  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.
