## 5 Evaluation
### Experience Results  
#### 5.2.1 Overall Performance  
Firstly, we discuss the performance gains compared with the load balancing method based on PRB utilization in the experiment scene, which is composed of two TDD cells, each of which owns 20 M bandwidth. Specifically, the load situation, the number of antennae, the number of users, and overall performance gains are shown in Table 7. As we can see, in all four different scenes, we obtain performance gain in network-level downlink user experience rate ranging from 17% to 30%. In Figures 11-14, we demonstrate the user experience rate of each user. Blue indicates the method based on the PRB utilization rate, and orange represents our proposed method based on time-frequency-space optimal resource allocation. As we can see, almost all of the users show a higher user experience rate than the traditional method. Although the performance of a small number of users is degraded, the overall user experience of the entire network has greatly improved. As shown in Figure 15, it is the average experience rate of the overall users. We can see the performance improvement for all the four experience scenes. In the mid-low load scenario, leveraging the load balancing method based on PRB utilization, the perceived downlink rate of the whole network user is 99.41 Mbps and 86.34 Mbps with 64 TRX and 32 TRX, respectively. For the time-frequency-space resource allocation method, the perceived downlink rate of the whole network user is 119.08 Mbps and 101.57 Mbps, respectively. The perceived downlink rate of the whole network user is increased by 21% and 17%, respectively. In the mid-heavy load scenario, leveraging the load balancing method based on PRB utilization, the perceived downlink rate of the whole network user is 86.34 Mbps and 64.71 Mbps with 64 TRX and 32 TRX, respectively. For the time-frequency-space resource allocation method, the perceived downlink rate of the \begin{table}
\begin{tabular}{c c} \hline
**Type** & **Detailed Configuration Parameters** \\ \hline Networking mode & (1) TDD 3.5 G@20 M+TDD 3.5 G@20 M \\ \multirow{2}{*}{Networking mode} & (2) TDD 3.5 G@40 M+TDD 3.5 G@40 M \\  & (3) TDD 3.5 G@40 M+TDD 3.5 G@40 M+FDD2.1G@20 M \\ \hline Number of antenna & 64 TRX/32 TRX \\ \hline Load & (1) Mid-Low \\ \multirow{2}{*}{User distribution} & (2) Mid-Heavy \\ \hline User distribution & Distribution ratio of users in the near, mid, and far: 1:2:1 \\ \hline Channel model & Urban Macro-cellular (UMa) model \\ \hline Sub-channel frequency & 30 KHz \\ \hline \multirow{2}{*}{Waveform modulation scheme} & Filtered-Orthogonal Frequency \\ \cline{2-2}  & Division Multiplexing (F-OFDM) \\ \hline \end{tabular}
\end{table} 
Table 6: The simulation configuration parameters. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Scene** & **Cell1** & **Cell2** & **Load** & **Antenna** & **User** & **Gain** \\ \hline Scene 1.1 & TDD @20 M & TDD @20 M & Mid-Low & 64 TRX & 50 & 21\% \\ Scene 1.2 & TDD @20 M & TDD @20 M & Mid-Low & 32 TRX & 40 & 17\% \\ Scene 1.3 & TDD @20 M & TDD @20 M & Mid-Heavy & 64 TRX & 80 & 30\% \\ Scene 1.4 & TDD @20 M & TDD @20 M & Mid-Heavy & 32 TRX & 70 & 26\% \\ \hline \end{tabular}
\end{table} 
Table 7: Performance Gain for Networking TDD@20 M+TDD@20 M.  
whole network user is 111.57 Mbps and 81.53 Mbps, respectively. The perceived downlink rate of the whole network user is increased by 30% and 26%, respectively. Figure 11: Performance comparison for each user in scene 1.1. Figure 12: Performance comparison for each user in scene 1.2. Figure 13: Performance comparison for each user in scene 1.3. In addition, by conducting separate statistics for each cell, as shown in Figure 16, it can be found that the proposed method can also effectively improve the average throughput for different cells.