## 2 Related Works  
[PERSON] et al. first presented the potential use cases, open challenges, and possible solutions of HAP drones for next-generation networks [14]. The main communication links between HAP drones and other non-terrestrial network (NTN) platforms, along with their advantages and challenges, are presented in [15]. Due to the rapid movement of LEO satellites, the matching relationship between HAP drones and LEO satellites is not fixed, so efficient matching and association strategies need to be developed. In [16], the matching relationship between user equipment (UE), HAP drones, and terrestrial base stations (BS) is formulated as a mixed discrete-continuous optimization problem under the HAP drone payload connectivity constraints, HAP drones and BSs power constraints, and backhaul constraints to maximize the network throughput. The formulated problem is solved using a combination of integer linear programming and generalized assignment problems. A deep Q-learning (DQL) approach is proposed in [17] to perform the user association between a terrestrial base station and a HAP drone based on the channel state information of the previous time slot. In addition to the above-mentioned UE's selection between terrestrial networks and non-terrestrial networks, there has been relevant research on the three-party matching problem among users, HAP drones and satellites in remote areas without terrestrial network coverage. In [18], the matching problem among users, HAP drones, and satellites is formulated to maximize the total revenue and it was solved by a satellite-oriented restricted three-sided matching algorithm. In [19], a throughput maximization problem is formulated for ground users in an integrated satellite-aerial-ground network by comprehensively optimizing user association, transmission power, and unmanned aerial vehicle (UAV) trajectory. In [20], a UAV-LEO integrated traffic collection network is proposed to maximize the uploaded traffic volume while ensuring the energy consumption by comprehensively considering bandwidth allocation, UAV trajectory design, power allocation, and LEO satellite selection. The maximum computation delay among terminals is minimized in [21] by a joint considering matching relationship, resource allocation, and deployment location optimization. An alternating optimization algorithm based on block coordinate descent and successive convex approximation is proposed to solve this. A joint association and power allocation approach is proposed for the space-air-ground network in [22] to maximize the transmitted traffic amount while minimizing the transmit power under the constraints of the power budget and quality of service (QoS) requirements of HAP drones and the data storage and visibility time of LEO satellites. The association problem and power allocation problem are alternately addressed by the GUROBI optimizer and the whale optimization algorithm, respectively.  
It is worth mentioning that reinforcement learning (RL) algorithms are widely used for HAP drone problems. HAP drones form a distributed network, and with multi-agent RL, the space-air-ground integrated network can effectively become self-organizing. In [23], a multi-agent Q-learning approach is proposed to tackle the service function chain placement problem for LEO satellite networks in a discrete-time stochastic control framework, thus optimizing the long-term system performance. In [24], a multi-agent deep reinforcement learning algorithm with global rewards is proposed to optimize the transmit power, CPU frequency, bit allocation, offloading decision, and bandwidth allocation via a decentralized method, thus achieving the computation offloading and resource allocation for the LEO satellite edge computing network. In [25], the utility of HAP drones is maximized by jointly optimizing association and resource allocation, which is formulated as a Stackelberg game. The formulated problem is transformed into a stochastic game model, and a multi-agent deep RL algorithm is adopted to solve it.