Topology Inference of Noncooperative Wireless Networks With Difference Neural Granger Causality

Noncooperative wireless networks are characterized by decentralized control, the absence of collaboration among nodes, and unpredictable environmental factors, all of which present significant challenges for network topology inference due to the limited availability of network information. To address this issue, we propose a novel topology inference methodology that leverages the time series of packet transmission times without relying on packet decoding. Our approach begins by converting signal transmission times into discrete time series data, which serves as the foundation for topology inference, transforming the connectivity problem into a causality problem. Rather than using traditional linear Granger causality (GC), we propose a novel architecture called difference neural GC (DNGC), which excels in learning network topology from sampled time series data without requiring access to the underlying protocol. By utilizing a hierarchical penalty and a differencing approach as adaptive weights, DNGC effectively captures the dynamic and nonlinear relationships between neighboring time steps in the collected sequence. Extensive simulations demonstrate that DNGC outperforms existing GC‐based methods, particularly when observation time is limited.