# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 6 Simulation Results  
In this section, we will verify the superiority of our proposed algorithm through a large number of experiments, including: analysis of convergence, comparative analysis of system utility function values, and time cost analysis of system and DNNs. In this paper, we think that there are three users with computing needs on the ground side, and each user has three computing tasks. This paper thinks that the wireless channel transmission signal conforms to the free space path loss model. \(L_{p}\) can be represented as \(L_{p}=32.4+20\lg(D)+20\lg(F)\), where \(D\) is 200 km and \(F\) is 30,000 MHz. We set user transmit power \(p=10\) mw and noise power \(\sigma=10^{-13}\) w. In this paper, we think that the tasks obey a uniform random distribution of (10 MB, 30 MB), the processing energy consumption of the users in local computing is \(3.25\times 10^{-7}\,\mathrm{J/bit}\). When the users are computing offloading, the energy consumption of transmitting tasks is \(1.42\times 10^{-7}\,\mathrm{J/bit}\), the total uplink bandwidth of the system is 150 Mbps. For DNNs, each DNN consists of three layers. The activation functions of the first and second layers are the rectifiers linear unit (ReLU), and the activation function of the third layer is the sigmoid function. The weight matrix size of the first layer is \(9\times 120\), the weight matrix size of the second layer is \(120\times 80\), and the weight matrix size of the third layer is \(80\times 9\). The number of neurons in the first layer is 9. The number of neurons in the second layer is 120. The number of neurons in the third layer is 80. The optimizer is the AdamOptimizer. The effect of the number of training steps on the convergence of DNNs is showed in Figure 3. It can be seen from Figure 3, as the number of training steps increases, the cost of DNNs tends to 0 rapidly, and DNNs can achieve good convergence effect with a small number of training steps. Figures 6 and 7 show the comparison of system utility function values corresponding to different offloading algorithms under different LEO satellite MEC server processing rates \(f^{sat}\) and local devices' processing capabilities \(t_{local}\). As shown in Figure 6, with the increase of the LEO satellite MEC server processing rates \(f^{sat}\), the system utility function values of the four offloading algorithms will decrease. This is because the higher processing rates of the MEC server reduce the processing time during the offloading process, and therefore the system utility function values decrease. It can be seen from Figure 6 that the downward trend of the full offloading algorithm is very obvious. This is because the total processing time on the LEO satellite of the full offloading algorithm only depends on the processing rates of the LEO satellite MEC server. As can be seen from Figure 7, the system utility function values of the three offloading algorithms increase with the local devices processing capabilities \(t_{local}\) because \(t_{local}\) is the processing time required by the local devices to compute each data bit locally. A larger \(t_{local}\) means that the tasks take more processing time to process locally, so the system utility function values of the three algorithms increase accordingly. It can be seen from Figure 7 that the values of the system utility function of the full offloading algorithm do not change with the increase of \(t_{local}\). This is because the system utility function values of the full offloading algorithm are independent of local devices processing capabilities, so this result is reasonable. Among the four offloading algorithms, our proposed algorithm achieves the optimal system utility function values. This is because our algorithm generates the offloading decisions with the lowest system utility function values through the DNNs, and can allocate the system bandwidth adaptively, so we can obtain the optimal system utility function values.