# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 5 Performance Evaluation
### Simulation Environment and Parameters  
A UAV-assisted air-space integration network scenario is considered; we used Python to simulate and evaluate the proposed algorithm. In our simulations, we consider a geographical area of \(1\) km \(\times\) 1 km, in which IoT devices are then randomly deployed within the area [38]. With LEO satellites flying at \([700,1000]\) km and UAVs flying at 100 m [39], the LEO satellites provide a seamless coverage of the geographical area. We assume that the efficiency of the satellite, IoT device and UAV antenna are 0.6, 0.55 and 0.6, respectively [34]. The maximum available computing resources of the satellite are evenly distributed in the \([3,8]\) GHz interval. The detailed simulation parameters are given in Table 1[40; 41; 42]. The DDPG-LSTM algorithm with a LSTM layer and the neural network [43] uses the Relu, Tanh and sigmoid functions as the activation function, while the end actor network results use softmax to restrict actions. Some critical parameters are analyzed to explore the impact of the algorithm parameters. For each parameter studied, we provide some possible reference values. Energy consumption and the delay weighted sum is used as an evaluation criterion to explore the effect of parameters. The detailed algorithm parameters are given in Table 2.  
Figure 3 represents the performance of the algorithm at different learning rates. The update step's size affects the convergence speed, and when the learning rate is too low, the algorithm converges slowly. When the learning rate is too high, the maximum value may be missed due to the excessive size of the update step. The graph shows that the algorithm performs optimally if \(\delta_{a}=0.0001\) and \(\delta_{c}=0.001\). In addition, it can be found that the network performs better when \(\delta_{c}\) is greater than \(\delta_{a}\) because the actor network needs guid \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Computing resources of LEO satellite, \(f_{j}\) & \([3,8]\) GHz \\ CPU cycles for processing one bit, \(\beta\) & 100 cycles/bit \\ Effective switched capacitance, \(k\) & \(10^{-27}\) \\ IoT maximum transmit power, \(p_{i}\) & 23 dBm \\ UAV maximum transmit power, \(p_{m}\) & 40 dBm \\ Coverage radius of UAV, \(d_{max}\) & 1 km \\ Height of LEO satellite, \(H_{j}\) & \([700,1000]\) km \\ Number of bandwidth chunks, \(K\) & 6 \\ Noise spectral density, \(N_{0}\) & \(-174\) dBm/Hz \\ Bandwidth of UAVs, \(\omega_{m}\) & 1 MHz \\ Bandwidth of LEO, \(B\) & 10 MHz \\ UAV altitude, \(H_{m}\) & 100 m \\ Light speed, \(c\) & \(3\times 10^{8}\) m/s \\ Task size, \(S_{i}\) & \([2.5,3.5]\) MB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Buffer capacity, \(M\) & 1,000,000 \\ Batch size, \(B\) & 256 \\ Learning rate, \(\delta_{a},\delta_{c}\) & 0.0001, 0.001 \\ Soft update rate, \(\tau\) & 0.005 \\ Discount factor, \(\gamma\) & 0.99 \\ Exploration rate, \(\epsilon\) & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm parameters. ance from the critic network to learn. When the critic network learns faster than the actor network, it can better guide the update direction of the actor network. Figure 4 shows the algorithm's performance under different soft update rates. Compared to the complex update strategy, the upgrade interval of the objective network is reduced by the soft update strategy. It ensures that the target mesh is updated in every iteration, increasing the frequency of updating the target mesh and helping to decrease the time taken for the algorithm to converge. The smaller the soft update coefficient, the more stable the algorithm will be and the less the parameters of the target mesh will change, resulting in a too-slow convergence of the algorithm. If the soft update coefficient is too large, the algorithm will be unstable. Therefore, an appropriate soft update factor can make the algorithm stable and fast.