# Onboard Processing in Satellite Communications Using AI Accelerators
[12] suggest an innovative RRM framework for next-generation heterogeneous satellite networks (HSNs), which can cooperate 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 in the management that provides pairing between resources and services. The authors apply deep reinforcement learning (DRL) in the system due to its strong capability for optimal pairing. Following more recent research suggesting DRL algorithms to solve the RRM problem, [PERSON] et al. [13] claim that a feasible solution can 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, while satellite resources, such as power, are inherently continuous. Therefore, [PERSON] et al. [14] explore a DRL architecture for power allocation that uses continuous, stateful action spaces, avoiding the need for discretization. However, the policy is not optimal since part of the demand is still lost. On the other hand, [PERSON] et al. [15] suggest a new dynamic channel allocation algorithm (DRL-DCA) in multibeam satellite systems. The results show that this algorithm could achieve lower blocking probability than traditional algorithms. However, the joint channel and power allocation algorithm is not considered. [PERSON] et al. [16] construct a game model to learn the optimal strategy in the SatComs scenario. In particular, the authors suggest a bandwidth allocation framework based on the bandwidth DRL, 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 per satellite can be managed with this method, a critical limitation when full flexibility is sought in the multibeam satellite system. In [17], a DRL architecture based on a cooperative multiagent system was presented, demonstrating better performance than a single agent for RRM in multibeam satellites with more than one flexibility resource since the number of possible states increases exponentially for a single agent. In addition, Q-learning (QL), deep Q-learning (DQL), and double deep Q-learning (DDQL) algorithms are analyzed by comparing their performance for RRM based on their throughput, complexity, and added latency. Based on the above, we can note that the study of AI applications in SatCom has advanced considerably in recent years. However, it is still in its infancy; for example, most of these studies are based on theoretical simulations performed on traditional processors without considering the hardware technical limitations in space. AI algorithms can be very computationally intensive, consume significant power, and are slow when running on standard processors. These problems severely limit their use in satellite payloads and, thus, applications. The industry has developed AI-specific processors capable of executing complex algorithms in real time and consuming a fraction of the power. These processors are now available as commercial AI chipsets. The availability of these chipsets has enabled the practical use of AI for satellite applications in terrestrial and onboard scenarios. However, next-generation defense systems and commercial broadband satellites require more autonomy, onboard data processing, and decision making. In this regard, interest in using commercial AI chipsets has increased significantly to evaluate, develop, and validate AI-based signal processing techniques onboard the satellite, such as signal identification, spectrum monitoring, spectrum sharing, or signal demodulation.