Entropy and Distributed Source Coding of Connected Soft Random Geometric Graphs
Provides theoretical foundations for distributed compression of random geometric graphs, relevant to network information theory and sensor networks.
The paper establishes the Slepian-Wolf rate region for distributed compression of Soft Random Geometric Graphs above the connectivity threshold, using novel limit theorems and asymptotic equipartition properties.
We consider the distributed compression of Soft Random Geometric Graphs (SRGGs) above the connectivity threshold. We establish the Slepian-Wolf rate region for the SRGG in the setting where there are a finite number of encoders compressing sections of the graph independently. To do so, we prove novel limit theorems and asymptotic equipartition properties for the SRGG and its entropy, which allow us to use random binning techniques for distributed compression.