# Biased Backpressure Routing Using Link Features and Graph Neural Networks

arXiv: https://arxiv.org/abs/2407.09753 · HTML: https://mlsanity.com/paper/2407.09753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 62 references that are in the map.
- First posted on arXiv 13 Jul 2024.

## Builds on (most cited first, 25 of 25)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://mlsanity.com/paper/2009.10812/) (2020)
- [RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1910.01508/) (2019)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Graph Embedding based Wireless Link Scheduling with Few Training Samples](https://mlsanity.com/paper/1906.02871/) (2019)
- [Distributed Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2011.09430/) (2020)
- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Massive Access for 5G and Beyond](https://mlsanity.com/paper/2002.03491/) (2020)
- [The Internet of Battle Things](https://mlsanity.com/paper/1712.08980/) (2017)
- [ML-aided power allocation for Tactical MIMO](https://mlsanity.com/paper/2109.06992/) (2021)
- [Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks](https://mlsanity.com/paper/2304.00446/) (2023)
- [Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks](https://mlsanity.com/paper/2201.11799/) (2022)
- [Optimal Control for Generalized Network-Flow Problems](https://mlsanity.com/paper/1611.08641/) (2016)
- [Stable and Transferable Wireless Resource Allocation Policies via Manifold Neural Networks](https://mlsanity.com/paper/2110.04706/) (2021)
- [Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks](https://mlsanity.com/paper/2203.14339/) (2022)
- [Distributed Exact Weighted All-Pairs Shortest Paths in Near-Linear Time](https://mlsanity.com/paper/1811.03337/) (2018)
- [Delay-Oriented Distributed Scheduling Using Graph Neural Networks](https://mlsanity.com/paper/2111.07017/) (2021)
- [Delay-aware Backpressure Routing Using Graph Neural Networks](https://mlsanity.com/paper/2211.10748/) (2022)
- [Learning to Transmit with Provable Guarantees in Wireless Federated Learning](https://mlsanity.com/paper/2304.09329/) (2023)
- [Learnable Digital Twin for Efficient Wireless Network Evaluation](https://mlsanity.com/paper/2306.06574/) (2023)
- [Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks](https://mlsanity.com/paper/2312.02471/) (2023)
- [Enhancing the Delay Performance of Dynamic Backpressure Algorithms](https://mlsanity.com/paper/1502.03276/) (2015)
- [Loop-Free Backpressure Routing Using Link-Reversal Algorithms](https://mlsanity.com/paper/1503.06857/) (2015)
- [Enhanced Backpressure Routing Using Wireless Link Features](https://mlsanity.com/paper/2310.04364/) (2023)

## Built on by (most cited first, 2 of 2)

- [Fully Distributed Online Training of Graph Neural Networks in Networked Systems](https://mlsanity.com/paper/2412.06105/) (2024)
- [A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking](https://mlsanity.com/paper/2512.10874/) (2025)

## Abstract (arXiv)

To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog metrics, since they introduce no additional time step-wise signaling overhead to the basic BP. Rather than relying on hop-distance, we introduce a new edge-weighted shortest path bias built on the scheduling duty cycle of wireless links, which can be predicted by a graph convolutional neural network based on the topology and traffic of wireless networks. Additionally, we tackle three long-standing challenges associated with SP-BP: optimal bias scaling, efficient bias maintenance, and integration of delay awareness. Our proposed solutions inherit the throughput optimality of the basic BP, as well as its practical advantages of low complexity and fully distributed implementation. Our approaches rely on common link features and introduces only a one-time constant overhead to previous SP-BP schemes, or a one-time overhead linear in the network size to the basic BP. Numerical experiments show that our solutions can effectively address the major drawbacks of slow startup, random walk, and the last packet problem in basic BP, improving the end-to-end delay of existing low-overhead BP algorithms under various settings of network traffic, interference, and mobility.