# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 6 Numerical Results
### Simulation Results
#### 6.3.1 Convergence Performance  
Figure 3 reveals the convergence of COPP-DDPG with different actor learning rates, \(\alpha_{a}\). The choice of learning rate \(\alpha_{a}\) can obviously affect the convergence effect and the speed of COPP-DDPG. From Figure 3, COPP-DDPG cannot be convergent due to the greater learning rate when \(\alpha_{a}=0.01\). The convergence of COPP-DDPG is obviously improved when \(\alpha_{a}=0.005\), but the result is not satisfactory. When \(\alpha_{a}=0.0005\) and \(\alpha_{a}=0.00001\), COPP-DDPG can converge on certain results, and the proposed algorithm does not perform well. Considering the stability and performance of COPP-DDPG, we set the actor's learning rate as \(\alpha_{a}=0.0001\), which is shown as the blue curve in Figure 3.  
In this paper, we consider the priority of arriving tasks generated by ICVs in each time slot. Figure 4 shows the convergence of the proposed COPP-DDPG with three different priorities, \(Q_{i}\). As described in subsection _V.A_, the values of priorities were set as \(Q_{i}=0.6+0.3i\); that is to say, \(Q_{1}=0.9\), \(Q_{2}=\)1.2, and \(Q_{3}=1.5\). As illustrated in Figure 4, due to the fewer values of low priority, the performance of the vehicular task with priority \(Q_{1}\) is better than the cases where the vehicular task priority is \(Q_{2}\) or \(Q_{3}\). The fewer the values of the priorities are, the greater the immediate reward and the less the cost. Figure 3: Convergence of COPP-DDPG with different actor learning rates \(\alpha_{a}\). It can be observed in Figure 4 that COPP-DDPG converges under the situation of three different priorities. In terms of the partial offloading for vehicular edge computing, Figure 5 illustrates the convergence of proposed COPP-DDPG with different partial offloading rates \(\lambda_{i}\). When \(\lambda_{i}=0.1\), the convergence of COPP-DDPG can be satisfied. However, from the perspective of the average cumulative rewards, the performance of COPP-DDPG whose offloading rate was set as 0.1 was not good due to the local processor of ICVs having remaining resources. When \(\lambda_{i}=0.4\), the partial offloading ratio of vehicular tasks reserved locally was too large, which led to the shortage of the local processor resources. Furthermore, due to more incomplete tasks, which caused the punishment, the performance of COPP-DDPG was greatly reduced. When \(\lambda_{i}=0.2\), it made better use of the computational resources of the ICV's processor and the resources of the VEC and MEC server. Its performance was the best. Therefore, we set the ratio for tasks offloading as \(\lambda_{i}=0.2\) in this work. Figure 4: Convergence of COPP-DDPG with three different priorities \(Q_{i}\). Figure 5: Convergence of COPP-DDPG with different partial offloading rates \(\lambda_{i}\). Figure 6 indicates the comparison of five algorithms with different arrival probabilities of tasks \(P_{task}^{arr}\). The vehicular task arrival rate represents the probability that ICVs can generate computation tasks in each time slot. When the vehicular task arrival rate is low, the vehicular task is processed in a timely pattern due to the small number of vehicular tasks, so that the computational tasks' delay is low and the proportion of tasks not processed is greatly reduced, which means that there is less punishment, so the average cumulative reward is greater. As shown in Figure 6, the COPP-DDPG algorithm keeps the best performance no matter what the vehicular task arrival rate is. As ICV's local processing capacity is the lowest, the performance of LE-VP is the worst. Compared with all vehicular tasks offloaded to the MEC server strategy, although the transmission rate of VEC is better, its computing capacity is lower. Therefore, when the task arrival rate is low, such as \(P_{task}^{arr}=0.25\), the performance of OT-VEC is better than that of OT-RSU. However, when the task arrival rate is high, the performance of OT-VEC is worse than that of OT-RSU. Figure 8: Comparison of all algorithms with different transmission rates of V2V.