## 4 Experiment
### Datasets  
The data sources utilized for traffic prediction in this study were established based on the ON/OFF data source overlay proposed by Bell Labs [27]. The ON/OFF model indicates that data generation occurs during the ON period, while no data are generated during the OFF period [28]. In Figure 8, the ON/OFF source overlay process is illustrated. As shown in Figure 8, suppose that there are N independent data sources \(x_{i}(t),\ i=1,2,\ldots,N\), then the ON/OFF time of each data source is subject to Pareto distribution. Furthermore, \(x_{i}(t)=1\) indicates that the data source is in the ON period and generates data, whereas \(x_{i}(t)=0\) indicates that the data source is in the OFF period and no data are generated. At time t, the data of N data sources are superimposed to obtain the data size of the current system. Regarding the ON/OFF cycle duration, it has been shown that when the ON and OFF times adhere to a heavy-tailed distribution, the resulting superimposed data exhibit self-similarity properties. The Pareto distribution aligns with the characteristics of heavy-tailed distribution, with its distribution function taking the following form:  
\[F(x)=1-\left(\frac{k}{x}\right)^{\alpha}, \tag{19}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Batch size & 32 \\ Optimizer & Adam \\ Number of hidden layer nodes & 12 \\ CPO population size & 5 \\ CPO iterations & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model hyperparameter settings. Figure 8: The ON/OFF source superposition process. where \(\alpha\) is a shape parameter with values in the range (1, 2), and \(k\) is a location parameter that determines the lower bound of the random number generated by the Pareto distribution. Each ON/OFF data source requires two sets of parameters, \((k_{on},\alpha_{on})\) and \(\left(k_{on},\alpha_{off}\right)\), to represent it. For the self-similar flow data generated from the superposition of the N ON/OFF sources, the self-similarity parameter \(H\) is related to the \(\alpha\), and it satisfies the following equation:  
\[H=\frac{1}{2}(3-\min\{\alpha_{1},\alpha_{2},\ldots,\alpha_{N}\}). \tag{20}\]  
Assuming there are \(N\) ON/OFF data sending sources in an ON/OFF traffic mode, each source generates a flow at time \(t\) denoted as \(X_{1}(t),X_{1}(t),\ldots,X_{N}(t)\), where \(t\) is a discrete time. The total flow \(X(t)\) is synthesized by the \(N\) sources as follows:  
\[X(t)=\sum_{i=1}^{N}X_{i}(t). \tag{21}\]  
In an FTIR network, since each room or area has its own ONU-AP, the traffic generated by multiple terminals under one ONU-AP is superimposed at that access point. Since each user's traffic conforms to the self-similarity property, its superimposed traffic also conforms to this property, and the ON/OFF model is able to simulate the data sending of the service source in different states; as such, traffic with self-similarity properties is generated by adjusting the packet sending rate, packet sending interval, etc., in the ON/OFF model. In this paper, the data traffic size was set to 10 Gbps, which is also in line with the traffic size in the actual situation of FTIR usage. Based on the aforementioned ON/OFF model, the traffic generated by the user roaming indoors was modeled using OPNET Modeler 14.5 software, as shown in Figure 9.  
As can be seen from Figure 9, this OPNET modeling superimposes ON/OFF data sources, where the ON/OFF time satisfies pareto(1,1.4) and pareto(1,1.2) distributions, respectively. During the ON period, packet sizes are uniformly distributed between (64,1518). The 32 data sources access a cache queue and are then received by a statistical node module that is responsible for traffic counting and data processing. To verify the self-similarity characteristics of the generated traffic, two sets of traffic data with time scales (Time Scale) of 0.5 s and 2 s, each with a length of 10,000, are collected from the simulation system.