Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
This work addresses performance degradation for GNNs in resource-constrained wireless systems, representing an incremental improvement.
The paper tackles the problem of unreliable communication links degrading Graph Neural Network (GNN) performance in wireless networks by optimizing power control to maximize long-term average stable links under energy constraints, with simulation results showing superiority over baselines.
As an emerging artificial intelligence technology, graph neural networks (GNNs) have exhibited promising performance across a wide range of graph-related applications. However, information exchanges among neighbor nodes in GNN pose new challenges in the resource-constrained scenario, especially in wireless systems. In practical wireless systems, the communication links among nodes are usually unreliable due to wireless fading and receiver noise, consequently resulting in performance degradation of GNNs. To improve the learning performance of GNNs, we aim to maximize the number of long-term average (LTA) communication links by the optimized power control under energy consumption constraints. Using the Lyapunov optimization method, we first transform the intractable long-term problem into a deterministic problem in each time slot by converting the long-term energy constraints into the objective function. In spite of this non-convex combinatorial optimization problem, we address this problem via equivalently solving a sequence of convex feasibility problems together with a greedy based solver. Simulation results demonstrate the superiority of our proposed scheme over the baselines.