Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach

Raz Paul, Kobi Cohen, Gil Kedar

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the problem 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.

Original languageAmerican English
Pages (from-to)3691-3705
Number of pages15
JournalIEEE Transactions on Wireless Communications
Volume23
Issue number4
DOIs
StatePublished - 1 Apr 2024

Keywords

  • Wireless interference networks
  • deep reinforcement learning (DRL)
  • distributed learning
  • graph neural network (GNN)

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Computer Science Applications

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