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

arXiv: https://arxiv.org/abs/2303.15544 · HTML: https://mlsanity.com/paper/2303.15544/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 69 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Why Deep Neural Networks for Function Approximation?](https://mlsanity.com/paper/1610.04161/) (2016)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1911.00025/) (2019)
- [Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://mlsanity.com/paper/1712.00162/) (2017)
- [Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://mlsanity.com/paper/1802.06958/) (2018)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Survey on Graph Neural Network Acceleration: An Algorithmic Perspective](https://mlsanity.com/paper/2202.04822/) (2022)
- [Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules](https://mlsanity.com/paper/1906.08120/) (2019)
- [QGNN: Value Function Factorisation with Graph Neural Networks](https://mlsanity.com/paper/2205.13005/) (2022)
- [Distributed Learning over Markovian Fading Channels for Stable Spectrum Access](https://mlsanity.com/paper/2101.11292/) (2021)
- [Learning in Restless Bandits under Exogenous Global Markov Process](https://mlsanity.com/paper/2112.09484/) (2021)
- [Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks](https://mlsanity.com/paper/1507.05664/) (2015)
- [Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks](https://mlsanity.com/paper/2110.14541/) (2021)
- [Exploiting Relevance for Online Decision-Making in High-Dimensions](https://mlsanity.com/paper/1907.00783/) (2019)
- [Interference Aware Cooperative Routing for Edge Computing-enabled 5G Networks](https://mlsanity.com/paper/2201.01520/) (2022)
- [An Online Learning Approach to Shortest Path and Backpressure Routing in Wireless Networks](https://mlsanity.com/paper/2204.03620/) (2022)

## Built on by (most cited first, 6 of 6)

- [SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks](https://mlsanity.com/paper/2402.17773/) (2024)
- [Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing](https://mlsanity.com/paper/2309.08477/) (2023)
- [Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach](https://mlsanity.com/paper/2505.12902/) (2025)
- [DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction](https://mlsanity.com/paper/2603.23544/) (2026)
- [Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems](https://mlsanity.com/paper/2507.00268/) (2025)
- [Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharing](https://mlsanity.com/paper/2512.17161/) (2025)

## Abstract (arXiv)

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.