# Learning Wireless Interference Patterns: Decoupled GNN for Throughput Prediction in Heterogeneous Multi-Hop p-CSMA Networks

arXiv: https://arxiv.org/abs/2510.14137 · HTML: https://mlsanity.com/paper/2510.14137/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation](https://mlsanity.com/paper/2404.11858/) (2024)
- [ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks](https://mlsanity.com/paper/2301.00757/) (2022)
- [Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach](https://mlsanity.com/paper/2301.01801/) (2023)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks](https://mlsanity.com/paper/1910.05221/) (2019)
- [Online Network Utility Maximization: Algorithm, Competitive Analysis, and Applications](https://mlsanity.com/paper/2101.10898/) (2021)
- [Computing the Saturation Throughput for Heterogeneous p-CSMA in a General Wireless Network](https://mlsanity.com/paper/2503.09869/) (2025)

## Built on by (most cited first, 1 of 1)

- [Evolving Inspectable O-RAN Slicing xApps with LLMs](https://mlsanity.com/paper/2609.27337/) (2026)

## Abstract (arXiv)

The p-persistent CSMA protocol is central to random-access MAC analysis, but predicting saturation throughput in heterogeneous multi-hop wireless networks remains a hard problem. Simplified models that assume a single, shared interference domain can underestimate throughput by 48-62% in sparse topologies. Exact Markov-chain analyses are accurate but scale exponentially in computation time, making them impractical for large networks. These computational barriers motivate structural machine learning approaches like GNNs for scalable throughput prediction in general network topologies. Yet off-the-shelf GNNs struggle here: a standard GCN yields 63.94% normalized mean absolute error (NMAE) on heterogeneous networks because symmetric normalization conflates a node's direct interference with higher-order, cascading effects that pertain to how interference propagates over the network graph. Building on these insights, we propose the Decoupled Graph Convolutional Network (D-GCN), a novel architecture that explicitly separates processing of a node's own transmission probability from neighbor interference effects. D-GCN replaces mean aggregation with learnable attention, yielding interpretable, per-neighbor contribution weights while capturing complex multihop interference patterns. D-GCN attains 3.3% NMAE, outperforms strong baselines, remains tractable even when exact analytical methods become computationally infeasible, and enables gradient-based network optimization that achieves within 1% of theoretical optima.