### Related Works  
Regarding resource management at SatComs, the authors in [24] proposed a combined learning and optimization approach to address a mixed-integer convex programming problem (MICP) in satellite RRM. The complex MICP problem is decomposed into two classification-like tasks and a remaining power control problem. A dual-DNN approach addresses the former, and the latter is solved by convex optimization. [PERSON] et al. [25] proposed an innovative resource management framework for next-generation heterogeneous satellite networks (HSNs), which can encourage cooperation between independent satellite systems and maximize resource utilization. The critical points of the proposed design lie in the architecture that supports intercommunication between different satellite systems and the management provided by the matching between resources and services. The authors apply deep reinforcement learning (DRL) in the system due to its strong capability for optimal pairing. The two problems of multi-target reinforcement learning and multi-agent reinforcement learning are studied to adapt HSN development. The combination of DRL and resource allocation achieves integrated resource management across different satellite systems and performs resource allocation in HSN. Continuing with the most recent research suggesting DRL algorithms to solve the RRM problem, [PERSON] et al. [11] stated that a feasible solution could be designed for real-time, single-channel resource allocation problems. However, in their study, DRL architectures are based on the discretization of resources before allocation, whereas satellite resources, such as power, are inherently continuous. Therefore, [PERSON] et al. [26] explored a DRL architecture for energy allocation that uses continuous, stateful action spaces, avoiding the need for discretization. Nonetheless, the policy is not optimal, as some of the demand is still lost. On the other hand, [PERSON] et al. [27] suggest a novel deep reinforcement learning-based dynamic channel allocation algorithm (DRL-DCA) in multibeam satellite systems. The results showed that this algorithm could achieve a lower blocking probability than traditional algorithms; however, the joint channel and power allocation algorithm is not considered. [PERSON] et al. [28] constructed a game model to learn the optimal strategy in the satellite communication scenario. In particular, the authors suggest a DRL-based bandwidth allocation framework, which can dynamically allocate the bandwidth in each beam. The effectiveness of the proposed method in time-varying traffic and large-scale communication is verified in the bandwidth management problem with acceptable computational cost. However, only one resource can be managed on the satellite with this method, which is a critical limitation when full flexibility is sought in the multibeam satellite system. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Project** & \multicolumn{2}{l}{**Identified but Not Investigated Case Studies**} & \multicolumn{2}{l}{**Investigated Case Studies**} \\ \hline  & - & Anomaly detection & - & Interference detection \\  & - & Interference classification & - & System resources management \\  & - & User allocation and network optimization & (flexible payload configuration) \\  & - & User classification & - & Congestion prediction \\  & - & Channel prediction for railway communications & - & Precoding matrix calculation \\ \hline  & - & System resources management & - & Encoding matrix calculation \\  & - & Carrier interferer detection & - & Link adaptation \\ MLSAT & - & Carrier interferer reduction & - & Ka-band frequency plan optimization \\  & - & Constellation mapping & - & Axiote antenna array \\  & - & Multicarrier nonlinear distortion & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of use cases considered in the SATAI and MLSAT project. In this sense, Table 2 presents the main features of 10 different ML models for RRM in multibeam satellites.