# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 5 Experiment and Result Analysis
### Discussion of Results  
Improved deep reinforcement learning (IDRL) differs significantly from existing methods in terms of algorithmic principles. For example, methods such as adaptive ranking-based energy-efficient opportunistic routing protocol (AREOR), improved-adaptive ranking-based energy-efficient opportunistic routing protocol (I-AREOR), and adaptive ranking-based improved opportunistic routing in wireless sensor networks (ARIOR) often select routes based on relatively fixed rules or simple heuristic strategies when dealing with dynamic changes in the network. The IDRL method uses enhanced deep reinforcement learning techniques that allow agents to learn through continuous interaction with the network environment, dynamically adjust routing decisions, and better adapt to complex and changing network topology and traffic changes. From the perspective of adapting to the network environment, it is difficult for existing methods to quickly and effectively re-plan routes in the face of complex situations such as network congestion and node failures, which can easily lead to increased data transmission delays and packet loss rates. The IDRL method, with its reinforcement learning mechanism, can detect real-time changes in network state and continuously optimize routing strategies  
Figure 5: Average recovery success rate of fault link recovery algorithm. based on reward functions, thereby maintaining low latency and high transmission success rates in complex network environments. In terms of optimization objectives for decision making, AREOR, I-AREOR, and AR-IOR focus on a single metric, such as shortest path or minimum hop count. The IDRL method considers multiple factors such as bandwidth utilization, latency, packet loss rate, etc. By balancing and optimizing these metrics, it achieves more efficient routing selection and improves overall network performance. The advantages of BPDM-GCN over traditional methods such as AREOR, I-AREOR, and ARIOR are not only reflected in the statistical indicators, but also stem from a fundamental innovation in its algorithm design:  
(1) Dynamic weight optimization: AREOR-based methods rely on static link weights (such as fixed hop count or bandwidth), while BPDM-GCN learns the network state (delay, load, etc.) in real time through GCN-DDPG, generates dynamic weight matrices, and adapts to topology changes (such as link failures or congestion). (2) Global topology awareness: I-AREOR and others are based on local heuristic rules and cannot capture remote dependencies between nodes; GCN's spectral domain convolution directly models node connectivity relationships to achieve global load balancing. (3) Incremental path computation: ARIOR uses a static shortest path tree, and error recovery requires recomputing the entire path; BPDM-GCN's incremental shortest path tree (Algorithm 2) updates only the affected links, reducing computational overhead. Although the BPDM-GCN algorithm performs well in terms of fault link recovery delay and success rate, it inevitably has some drawbacks. On the one hand, this algorithm requires high computational resources. In large-scale network topologies, the fusion of GCN and DDPG may lead to complex operations, increase the computational cost, and limit its application on resource-constrained devices. On the other hand, although it performs well in various common network topologies, its performance may degrade for special network structures or extreme network environments. In addition, the effectiveness of the algorithm depends on accurate information about the status of the network. If there are data collection errors or transmission delays, it may affect the selection of backup paths and the effectiveness of fault recovery. Specifically, during the inference phase, a single-path calculation takes 3.4 ms, which meets the real-time requirement of less than 10 ms, but requires significant GPU acceleration and consumption.