EM-Based Channel Estimation from Crowd-Sourced RSSI Samples Corrupted by Noise and Interference

摘要

We propose a method for estimating channel parameters from RSSI measurementsand the lost packet count, which can work in the presence of losses due to bothinterference and signal attenuation below the noise floor. This is especiallyimportant in the wireless networks, such as vehicular, where propagation modelchanges with the density of nodes. The method is based on StochasticExpectation Maximization, where the received data is modeled as a mixture ofdistributions (no/low interference and strong interference), incomplete(censored) due to packet losses. The PDFs in the mixture are Gamma, accordingto the commonly accepted model for wireless signal and interference power. Thisapproach leverages the loss count as additional information, henceoutperforming maximum likelihood estimation, which does not use thisinformation (ML-), for a small number of received RSSI samples. Hence, itallows inexpensive on-line channel estimation from ad-hoc collected data. Themethod also outperforms ML- on uncensored data mixtures, as ML- assumes thatsamples are from a single-mode PDF.