and responding directly and successfully to real-time changes in the radio channel. Recent studies have argued that learning a communication system from E2E can be realized by considering this system as an autoencoder [102]. The autoencoder describes a DNN which is trained to recreate the input as the output, while the radio channel is structured as a set of levels with stochastic and deterministic behavior. As the information has to pass through multiple levels, the network must adopt a robust representation of the incoming messages at each level. Figure 6 illustrates the structuring elements of a prospective NTN in which learning techniques are applied. In this intelligent NTN, a data transmission and reception unit facilitates the exchange of IIoT data streams among devices, a data conversion unit with analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) enables signal conversion, a control unit controls information and inspects data exchange among network entities, and an AI-based advanced learning mechanism allows for effective action prediction.