Distributed estimation of macroscopic channel parameters in dense cooperative wireless networks

In peer-to-peer wireless networks, knowledge of the channel quality information of multiple links is fundamental to calibrate cooperative communication/processing techniques and design efficient resource sharing strategies. This paper is focused on distributed estimation algorithms that enable the network to self-learn key environment-dependent parameters that rule the channel quality of all links in the network. Considering an indoor scenario with fixed wireless terminals and moving objects/people in the environment, we parameterize the channel quality of each link in terms of path-loss and Rician K-factor, modelling these macro-parameters according to a site-specific stochastic model. Contribution of the paper is twofold: a measurement campaign carried out with IEEE 802.15.4 devices to validated the stochastic model; distributed algorithms to estimate the environment-dependent parameters of the model. Various schemes of weighted average consensus are proposed to enable the convergence to the equivalent global (centralized) estimate. Performance analysis is carried out in terms of convergence speed, error at convergence and communication overhead using both experimental and simulated data.