## 5 Experiment Results and Analysis  
A simulation platform has been built to illustrate the proposed method which realizes the air interface communication and handover process of the cellular system, as well as implements the two DQN algorithms proposed in this paper. Table 1 presents the assumption of some important parameters. Data set for LSTM training and validating is first gathered. The true positive and true negative possibilities of LSTM-predicting events achieve 91.64% and 87.96% for handover failure and 92.19% and 91.98% for handover ping-pong, respectively. In addition, the number of samples entering the digital twin is at least 1.4 times that of the physical world. Thus, the unbiased estimation variance of the digital twin is at most 1.12 times (for failure prediction) and 1.01 times (for ping-pong prediction) that of the physical world. It can be considered that the estimation accuracy of digital twins is at the same level as that of the physical world. Figure 6 shows the average reward iterative curves of the two DQN methods. It can be seen that no matter which method is applied, the reward can be gradually increased until it is roughly stable at a higher value, and the DTe-DQN method is better than the ordinary one in the following three aspects. First, the DTe-DQN has a faster convergence speed. This advantage reveals that in the early stage of algorithm iteration, more reward information can be collected quickly within a limited measurement feedback cycle. Both rewards from the real world or the digital twin makes the exploration more efficient. Second, the DTe-DQN has strong stability, which has less fluctuation and is more difficult to degenerate to a worse local optimum. This is still due to more exploration opportunities, guaranteeing robustness. Finally, the DTe-DQN has better performance and can get a higher reward value, which is the result of the combined effect of convergence efficiency and robustness. Figure 7 shows the average handover failure rate and ping-pong rate during the iterative process. Both methods can achieve the decrease in the average handover failure rate and ping pong rate, but the DTe-DQN is more efficient and stable. Although the handover failure rate has only dropped by about 2-3%, the ping-pong rate has decreased \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Configurations** \\ \hline \(n_{\text{period}}\) & 80,000 \\ \(n_{\text{$\varepsilon$}}\) & 500 \\ \(\beta\) & 0.9 \\ Learning rate decay & Exponential decay \\ Optimizer & Adam \\ Activation & ReLu \\ Initial learning rate & 0.001 \\ Inter-site distance & 50 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation and algorithm parameters. Figure 6: Average reward iterative curves. by about 8-9%. It is revealed that in the scenario of UDN, ping-pong rate is an easier optimization indicator, or in other words, the final effective handover rate improvement depends more on the decline of ping-pong rate. The iterative curve of the average effective handover rate is presented in Figure 8. In addition to the fact that the effective handover rate of the DTe-DQN increases faster at the initial stage, the final convergence effect of the DTe-DQN is higher than that of the DQN. The former is about 83.1% and the latter is about 80.4%. Since this paper is optimized for different signal fading conditions, the performances of different fading intervals are also the focus of this paper. Figure 9 shows the iterative process of the average reward in different fading intervals. The fading intervals here are divided into 1-2 dB, 2-3 dB, and 3-4 dB according to the fading standard deviation. It can be seen that as the signal fading condition becomes severe, the achievable reward declines, and the convergence effect fluctuates more. Therefore, the larger the signal fading standard deviation is, the harder the optimization is. Although there are differences in the reward  
Figure 8: Average effective handover rate iterative curves. Figure 7: Average handover failure rate and ping-pong rate iterative curves.