Traffic estimation in wireless networks using filtered doubly stochastic point processes

Abstract

Traffic estimation techniques play an increasingly vital role in the - accurate design of networks. This paper presents stochastic models and estimation algorithms for traffic in wireless networks. The stochastic models for traffic are based on a doubly stochastic filtered Poisson point process., For each Of these statistical models, we present optimal and compulationally efficient estimation algorithms to determine the average traffic intensity using transformed data measurements. We show that the maximum likelihood. estimator is optimal in the sense that the variance of the error is the smallest possible using any other conceivable estimate having the same bias with the same data.