We consider the problem of of multi-flow transmission in wireless networks, where data signals from different flows can interfere with each other due to mutual interference between links along their routes, resulting in reduced link capacities. The objective is to develop a multi-flow transmission strategy that routes flows across the wireless interference network to maximize the network utility. However, obtaining an optimal solution is computationally expensive due to the large state and action spaces involved. To tackle this challenge, we introduce a novel algorithm called Dual-stage Interference-Aware Multi-flow Optimization of Network Data-signals (DIAMOND). The design of DIAMOND allows for a hybrid centralized-distributed implementation, which is a characteristic of 5G and beyond technologies with centralized unit deployments. A centralized stage computes the multi-flow transmission strategy using a novel design of graph neural network (GNN) reinforcement learning (RL) routing agent. Then, a distributed stage improves the performance based on a novel design of distributed learning updates. We provide a theoretical analysis of DIAMOND and prove that it converges to the optimal multi-flow transmission strategy as time increases. We also present extensive simulation results over various network topologies (random deployment, NSFNET, GEANT2), demonstrating the superior performance of DIAMOND compared to existing methods.

翻译：本文考虑无线网络中的多流传输问题，其中不同流的数据信号因其路径上链路间的相互干扰而导致链路容量降低。目标是制定一种多流传输策略，在无线干扰网络中路由各流以最大化网络效用。然而，由于涉及的状态空间和动作空间庞大，获取最优解在计算上代价高昂。为应对这一挑战，我们提出一种名为双阶段干扰感知多流网络数据信号优化（DIAMOND）的新算法。DIAMOND的设计支持混合集中-分布式实现，这是5G及未来技术中集中式单元部署的特征。集中式阶段通过一种新颖的图神经网络（GNN）强化学习（RL）路由智能体计算多流传输策略；随后，分布式阶段基于一种新颖的分布式学习更新机制提升性能。我们对DIAMOND进行理论分析，证明其随时间增加收敛至最优多流传输策略。我们还展示了在多种网络拓扑（随机部署、NSFNET、GEANT2）上的大量仿真结果，表明DIAMOND相比现有方法具有更优越的性能。