# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 4 System Simulations and Performance Evaluations
### Different Numbers of Source-Destination Pairs for the Sparse and Congested Periods
However, IDRF_DQN_RF does not account for potential improvements in packet relay paths arising from newly introduced real-time vehicles. Therefore, the IDRF framework introduced in this study executes relay vehicle decision-making by considering new vehicles in real time, end-to-end delays, and inter-vehicle connection stabilities in the real-time phase. By combining the integrated RF and DQN models in the offline phase to overcome the abovementioned constraints, the IDRF determines the optimal packet relay paths based on the real-time VANET environment. For sparse periods, with 32 pairs, the average packet delivery ratio of IDRF surpasses that of Epidemic. Notably, for congestion periods with 16 and 32 pairs, the average packet delivery ratios of the IDRF outperform those of Epidemic. Figures 34 and 35 illustrate the average end-to-end delays for different numbers of pairs for sparse and congested periods, respectively. Epidemic disseminates packets through broadcasting, which could yield the optimal packet forwarding path with the shortest end-to-end delay in an unknown VANET environment. Since there are multiple forwarding paths, Epidemic calculates the average delay for all possible paths through which packets may ultimately be received by the destination vehicle. This aggregation contributes to the relatively higher performance in terms of the average end-to-end delay observed in Epidemic. GPSR, which does not consider how vehicles carry packets, exhibits the poorest average end-to-end delay performance. TDRL-RP and VRDRT, which do not address the handling of untrained real-time vehicles, may result in prolonged packet-carrying delays during transmission to such vehicles. As mentioned above, the IDRF_DQN approach exclusively prioritizes vehicles with the highest delay weights, namely those associated with the packet forwarding path with the shortest end-to-end delay. Therefore, IDRF_DQN yields the lowest average end-to-end delay for packets received among all methods. IDRF_DQN_RF considers both the DQN for selecting vehicles with the highest delay weights and the RF for determining vehicle movement direction, leading to a slightly higher performance in terms of the average end-toend delay compared to IDRF_DQN, yet its performance remains lower than other methods. IDRF is built upon the foundation of IDRF_DQN_RF and further considers new forwarding paths consisting of untrained real-time vehicles. When compared to the optimal propagation paths achieved through trained vehicles, IDRF exhibits a slightly higher average end-to-end delay than IDRF_DQN and IDRF_DQN_RF, but it still outperforms the other methods. Figures 36 and 37 show the average overhead ratio with different numbers of pairs for sparse and congested periods. Packets are disseminated through broadcasting using the Epidemic protocol. As the number of pairs increases, the number of replicated packets in the VANET environment also increases, resulting in the highest average overhead ratio. Although GPSR only considers the distance relationship between the vehicle carrying the packet and the destination vehicle, it performs better than VRDRT. Given that VRDRT requires the retrieval of vehicle information in each time frame, its increasing average overhead ratio is notable, causing it to exhibit a higher average overhead ratio compared to other protocols, except for Epidemic. Meanwhile, TDRL-RP continually uses trial and error to train different transmission paths during packet forwarding. Consequently, its average overhead ratio is also higher than those obtained by the methods proposed in this paper, namely IDRF_DQN, IDRF_DQN_RF, and IDRF. IDRF_DQN achieves the lowest average overhead ratio among the compared methods as it focuses solely on employing DQN to make packet relay decisions. However, its performance regarding the average packet delivery ratio is unsatisfactory. IDRF_DQN_RF enhances this by incorporating RF decisions to determine the vehicle movement direction in cooperation with DQN-based packet relay decisions.