Abstract
Impulsive noise is the main limiting factor for transmission over channels affected by electromagnetic interference. We study the estimation of (correlated) Gaussian signals in an impulsive noise scenario. In this work, we analyze some of the existing, as well as some novel estimation algorithms. Their performance is compared, for the first time, for different channel conditions, including the Markov–Middleton scenario, where the impulsive noise switches between different noise states. Following a modern approach in digital communications, the receiver design is based on a factor graph model and implements a message passing algorithm. The correlation among signal samples, as well as among noise states brings about a loopy factor graph, where an iterative message passing scheme should be employed. As is well known, approximate variational inference techniques are necessary in these cases. We propose and analyze different algorithms and provide a complete performance comparison among them, showing that the expectation propagation, transparent propagation, and parallel iterative schedule approaches reach a performance close to optimal, at different channel conditions.
Highlights
The design of a robust communication system for a transmission medium with strong electromagnetic interference (EMI), such as a power line communication (PLC) channels or a wireless network of distributed sensors subject to EMI, is a challenging task
By numerical simulation, the mean-squared error (MSE) versus the average signal-to-noise ratio (SNR) in a bursty impulsive noise scenario with a maximum of M − 1 interferers, i.e., with M noise states, where the zeroth state corresponds to background noise only
We proposed different algorithms to estimate correlated Gaussian samples in a bursty impulsive noise scenario, where successive noise states are highly correlated
Summary
The design of a robust communication system for a transmission medium with strong electromagnetic interference (EMI), such as a power line communication (PLC) channels or a wireless network of distributed sensors subject to EMI, is a challenging task. We provide, for the first time, a comprehensive study of signal estimation over bursty impulsive noise channels, modeled either as Markov-Gaussian or as Markov–Middleton noise with memory, where the signal is modeled as a correlated (AR(1)) sequence of Gaussian samples, as representatives of multicarrier signals or of a Gaussian sensing source with memory. This paper is an extension of our recent work [23], where EP and TP were applied for the first time to a channel with Markov– Middleton impulsive noise Besides describing these algorithms in more detail, here, we critically review the channel model and compare it with the simpler Markov-Gaussian model. Well known, impulsive noise events occur in bursts, i.e., through a sequence of noise samples whose average power (variance) becomes suddenly larger
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