## 5 Evaluation  
### Experience Setup  
In order to evaluate the performance of the proposed time-frequency-space resource allocation method based on network-level user experience maximization, we conduct a simulation and compare it with the classical load balancing method based on PRB utilization. Through the analysis of performance indicators such as user experience rate and throughput rate, we verify the effectiveness and superiority of the method. We use C++ object-oriented programming language to build the whole communication process, including instantiating various types of network elements, base stations, users, network configurations, and servers in communication scenarios. We also simulate the process of scheduling and receiving service requests, channel measurement, and resource allocation. In addition, we use Python to realize the deep learning method for information prediction.  
The end-to-end system-level simulation process in this paper simulates the whole communication process. It does not involve modifications in the PHY layer, which is fully implemented in accordance with 3 GPP NR standards. The simulation focuses on the design of the resource allocation in the multi-user scenario on a high level. In the simulation process, we begin by configuring simulation-specific model parameters for scene construction and channel initialization. This primarily includes initial user deployment, business model, and channel model. Specifically, for user deployment, we distribute a specified number of users evenly across the geographical locations of each cell. Users select the optimal RSRP beam for access, with a minimum access RSRP of \(-113\) dB. If a user does not meet the access conditions for any beam, the user is repositioned. Regarding the business model, we employ the Burst model defined in 3 GPP TR, where each user's packet size and packet duration vary, and the inter-packet intervals differ. For the channel model, we utilize the large-scale channel model defined in 3 GPP TR 37.910. In addition, in the simulation environment, different cells are overlapped by common stations. Noise power spectral density is 7 dB. The transmitting power of the base station side is 46 dB in 20 M bandwidth. The detailed simulation configuration parameters are shown in the Table 6. Next, we proceed with business scheduling and channel-related computations. Subsequently, we employ the proposed time-frequency-space resource allocation algorithm to further conduct user behavior scheduling. Throughout the simulation, relevant statistics are recorded, including user traffic volume, user session durations, available RBs in each cell, and the number of users in each cell. The simulation outputs encompass network-level user experience rate, SINR, and cell-level PRB utilization.  
The specific flow for one scheduling cycle is as follows. Specifically, first, we randomly scatter points. When t = 0.5 ms, collect the initial data involving the service cell and the co-frequency neighbor cell, input them to the trained smart grid model, and predict the inter-frequency RSRP, spectral efficiency, and beam information; then, input them into the optimization formula for accumulation. The time step length is 0.5 ms. When t = 1 ms, continue to record the data of the service cell and the co-frequency neighbor cell at this time, these data change due to changes in information such as user location and service. Input these data into the smart grid model again, predict the RSRP, spectral efficiency, and beam information at this time, and provide this output to the optimization formula to continue to accumulate again. At this time, a scheduling cycle finished. Finally, we solve the optimization algorithm to calculate the resource allocation combination that maximizes the objective function.