## 5 IoRT Systems Based on Space-Borne NTN Networks
### Enhancement of Energy Efficiency
However, existing strategies often fail to adequately address the unique characteristics of low-earth-orbit satellite systems, such as mobility and energy constraints. In [78], the authors propose DeepCA, a Deep Reinforcement Learning-based (DRL) method for energy-efficient channel allocation. DeepCA effectively tackles the challenge of limited channel resources in ground-based nodes, leading to a significant reduction in energy consumption compared to conventional approaches. The growing demand for IoT connectivity necessitates enhanced spectrum and energy efficiency in satellite-based IoT systems. The work in [79] proposes a novel power control algorithm for IoT terminals in satellite-based networks, leveraging terrestrial base stations for data acquisition and resource management. By employing the Poisson Point Process (PPP) theory, the optimization problem is formulated considering the distribution of IoT devices within the network. The optimal power control scheme is determined by optimizing user distribution and Signal-to-Interference-plus-Noise Ratio (SINR) demand. To address the complexity of the PPP-based objective function, the pattern search procedure is employed. Numerical evaluations of user rates and energy efficiency demonstrate the effectiveness of the proposed power control algorithm. Furthermore, the study in [80] introduces multi-connectivity to multi-orbit NTNs, allowing user terminals to connect to numerous satellites in order to increase peak throughput. The researchers propose a terminal-aware multi-connectivity scheduling algorithm that optimizes uplink data rates and minimizes energy consumption at the user terminal based on radio availability and propagation data. Operating within a differentiated multi-layer NTN resource scheduling architecture, the efficacy of the algorithm is compared to that of other scheduling methods, evaluating the uplink data rate and energy efficiency. In addition, they present an architectural design for implementable schedulers in multi-orbital satellite networks that can accommodate a variety of terminal classes. In [81], the authors explore the challenges of enabling massive access in a Beyond-Fifth-Generation (B5G) satellite IoT network using a multibeam architecture. They focus on the issue of channel phase uncertainty caused by the transmission of channel-state information from devices to satellites through gateways. To overcome this challenge, they propose a novel NOMA scheme that effectively supports the wide-scale deployment of IoT devices. Considering the energy limitations of LEO satellites, they introduce two robust beamforming algorithms to minimize total power consumption for non-critical and critical IoT applications while taking channel phase uncertainty into account. Theoretical analysis and simulations demonstrate the effectiveness and robustness of these algorithms in enabling massive access in the satellite IoT. The authors of [82] propose a DRL algorithm to manage channel allocation and power control in the satellite IoT uplink scenario. The algorithm aims to optimize resource utilization and service quality. Simulations demonstrate that the approach successfully achieves an efficient balance between power efficiency and service quality. Under typical scenarios for satellite IoT, the proposed scheme greatly improves power efficiency in comparison to current methods, with minimal training overhead. In conclusion, to enhance the energy efficiency of IoT systems based on space-borne NTN networks, various techniques have been proposed, ranging from utilizing the repetition factor of data symbols to DRL-based methods for dynamic and energy-efficient channel allocation. Table 3 presents a concise summary of the information presented in this section.