Learning from A Single Graph is All You Need for Near-Shortest Path Routing in Wireless Networks
This addresses efficient and scalable routing in wireless networks, but it is incremental as it builds on existing methods with domain-specific enhancements.
The paper tackles the problem of learning local routing policies for near-shortest paths in wireless networks, achieving results where one trained deep neural network matches greedy forwarding performance and another generally outperforms it, with generalization demonstrated across random graphs using only a few samples from a single seed graph.
We propose a learning algorithm for local routing policies that needs only a few data samples obtained from a single graph while generalizing to all random graphs in a standard model of wireless networks. We thus solve the all-pairs near-shortest path problem by training deep neural networks (DNNs) that efficiently and scalably learn routing policies that are local, i.e., they only consider node states and the states of neighboring nodes. Remarkably, one of these DNNs we train learns a policy that exactly matches the performance of greedy forwarding; another generally outperforms greedy forwarding. Our algorithm design exploits network domain knowledge in several ways: First, in the selection of input features and, second, in the selection of a ``seed graph'' and subsamples from its shortest paths. The leverage of domain knowledge provides theoretical explainability of why the seed graph and node subsampling suffice for learning that is efficient, scalable, and generalizable. Simulation-based results on uniform random graphs with diverse sizes and densities empirically corroborate that using samples generated from a few routing paths in a modest-sized seed graph quickly learns a model that is generalizable across (almost) all random graphs in the wireless network model.