## 1 Introduction
### Related Work  
In order to obtain better performance, many types of research has been conducted on the resources management of the HAPS network, such as bearing layer selection, transmission resource allocation, and power control [4]. A centralized resource allocated strategy called ORA-LF for HAPS is proposed in [5], aiming to maximize system throughput. However, the strategy is proposed for the WCDMA system in [5], which requires further research on the OFDM system. In the meantime, the fairness of the same service among users has also been reasonably considered. A centralized call admission control (CAC) algorithm under power limitation was proposed by [6; 7; 8]. Centralized scheduling could optimize system performance through better resource allocation. However, these three papers only optimized the HAPS systems internally, and the problem modelling under system coexistence scenarios needs further study. A joint learning (FL) algorithm, which is based on a support vector machine (SVM) scheme, is proposed in [9]. The FL algorithm can actively figure out user association, service sequence, and a task allocation plan. Thus, mobile edge computing (MEC) is reasonably deployed on each HAPS node to reduce the delay caused by task transmission. The aim of [9] is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme. Further study of system throughput performance is required, as highlighted by [9]. The research on wireless resource allocation of OFDMA-based HAPS network is conducted in [10], which formulates and solves specific optimization problems to identify the best resource allocation plan for transmission power, sub-channels allocation, and time slots allocation. Consequently, the resource allocation scheme for a single-layer network is difficult to be applied in integrated networks. Therefore, the optimization of resource allocation for the integrated network is an important research field.  
Research on resource allocation mechanisms of the multi-layer network, including HAPS layer, satellite layer and terrestrial layer, are mainly carried out in [11; 12; 13; 14; 15; 16]. The research in [11] is about the method of improving QoS of different services under the same frequency band and coverage of multiple HAPS networks. However, the impact of terrestrial networks is not considered in [11]. A new framework for airborne heterogeneous wireless networks has been developed in [12], which can effectively support real-time statistics in a heterogeneous network. The optimal system transmission power plan can be established with the guarantee of system QoS requirements. However, the mutual interference suppression method in the TDD mode needs further study. QoS statistics frameworks for 5G mobile heterogeneous wireless networks and a heterogeneous network QoS statistics framework for mobile wireless networks with the two-way full-duplex transmission have been proposed also [13; 14]. Therefore, statistically delayed QoS statistics can provide an effective alternative to guarantee delay-limited QoS services for real-time traffic of various types of mobile wireless networks, including AMWNs. The research in [15; 16] is mainly aimed at optimization of resource allocation in downlink transmission scenarios.  
In order to obtain more valuable references in resource management algorithms for future actual integrated networks, this paper models a two-layer HAPS-IMT integrated system. Both the IMT layer and HAPS layer adopt OFDMA as their multiple address access mechanisms. Considering the research purpose of agenda item 1/4 of WRC-23, both IMT and HAPS layers employ TDD mode and operate in the same frequency bands.