# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 1 Introduction  
The integrated space-ground communication systems hold a promising future in the evolving landscape of mobile communication networks, particularly with their potential to expand coverage significantly [1; 2; 3]. Such systems are pivotal in advancing the Internet of Things (IoT), enabling a vast network of interconnected devices across more extensive and varied terrains [4; 5; 6]. Moreover, the fusion of these integrated systems with existing cellular networks promises to diversify and enrich the service types available to individual users, marking a significant leap forward in communication technology [7; 8]. To realize the full potential of space-ground integrated communication systems, one crucial challenge is the efficient allocation of resources [9; 10; 11]. This involves the development of network slicing strategies and cross-network resource scheduling algorithms that take the unique characteristics of both satellite and cellular networks into account. Specifically, the location of MS will no longer be limited to the ground, but will expand and include sea and air [12; 13; 14; 15]. Thus, certain devices can access not only the cellular network but also the satellite or drone network at the same time. When necessary, the MS also needs to switch between the network. Therefore, for the new generation of network resource allocation algorithms, it is necessary to include location information in order to maximize network efficiency and ensure seamless service delivery across the different layers of the integrated system. Despite numerous advancements, current resource allocation methods, particularly those based on deep reinforcement learning techniques such as Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) exhibit limitations. These algorithms often fall short in real-time performance and fail to consider the integration of multiple physical channels within the space-ground system, thus leaving considerable room forimprovement in network efficiency. These gaps in existing methodologies underscore the necessity for our research. To address these shortcomings, we introduce a novel integrated network slicing approach coupled with a Distributed Deployment Deep Q-Network (DP-DQN) resource allocation method. Our approach decentralizes the decision-making process by distributing the decision models to the user end, thus facilitating on-the-spot network resource decisions. This not only ensures real-time responsiveness but also reduces network overhead while accommodating the demands of various physical channels [16; 17; 18; 19]. Through this innovation, our study seeks to significantly enhance the operational efficiency of space-ground integrated communication systems. The contributions of this paper are summarized as follows:  
* Firstly, we propose a new converged system based on the existing infrastructure of cellular and satellite communication networks, in which all resources are deployed in a unified manner rather than \"separate and conquer\". * Secondly, based on the idea of SDN, we propose a new network controller based on a distributed resource allocation decision-making model, which assigns the implementation body of decision-making behavior from the original core network controller to the access mobile terminal, so as to alleviate the dilemma of the rapid increase in access volume which will cause a huge burden on the core network in the future. * Thirdly, based on DQN and negative feedback network technology, we propose a new DP-DQN model based on reinforcement learning for the overall system resource allocation, so that the mobile station can consider the problem of resource allocation from the perspective of the core network overhead, so as to make access requests conducive to the efficiency of the core network. The remainder of this article is organized in the following order. The related works involved in this paper are reviewed in Section 2. In Section 3, the allocation system model is proposed and the optimization problem is formulated.