# Onboard Processing in Satellite Communications Using AI Accelerators
## 3 Onboard AI Applications
### Use Cases
#### 3.1.4 Flexible Payload Reconfiguration  
Flexible payloads are becoming mainstream, and are revolutionizing the conventional idea of \"mission by satellite\". Focusing on frequency-flexible satellites, the goal is to dynamically adapt frequency resources to avoid congestion and ensure a high QoS. More advanced satellite systems can allocate bandwidth dynamically to each beam (assuming a multibeam satellite system). Multiple carriers can be made available within each beam, and the per-user carrier assignment can also be optimized. However, the degree of flexibility available in most satellite systems is power allocation. Power control is easy to implement, and can resolve small congestion events such as the one depicted, where a user terminal is suffering from low SNR (mainly because it is located at the edge of the beam). At the same time, a privileged user in the center of the beam is receiving more SNR than it actually needs. Proper power balancing could resolve the unfortunate situation of the congested user without affecting the QoE of the user in the center of the beam.  
The next generation of satellite communications (GSO or NGSO) is being built with the capability to quickly and flexibly assign radio resources according to the system load and  
Figure 5: LSTM network for predicted SNR.  
the changing environment. Many significant resource allocation problems are non-convex or combinatorial because of the discrete nature of the variables involved. Hence, computing the optimal solution is challenging, leading to unaffordable computational times.  
However, some approaches are based on simplifying the case study to reduce the search variables. For example, the authors in [28] propose an assignment-game-based dynamic power allocation (AG-DPA) to achieve suboptimal low complexity in multibeam satellite systems. The authors compare the results obtained with a proportional power allocation (PPA) algorithm, obtaining a remarkable advantage in energy savings; however, the resource management is still insufficient for the required demand.  
As an alternative, [PERSON] et al., have proposed a suboptimal [29] method, which addresses parts of the problem separately and then iteratively adjusts the parameters. The problem splitting is conducted in such a way that power allocation and carrier allocation are separated.  
On top of the new perspective of reconfigurable payload, AI shall be integrated into the end-to-end service delivered to introduce a smart interference management system. A fully reconfigurable payload using a conventional interference management approach could not bring a substantial differentiator to the industry or customers. Instead, the winning combination of flexible payload with a smart interference avoidance system is a necessity that can deliver the benefits of the previously mentioned use cases and increase the customer QoS. This use case proposes a machine learning system capable of providing a satellite payload configuration either at the RF interface to meet the requested user bit rates under certain external interference power levels over the coverage area, or at the physical layer (PHY) to provide flexibility for future satellite-terrestrial network integration.  
In that sense, it is proposed to use a convolutional neural network (CNN) for flexible payload reconfiguration at the RF interface, as shown in Figure 6. The CNN input is represented in the form of tensor matrices, which will be passed to the convolutional layers where feature extraction is performed and then propagated to the full connection layers, where a classification will be generated, and each class will represent a payload resource configuration. The depth of the input layer will be given by the number of features evaluated as the traffic demand, \(r_{i,j}\), the interference power level, \(I_{i,j}\), and/or the rain attenuation, \(A_{i,j}\), where \(i\) and \(j\) represent the geographic coordinates within the service area.