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.

翻译：作为新兴的人工智能技术，图神经网络（GNN）在各类图相关应用中展现出了优越性能。然而，在资源受限场景（尤其是无线系统）中，GNN邻居节点间的信息交互带来了新的挑战。在实际无线系统中，由于无线衰落和接收机噪声，节点间的通信链路通常不可靠，进而导致GNN学习性能下降。为提升GNN的学习性能，我们旨在通过优化功率控制，在能耗约束下最大化长期平均（LTA）通信链路数量。利用李雅普诺夫优化方法，首先通过将长期能量约束转化为目标函数，将难以处理的长期问题转化为每个时隙的确定性问题。尽管这是一个非凸组合优化问题，我们通过等价求解一系列凸可行性问题并结合贪心求解器来处理该问题。仿真结果表明，所提方案相较于基线方法具有优越性。