1 research outputs found
EM-Based Channel Estimation from Crowd-Sourced RSSI Samples Corrupted by Noise and Interference
We propose a method for estimating channel parameters from RSSI measurements
and the lost packet count, which can work in the presence of losses due to both
interference and signal attenuation below the noise floor. This is especially
important in the wireless networks, such as vehicular, where propagation model
changes with the density of nodes. The method is based on Stochastic
Expectation Maximization, where the received data is modeled as a mixture of
distributions (no/low interference and strong interference), incomplete
(censored) due to packet losses. The PDFs in the mixture are Gamma, according
to the commonly accepted model for wireless signal and interference power. This
approach leverages the loss count as additional information, hence
outperforming maximum likelihood estimation, which does not use this
information (ML-), for a small number of received RSSI samples. Hence, it
allows inexpensive on-line channel estimation from ad-hoc collected data. The
method also outperforms ML- on uncensored data mixtures, as ML- assumes that
samples are from a single-mode PDF.Comment: CISS 201