## 3 Key Technology of the A2S Integrated MIoT
### Resource Allocation
#### 3.1.2 NOMA Technique  
The NOMA technique in MIoT technology is a key technique for increasing spectral efficiency and connectivity capacity. A schematic diagram of a NOMA link is shown in Figure 4. The NOMA technique achieves this by transferring data for multiple users simultaneously on the same time and frequency resource. Unlike traditional frequency division multiple access (FDMA) and time division multiple access (TDMA), the NOMA technique allows multiple users to share the same spectrum resources in parallel [104]. The NOMA technique enables the simultaneous transmission of data from multiple users at the same time and frequency resources by distributing data from different users to different power levels [105]. This power allocation can be dynamically adjusted based on the user's channel conditions and transmission needs to achieve a higher spectral efficiency and connection capacity. The NOMA technique can also efficiently distinguish and decode simultaneously transmitted user data through superior multiple access (SMA) decoding. At the receiving end, the data of the strong signal user is decoded first, and then the data of the weak signal user is decoded using channel estimation and interference cancellation (CEAIC). This interference cancellation technique can significantly improve the reception performance and system capacity. In the MIoT, the NOMA technique is also widely used. The NOMA technique can be used to improve the reliability of the system through the power allocation and channel scheduling. If one of the users has an interruption or other problem, the system can quickly reallocate the other users' signals and resources, thus ensuring the stability of the entire system [48]. As one of the major mobile operators in China, the China Mobile Communications Corporation has begun to adopt the NOMA technique in its network architecture. Figure 4: A diagram of the implementation of NOMA. The NOMA technique is being used in next-generation wireless communication networks to improve the spectrum utilization, enhance system capacity, and support diverse application scenarios. Tencent Cloud, a well-known cloud computing service provider in China, is also exploring the application of the NOMA technique in its IoT platform. They are using the NOMA technique for communication among IoT devices to enable more efficient DT and connectivity, and to promote the application of the IoT in the integration of land and ground; NTT DOCOMO, one of the largest mobile operators in Japan, is also using the NOMA technique in research and practice [106; 107; 108]. They are applying the NOMA technique to 5G networks to increase network capacity and coverage, and support A2S integrated applications including smart cities, connected vehicles, and industrial automation. Some European telecom companies are also actively exploring the application of the NOMA technique. For example, companies such as Vodafone in the UK and Deutsche Telekom in Germany are looking into applying the NOMA technique to their communications networks to provide more efficient wireless connections and services [109]. In general, the NOMA technique can provide a higher spectral efficiency and connectivity capacity in the MIoT, thus supporting more DT and communication needs of maritime devices and sensors. The NOMA technique is a technique with great potential to improve the performance and reliability of MIoT systems. Additionally, machine learning plays a crucial role in enhancing the resource allocation performance of the MIoT. By leveraging big data and advanced algorithms, machine learning technologies can analyze the complex scenarios of maritime traffic, enabling more efficient resource utilization [110]. In particular, machine learning algorithms can be integrated in MIoT deployments, for example, to improve the communication performance or support a specific application [111]. First, machine learning, through in-depth analysis of historical data, can identify potential patterns and trends in maritime traffic systems.