## 1 Introduction  
In the digital era, with the widespread use of smart devices and the continuous expansion of mobile internet services, global mobile data traffic has been growing explosively [1, 2, 3, 4, 5]. This trend has placed higher demands on existing wireless communication systems in terms of transmission rate. To meet these challenges, researchers have started to investigate new communication methods including 6G technologies [6]. In particular, non-orthogonal multiple access (NOMA) technology has been proposed and rapidly developed. It has become an effective means to improve the performance of wireless communication systems and is receiving significant attention in the research of 5G and future network technologies. NOMA technology allows multiple users to transmit signals simultaneously in the same frequency band and time resource, which significantly improves spectral efficiency compared to the traditional orthogonal multiple access (OMA) technology [7, 8, 9]. The base station allocates different power levels to users on the same subchannel, enablingthe receiver to decode signals of users one by one in order of their power levels using the successive interference cancelation (SIC) technique [10], thereby achieving multi-user sharing in the power domain. The introduction of SIC technology provides a new solution to the problem of allocating limited wireless communication resources, but it also presents a series of technical challenges. To achieve efficient operation of NOMA technology, the key is to optimally allocate channel and power resources in a dynamic network environment. This joint channel and power allocation problem has been proven to be NP-hard [11; 12]. Namely, to find the optimal solution in the current environment, all possible combinations of resource allocations must be traversed. However, in multi-user and multi-cell scenarios, even if this exhaustive search method is feasible, the consumption of time and computing resources is unbearable. According to the most relevant related works summarized in [13], most of the research objectives are mainly focused on the sum rate and energy efficiency. It can be seen that how to reasonably allocate these resources to maximize the sum rate and the energy efficiency has become an important issue in the technical research and practical applications of NOMA systems. Many traditional algorithms have been proposed to solve the resource allocation problem [14; 15; 16; 17; 18; 19; 20; 21], such as game theory methods and convex optimization techniques. However, due to the highly complex channel characteristics, these traditional methods may have limitations in dealing with the resource allocation problems under large-scale environments. Additionally, while these traditional resource allocation strategies are feasible to a certain extent, their efficiency and reliability often fail to meet the demands of modern wireless communication systems in dynamic network environments. In response to this, researchers have begun exploring the use of the artificial intelligence technologies [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33] to solve the resource allocation problem. Studies have shown that deep learning has unique advantages in addressing resource allocation problems in the field of wireless communications [31; 32; 33; 34; 35]. For example, in supervised deep learning, common approaches include genetic algorithms or other heuristic algorithms that generate labels to train neural network models for channel assignment and power control [34; 35]. In deep reinforcement learning, researchers typically employ deep Q-network (DQN) to formulate channel allocation policies and use deep deterministic policy gradient (DDPG) algorithm for continuous power control in dynamically changing wireless environments [33]. In addition, the application of unsupervised learning to the power control problem has also attracted much attention.