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

TL;DR

This paper introduces an EM-based method for estimating wireless channel parameters from RSSI data, effectively handling packet losses caused by interference and noise, and outperforming traditional maximum likelihood approaches especially with limited data.

Contribution

The paper presents a novel EM algorithm that utilizes packet loss counts to improve channel estimation accuracy in noisy, interference-prone wireless environments.

Findings

Outperforms maximum likelihood estimation in small sample scenarios.

Effective in both censored and uncensored data conditions.

Enables inexpensive online channel estimation from ad-hoc data.

Abstract

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.