# Generating Datasets for Real-Time Scheduling on 5G New Radio
## 2 Related Work  
There exist two categories of methods for generating scheduling datasets. The first category involves solving scheduling problems and then compiling the solutions to form a dataset. The second one focuses on expanding existing datasets to make them larger. The following provides introductions to the two categories. For solving 5G scheduling problems, some methods have been proposed. The work in [12] focused on an ideal scheduling model with fine-grained and flexible frequency resources and proposed an orthogonal frequency-division multiple access-based scheduling method to reduce the impact of low-latency communications on other communications. The work in [13; 14] transformed 5G NR scheduling problems into programming problems and then proposed a method based on reinforcement learning to dynamically send packets according to instantaneous requirements. The work in [15] integrated frequency adjustment and resource allocation into one problem and used linear programming and Lagrangian duality to reduce the transmission delay and improve the transmission rate. In order to improve control performance, the work in [10] combined control with 5G and adjusted data transmissions according to current control needs. The work in [11] proposed a scheduling algorithm for multi-user, multiple-input, and multiple-output networks. The algorithm decomposes the problem into multiple sub-problems and runs them on the GPU in parallel, thereby reducing the execution time of the algorithm to 1 ms and realizing fast response to transmission requirements. The work in [16] added the execution time of the proposed scheduling algorithm to its processing process and designed a 5G scheduler based on deep reinforcement learning to improve the system reliability. The work in [17] considered the impact of signaling on scheduling and proposed an easy-to-implement resource reselection mechanism to improve the reliability of 5G vehicle networking. The work in [18] treated signaling as a resource consumption and effectively reduced the transmission latency through system-level optimization. The work in [19] formulated the problem of scheduling broadcast and unicast as a sequential decision problem and proposed a deep reinforcement learning-based algorithm to solve it. The work in [20] proposed a fair request scheduling method to solve the coexistence problem of 5G NR and WiFi. The work in [21] focused on the QoS management for XR traffic in 5G NR and applied a weight-based scheduling policy to ensure traffic QoS. Because the 5G NR scheduling problem addressed in this paper is NP-hard, there is no polynomial-time optimal algorithm for it. To obtain the optimal solution, mathematical solvers must be invoked. However, the execution time of these solvers is excessively long, rendering them infeasible for real industrial applications. As a result, most of the relevant literature proposed non-optimal heuristic methods, leading to a gap between their results and the optimal solution. Although the existing solver-based methods can yield optimal solutions in the absence of time considerations, none have accounted for the non-ideal channel conditions that our problem model focuses on. The problem model in [9] is similar to that in this paper. However, the work in [9] adopted classical real-time theory to solve the 5G NR scheduling problem. There is still a gap between the schedules and the optimal solution. Therefore, in this paper, we will propose two novel methods to obtain optimal solutions, which cannot be obtained in [9]. For expanding the scale of datasets, the generative adversarial network (GAN) is a widely used algorithm at present. GAN is a deep generative neural network proposed by [PERSON] in 2014 [22]. At first, GAN was mainly used for image and text enhancement in the direction of computer vision, but because of its ability to capture the underlying distribution of data, GAN soon was successfully applied to other research fields, such as communication and medical care.