## 3 ML for SatCom RRM: Architecture and Techniques
### Machine Learning Implementation in SatComs Systems
#### 3.1.2 Ai Chipset On-Ground or On-Board  
Selecting the most suitable system architecture to meet the minimum service requirements is one of the main challenges of implementing ML techniques in SatComs systems. In this sense, in Figures 2 and 3, we propose two system architectures that use ML techniques to automate the RRM process. The main difference resides in whether the AI Chipset is onboard or on-ground.  
The first proposed architecture (Figure 2) represents process automation implementation using an ML algorithm trained online. For this scenario, the AI Chipset is part of the ground segment because the trained model must be updated every time. Information on the system traffic demand, on the geographical distribution of users and on channel information is obtained through the return link, which will reach the servers connected to the system gateway network. The data are processed to train the ML model with which the RRM policy is updated, and the AI Chipset is used for inference. The information on how the radio resources should be distributed on the satellite is generated. By using TT&C (Telemetry, Tracking and Command), this information is transmitted to the satellite, allowing the satellite to manage the radio resources for the user link.  
Figure 1: Difference between training and inference of the ML model for RRM.  
The main advantage of this architecture (Figure 2) is that the system will have greater flexibility to adapt to unforeseen changes, e.g., a drastic and unexpected change in traffic demand. Thus, obtaining an outstanding performance in the automation of the RRM  
Figure 3: AI Chipset implementation in the space segment for RRM in a multibeam satellite system. ML algorithm training is performed offline in ground segment with a training database, thus saving the model obtained and the AI chipset can go onboard the satellite using the model for inference.  
Figure 2: AI Chipset implementation in the ground segment for RRM in a multibeam satellite system. ML algorithm training is performed online in the ground segment using the information received on the return link updating the model for the RRM and sending the necessary configurations on the satellite via TT&C.  
process since the updated RRM model is obtained with the instantaneous values of the actual system. However, there are two main disadvantages: (i) the processing time due to which could add a delay in updating the satellite radio resources and (ii) the signaling and reallocation of resources may introduce performance degradation to the system.  
On the other hand, the second proposed architecture (Figure 3) is based on training the offline ML model with a training database that describes the system behavior. A model that manages satellite resources according to user link conditions could be obtained. Once the model has been trained, the AI Chipset could be located onboard the satellite [35] for inference. The main advantage of this architecture (Figure 3) is that processing times are reduced, firstly because the model has been previously trained. However, a return channel demodulator would be required to analyze the traffic demand in real-time or an additional ML block for onboard traffic prediction is also feasible; thus, this architecture has a strong dependency on the training data and the models used. This will imply additional complexity and power needs onboard. In addition, the dependence of the traffic model could affect the performance of the RRM in case of drastic changes in the actual system and the training has to be redone; furthermore, the cost and mass of the payload would be increased.  
A third scenario would be a hybrid architecture where model training is initially performed online. However, it is possible to update the trained model by collecting new data obtained during the forward link.