## 2 Related Work  
Large-scale resource allocation for massive IoT devices is one of the challenging issues that has attracted much attention in S-IoT [7]. Furthermore, [PERSON] et al. provided an overview discussion on QoS management and resource allocation [8]. Several works have focused on energy-saving issues in the research field of S-IoT resource allocation. Considering the limited onboard energy of LEO in the S-IoT scenario, [PERSON] et al. addressed the energy-saving channel allocation problem with battery load constraints by adopting DRL and taking the normalized power efficiency and service blocking rate criteria as reward function components [9]. [PERSON] et al. poured their main attention into the energy-limited remote IoT terminals in rural areas rather than on the energy-saving issue of satellites [10]. This paper adopted unmanned aerial vehicles (UAVs) as relays and then proposed an energy-efficient model by jointly optimizing channel selection, uplink transmission power control, and UAV relay deployment. QoS is as critically vital as the energy-saving issue in S-IoT, which has been discussed from different views and approaches in recent decades. Transmission rate and delay are frequently considered QoS requirements for bandwidth and delay-sensitive services, such as environmental management. [PERSON] et al. proposed a delay constraint joint power and bandwidth allocation algorithm by analyzing the interactions among inter-beam interference, delay factor, channel conditions, and traffic demand [11]. Furthermore, [PERSON] et al. focused on a NOMA-based satellite industrial IoT system and proposed a power proportion optimization method for beams and nodes to guarantee the QoS, namely transmission delay and transmission rate [12]. Different from the aforementioned deterministic QoS constraints, effective capacity, which holds statistical QoS guarantee, is a more feasible alternative of QoS requirement with the consideration of the variable quality of the satellite channels. Considering the delay requirements of S-IoT devices, [PERSON] et al. employed effective capacity to express delayed QoS requirements and developed a dynamic power allocation strategy for NOMA by the DRL algorithm [13]. Power allocation factors for NOMA users are selected dynamically by DRL in each time stick to maximize sum effective capacity while meeting each user's minimum capacity demand constraint. Although this method solved the problems of delay and transmission rate constraints, it ignored network reliability indicators, which are of decisive importance for control applications such as position reporting in vessel navigation. Another limitation of existing studies is that they are designed to handle specific QoS requirements for applications of the same type. However, there are usually IoT devices of heterogeneous applications, and thus with diverse QoS demand, to access the same satellite in the S-IoT scenario. As an essential factor influencing communication performance, CSI has constantly captured researchers' attention in the resource allocation field of S-IoT. However, in most existing research, CSI is usually treated as a random variable consisting of large-scale and small-scale fading. Therefore, it is difficult to adjust the resource allocation strategy according to the specific channel quality of a certain moment. There are three alternative ideas to solve this problem. One is allocating resource flexibly according to a specific channel model, which is adopted by [PERSON] et al. [11] Nevertheless, they had to repeatedly build the model and optimize the problem in case the channel quality model changes [14; 15], which may be caused by weather variations or external interference. The second solution, adopted by [PERSON] et al., is leveraging a deep neural network (DNN) to approximate the SIC decoding order in NOMA-based S-IoT since the queue state and channel state continually changes [16].