## 4 Simulation Results  
This section presents the experimental results conducted to evaluate the performance of the proposed algorithm in an HSR environment with MECSs. The experimental environment was built based on python 3.9 and pytorch 2.4.1+cu118. The experimental environment consists of HSTs moving along HSRs track and BSs with MECSs. The BS was placed along the railway. The experiment was conducted in a scenario including multiple HSTs and MECSs. Multiple passengers were set to create tasks through UEs inside the HST, and the number of HSTs was designed to change dynamically over time. In addition, the movement information of the HST was conducted using SUMO (Simulation of Urban MObility) [24]. The proposed model for the simulation is an initial scenario model for subsequent studies. In the experimental environment, the speed of HSR was set to 0-100 m/s, and the service radius of BS was configured to 200 m. The task occurrence was modeled by forming a distribution based on the Poisson Process, and the performance of the proposed algorithm was evaluated in various network and task and HST environments. The experimental parameters in HSR are referred to in [20]. The specific experimental parameters are specified in Table 1.  
The network structure used in this paper is designed based on the TD3 algorithm. The actor network receives a state as input and generates continuous actions, and the structure consists of a fully connected layer of [256, 256]. The first hidden layer is mapped from the state space size to 256 units, and the second hidden layer maintains 256 units. The last layer is mapped from 256 units to the action space size, and the output value is converted to the parameters of the Dirichlet distribution through the Softplus activation function. Through this, the action sampled from the Dirichlet distribution is output so that it can solve continuous and probabilistic ratio-based problems. The Critic network consists of two independent Q-networks (\(Q_{\theta_{1}},~{}Q_{\theta_{2}}\)), which take state and action as input and output Q values. Each Q-network consists of a fully connected layer of [256, 256], where the first hidden layer maps 256 units from the input of the state space size and the action space size, and the second hidden layer also maintains 256 units. The last layer outputs a single scalar value from the 256 units to generate the Q value. Both hidden layers used the ReLU activation function, and the two Q-networks were trained independently to ensure the stability of Clipped Double Q-Learning. The specific hyper-parameter values used in TD3 are specified in Table 2. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline The number of BSs & 10 \\ Task arrival rate (\(\lambda\)) & 30 \\ Task data size (\(S(t)\)) & [0.1, 0.6] Mbit \\ Task workload (\(C(t)\)) & 1100 cycles/bit \\ Noise (\(\sigma^{2}\)) & \(-\)100 dBm \\ Pathloss model (\(g_{n,m}(t)\)) & WINNER II D2a [25] \\ The computation capability of MECS \(k\) (\(f_{k}\)) & 10 GHz \\ Bandwidth (\(B\)) & 100 MHz \\ Time duration per 1 slot (\(t\)) & 0.5 s \\ \(L_{max}\) & 5 s \\ The number of episode (\(E\)) & 2000 \\ Power of the HST (\(p_{n,m}(t)\)) & 23 dBm \\ \hline \end{tabular}
\end{table} 
Table 1: The parameters setting of the HSR. In order to evaluate the proposed algorithm in this paper, the performance was compared with the following three different types of schemes. 1. Random: This method randomly selects the task partitioning ratio, without considering the task characteristics or MECS status. This is used as a reference point to check how much the structured decision-making method improves performance. 2. No Partition: This method processes all tasks on the initially selected MECS without performing task partitioning. This is used to compare the impact of task partitioning on resource utilization and performance. Figure 4a illustrates the performance showing task throughput as the number of serving MECSs increases.