mlsanityData to 7 Oct 2026

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

arXiv 2303.15544 ↗First posted 27 Mar 2023cs.LG cs.NIcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 19 of its 69 references that are in the map. First posted on arXiv 27 Mar 2023.

Builds on

19 of 69 references.

References · 19

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  3. 2018Graph Convolutional Reinforcement Learningcited by 148
  4. 2016Why Deep Neural Networks for Function Approximation?cited by 82
  5. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 80
  6. 2019PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learningcited by 46
  7. 2017Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networkscited by 37
  8. 2018Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networkscited by 34
  9. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  10. 2022Survey on Graph Neural Network Acceleration: An Algorithmic Perspectivecited by 26
  11. 2019Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rulescited by 15
  12. 2022QGNN: Value Function Factorisation with Graph Neural Networkscited by 14
7 more
  1. 2021Distributed Learning over Markovian Fading Channels for Stable Spectrum Accesscited by 13
  2. 2021Learning in Restless Bandits under Exogenous Global Markov Processcited by 9
  3. 2015Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networkscited by 8
  4. 2021Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networkscited by 8
  5. 2019Exploiting Relevance for Online Decision-Making in High-Dimensionscited by 3
  6. 2022Interference Aware Cooperative Routing for Edge Computing-enabled 5G Networkscited by 2
  7. 2022An Online Learning Approach to Shortest Path and Backpressure Routing in Wireless Networkscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 3; 2026: 1.

6 papers.

  1. 2024SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networkscited by 5
  2. 2023Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testingcited by 4
  3. 2025Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approachcited by 1
  4. 2026DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reductioncited by 1
  5. 2025Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systemscited by 0
  6. 2025Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharingcited by 0