Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach
This addresses routing efficiency in wireless networks, particularly for 5G and beyond, but is incremental as it builds on existing graph learning and reinforcement learning methods.
The paper tackles the problem of multi-flow transmission in wireless interference networks, where interference reduces link capacities, by introducing the DIAMOND algorithm, which uses a hybrid centralized-distributed approach with graph neural network reinforcement learning to maximize network utility and is proven to converge to the optimal strategy with superior performance in simulations across various topologies.
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.