Link Gain Matrix Estimation in Distributed Large-Scale Wireless Networks

In planning and using large-scale distributed wireless networks, knowledge of the link gain matrix can be highly valuable. If the number N of radio nodes is large, measuring N(N - 1)/2 node-to-node link gains can be prohibitive. This motivates us to devise a methodology that measures a fraction of the links and accurately estimates the rest. Our method partitions the set of transmit-receive links into mutually exclusive categories, based on the number of obstructions or walls on the path; then it derives a separate link gain model for each category. The model is derived using gain measurements on only a small fraction of the links, selected on the basis of a maximum entropy. To evaluate the new method, we use ray-tracing to compute the "true" path gains for all links in the network. We use knowledge of a subset of those gains to derive the models and then use those models to predict the remaining path gains. We do this for three different environments of distributed nodes, including an office building with many obstructing walls. We find in all cases that the partitioning method yields acceptably low path gain estimation errors with a significantly reduced number of measurements.