Biased Backpressure Routing using Link Features and Graph Neural Networks
Published in IEEE TMLCN, 2024
Recommended citation: Zhongyuan Zhao, Bojan Radojičić, Gunjan Verma, Ananthram Swami, Santiago Segarra, " Distributed Link Sparsification for Scalable Scheduling using Graph Neural Networks," IEEE Transactions on Machine Learning in Communications and Networking, accepted
Related conference papers and code
- Delay-aware Backpressure Routing using Graph Neural Networks
- Enhanced Backpressure Routing with Wireless Link Features
- Preprint available at https://arxiv.org/abs/2407.09753
- Source code available at https://github.com/zhongyuanzhao/dutyBP
- IEEE Early Access https://ieeexplore.ieee.org/document/10681132
Abstract
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.
Key words: Backpressure, MaxWeight scheduling, shortest path, queueing networks, last packet problem, graph neural networks.
History
- 2024-09-04: Accepted for publication in IEEE TMLCN
- 2024-04-09: submitted to IEEE Transactions on Machine Learning in Communications and Networking
- 2024-04-04: EiC think it does not fit to the scope of IEEE TWC
- 2024-03-20: submitted to IEEE Transactions on Wireless Communications