Learning Wireless Networks' Topologies Using Asymmetric Granger Causality

TL;DR

This paper introduces a novel non-parametric Granger causality-based method to infer wireless network topologies by analyzing transmission timings, effectively identifying directed links and response behaviors in both infrastructure and ad hoc networks.

Contribution

It proposes a new approach using asymmetric Granger causality and a non-parametric test to detect directed links and response times, improving topology inference accuracy.

Findings

Outperforms existing methods in infrastructure network topology learning.

Accurately infers directed data flow in ad hoc networks with finer resolution.

Effective differentiation between response behavior and opportunistic spectrum access.

Abstract

Sharing spectrum with a communicating incumbent user (IU) network requires avoiding interference to IU receivers. But since receivers are passive when in the receive mode and cannot be detected, the network topology can be used to predict the potential receivers of a currently active transmitter. For this purpose, this paper proposes a method to detect the directed links between IUs of time multiplexing communication networks from their transmission start and end times. It models the response mechanism of commonly used communication protocols using Granger causality: the probability of an IU starting a transmission after another IU's transmission ends increases if the former is a receiver of the latter. This paper proposes a non-parametric test statistic for detecting such behavior. To help differentiate between a response and the opportunistic access of available spectrum, the same test statistic is used to estimate the response time of each link. The causal structure of the response is studied through a discrete time Markov chain that abstracts the IUs' medium access protocol and focuses on the response time and response probability of 2 IUs. Through NS-3 simulations, it is shown that the proposed algorithm outperforms existing methods in accurately learning the topologies of infrastructure-based networks and that it can infer the directed data flow in ad hoc networks with finer time resolution than an existing method.