## 2 Related Works  
The research on the AIoT network throughput mainly focuses on two aspects: technical characteristics and scene characteristics. For the technical characteristics, the throughput performance under the current conditions is solved by setting specific constraints. For example, [PERSON] et al. [6] studied the use of finite-length coding in QoS-constrained AIoT systems to support low-latency communication. For the scene characteristics, the network throughput is mainly improved by utilizing the advantageous resources in the scene. For example, [PERSON] et al. [7] studied the throughput problem of the AIoT in an ultra-dense network. Because the network capacity will cause huge energy consumption when deploying a large number of small base stations (BSs) in an ultra-dense network, we therefore use energy harvesting and energy cooperation technology to maximize the throughput of the system. [PERSON] et al. [8] studied the throughput performance of the license-free multiple access mode in the AIoT. To address the sequence information conflict problem in the unauthorized multiple access mode, we propose a sequence selection scheme based on deep reinforcement learning to reduce the collision risk. [PERSON] et al. [9] studied the throughput performance of Device-to-Device (D2D) communication in cellular-based AIoT networks. In addition, AIoT devices often send latency-sensitive information to their nearest BS. Based on the above assumptions, the location of the AIoT device is modeled as a bipolar Poisson point process, and the BS location is modeled as another independent Poisson point process. When an AIoT device sends a status update to the BS, it is considered to control the transmission power based on the distance. Therefore, based on the available maximum transmission power, the AIoT devices covered by the BS can be determined and associated. Then, by deriving the average success probability of the D2D link, the average throughput of the AIoT network can be obtained. [PERSON] et al. [10] studied the throughput performance of the network after introducing blockchain and edge computing in AIoT applications. In large-scale AIoT scenarios, blockchain technology was used for encryption to ensure the security of transmission. However, due to the limited throughput caused by blockchain technology, AIoT applications with frequent interactions cannot be supported. To meet AIoT application requirements and enhance the network throughput, we propose a comprehensive framework that integrates blockchain and edge computing based on spatially structured ledgers. The framework takes into account the collaboration between AIoT devices and edge servers and optimizes bandwidth and computing allocation strategies to improve the network throughput.  
The F-RAN is divided into two types in terms of optimizing the network throughput performance in AIoT scenarios. The first type combines traditional F-RAN architectures with the characteristics of AIoT applications to improve the system throughput performance [11]. The second type combines fog computing with existing open-source frameworks on the infrastructure and leverages the programmable features of the framework to enable the software-based processing of multiple functions. Different types of AIoT services make the new fog architecture better suited for various types of AIoT services.