### Motivation and Contribution  
ML techniques have shown innovative results in resource management in SatComs compared to conventional optimization techniques. However, previous works do not provide a comprehensive overview of ML applications for RRM in SatComs. In other words, previous works have two distinct weaknesses: (i) they focus on the management of \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Proposal**} & \multicolumn{3}{c}{**Flexible Resources**} & \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**Optimization**} & \multirow{2}{*}{**ML Technique**} & \multirow{2}{*}{**Evaluated in Our Paper**} \\ \cline{2-2} \cline{6-7}  & **Power Bandwidth** & & & & & & \\ \hline
[29] & & o & Full & ML-based & RL-DQL (SA) & \\ \hline
[30] & & & o & Full & ML-assisted & SL-DNN & o \\ \hline
[12] & o & & & 4-colors & ML-based & RL-DQL (MA) & \\ \hline
[26] & o & & & 4-colors & ML-based & RL-PPO (SA) & \\ \hline
[27] & & o & & 4-colors & ML-based & RL-DQL (SA) & \\ \hline
[31; 32] & o & o & & 4-colors & ML-based & SL-DNN & o \\ \hline
[33] & o & o & o & 4-colors & ML-based & SL-CNN & o \\ \hline QL-[34] & o & o & o & 4-colors & ML-based & RL-QL (MA) & o \\ \hline DQL-[34] & o & o & o & 4-colors & ML-based & RL-DQL (MA) & o \\ \hline DDQL-[34] & o & o & o & 4-colors & ML-based & RL-DQL (MA) & o \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ML model proposals features in SoA for multibeam satellite radio resources management. a specific resource/problem and scenario, and (ii) they make use of independent numerical simulations. In that regard, many open questions remain, such as how/where the ML block will be implemented in the SatComs system, the hardware requirements or what the most suitable technique is for its implementation. Based on these questions and the results presented in previous publications [30; 31; 32; 33; 34], from a global view, we focus on the analysis of the implementation of ML techniques for RRM in multibeam satellites in GEO orbit, i.e., we study how ML techniques should be implemented depending on system features. In particular, we consider a flexible SatCom system capable of managing more than one satellite resource, highlighting two different systems studied. In addition, we analyze two approaches to implement ML in RRM: supervised learning (SL), which is generally trained offline, and reinforcement learning (RL), which is generally trained online, which leads to whether the AI Chipset should be onboard the satellite or on the ground. The discussion presented in this paper is expected to serve as a milestone for upcoming projects to test the feasibility of implementing the AI Chipset in future SatCom systems such as the SPAICE project [35] and the forthcoming launch of TechEdSat-13 [36]. In that sense, the main contributions of this paper are listed below:  
* We define two different system architectures based on ML techniques for RRM depending on whether the AI Chipset is at the ground station or onboard the satellite depending on the training-learning characteristics. * We study and compare the proposed ML techniques to evaluate their performance and feasibility for both systems. * We evaluate the performance and processing time required depending on whether training is online or offline. * We identify the main trade-offs for selecting the commercial AI Chipset that could be used for ML implementation in SatCom systems with a flexible payload. * We identify different critical challenges for implementing ML techniques for the new lines of research needed. The remainder of the paper includes the dynamic radio resource management problem and system model in multibeam satellite systems, which is presented in Section 2. Section 3 offers the ML approach for SatCom RRM, discussing the possible architectures and techniques and restructuring the RRM problem to a supervised learning (SL) and reinforcement learning (RL) approach.