Randomized and Distributed Self-Configuration of Wireless Networks: Two-Layer Markov Random Fields and Near-Optimality
This paper studies the near-optimality versus the complexity of distributed configuration management for wireless networks. The authors first develop a global probabilistic graphical model for a network configuration which characterizes jointly the statistical spatial dependence of a physical- and a logical-configuration. The global model is a Gibbs distribution that results from the internal network properties on node positions, wireless channel and interference; and the external management constraints on physical connectivity and signal quality. A local model is a two-layer Markov Random Field (i.e., a random bond model) that approximates the global model with the local spatial dependence of neighbors.