# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 3 System Model
### Network Scenario  
In the architecture designed by us, there are three types of physical information carriers, namely satellite networks, cellular networks and drone networks, which are marked as \(M_{1}\), \(M_{2}\) and \(M_{3}\). At the same time, each physical channel contains three network slices, \(T_{1}\), \(T_{2}\) and \(T_{3}\). By the way, we define a virtual concept as a 'location area'. As shown in Figure 1, each 'area' contains several MSs with the similar properties. Also, each of the networks \(M_{i}\) will serve a number of MSs. For example, the distances between users from \(K_{21}\) to \(K_{2N}\) and certain base stations are relative high, where we call them sub-urban or rural areas. In these circumstances, we introduce a satellite network that acts as the carrier of eMBB service instead of cellular network. In Figure 1, we can see a special access device, the \(K_{2N}\). For the same kind of service slice, its state is bounded between suburban and urban areas, so both networks can be used as its carrier. In order to select a more suitable network for access, we conduct our works based on core network overhead, which means the network access decision will be made by the MS from the perspective of network overhead. The whole decision process of network access at the MS side will follow the procedure below. * First of all, the MS will analyze the data size and service type of the data to be sent, and determine its transmission power. * Secondly, this information is fed into the DP-DQN model (discussed in detail later in this article) delivered from the network side and determines the type of network that is ultimately accessed. Different from the traditional network, the training and deployment process of DP-DQN model is based on SDN (Software Defined Network). Here, we introduce the DP-DQN deployment method. Firstly, as shown in Figures 1 and 2, the basic model will be trained at the side of the core network controller. Specifically, the input parameters are service types, average data volume and current network congestion. After this process, the pre-trained model is sent to each network carrier without distinction. The red dashed line in Figure 1 represents the model transfer flow for this process. Secondly, each network will then train the model twice, adding its own parameters based on the model of core network controller, such as the signal transmission power on the network side, the average delay of the network and the user amount in the coverage. Finally, these secondary training models will be passed to the MS at the edge, where these models will undergo the final training. Here, the third (final) training process at the MS side will also conduct integrated learning with all the models received. Here, the training of the model will take the following parameters into account: the user's own average amount of data, location information (i.e., latitude and longitude altitude), the state of the three networks at this time and the records of past resource decisions. Thus, what the core network controller should do is to keep monitoring the crowd state while renewing the basic decision model. The parameters included in the three training sessions are summarized in Table 1.  
Figure 1: The deployment of decision model: the decision model will be pre-trained on the core network controller and sent to the MS. The overall frequency resource mapping relationship is shown in Figure 3. On the left is the frame structure of the three physical networks, in the middle is the time-frequency domain resource allocation diagram and on the right is the data packet to be sent by the user. The data to be sent by the user are first analyzed to determine the type of service they belong to. The data are then mapped to the appropriate resource block. Finally, the access network is determined according to the user's location and other related information.