# Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs
## 5 Simulation and Performance Analysis
This method is similar to [27] in terms of factors considered, although the lifetime factor of candidate links is not predicted in the former. We first show the convergence performance of the proposed DRL-FIR method with different decay values in Figure 4. The decay value \(\gamma\) can reflect the preference of the model for present and future rewards in the training process. As can be seen, both the proposed method and FIR-OH can converge under different parameter configurations. This reflects the effectiveness of the proposed method. We could observe that the proposed method has the best performance when \(\gamma=0.9\), which is consistent with the feature that the data need to be finally transferred to the destination in the routing scenario. Since DRL-FIR is capable of perceiving the regional dynamics of the node's environment more comprehensively compared to FIR-OH, the performance after convergence of the former is better than that of the latter under the same configuration. More specifically, the information about the channel state, link lifetime, spatial location, and MAC queue length of the neighboring nodes within the two hops rather than the one hop enables the DRL-FIR method to determine a better next hop than the FIR-OH method, so as to obtain greater rewards. In the subsequent simulation, we set the decay value as 0.9. In order to intuitively show the performance of the proposed DRL-FIR in this paper, the packet loss rate (PLR) and delay performance of methods for random FANET topology snapshots are shown in Figures 5 and 6, respectively. It can be observed that the methods show obvious differences in their performance in terms of PLR. It should be noted that compared with factors such as channel capacity and spatial location, the PLR performance is closely related to the link lifetime and MAC queue length, as these two can greatly affect the reliable reception of data packets. In contrast, the delay performance is more concerned with channel capacity and spatial location factors. Figure 4: Convergence performance of the proposed method under different decay values. The performance of FIR-DLT is slightly better than that of FIR-OH; this is because the PLR has little to do with the capacity status of the channel, and FIR-DLT can give play to the advantage of a multihop node environment perception without considering it. Meanwhile, the PLR performance of the FIR-CD method deteriorates as the relevant queuing and link lifetime factors have not been considered. GPSR has the worst performance because it only considers the distance between each surrounding one-hop node and the destination node. The PLR performance of W-GFCD is significantly better than that of GPSR due to the more abundant influence factors considered. As the link lifetime parameter is referenced in the decision-making, its PLR is also lower than that of FIR-CD. It is worth noting that the performance of the methods in terms of transmission delay changes significantly. Since the distance and channel capacity factors have the greatest relationship with the transmission delay, the performance of the FIR-CD method is better than that of the FIR-OH, W-GFCD, and FIR-DLT methods. For the same reason, the FIR-DLT and GPSR methods basically show similar performance without considering the channel capacity factor. By observing the two figures, it can be concluded that the performance of DRL-FIR is better because it considers more comprehensive influencing factors. Finally, the average performance of the methods for 1000 randomly generated FANET topology snapshots is summarized in Table 2. It can be seen that GPSR shows the worst performance in terms of both PLR and transmission delay. This is because the factors it considers are too limited or single in area size and node state.