Sequential Channel State Tracking & SpatioTemporal Channel Prediction in Mobile Wireless Sensor Networks

TL;DR

This paper introduces a Bayesian nonlinear filtering framework for real-time channel state tracking and spatiotemporal channel gain prediction in mobile wireless sensor networks, using grid-based approximate filters for robustness.

Contribution

It develops a novel recursive filtering and prediction method for dynamic wireless channels, demonstrating convergence to optimal estimators and practical effectiveness through simulations.

Findings

Effective recursive channel state tracking using grid-based filters.

Accurate real-time spatiotemporal channel gain prediction.

Convergence of estimators to MMSE optimal solutions.

Abstract

We propose a nonlinear filtering framework for approaching the problems of channel state tracking and spatiotemporal channel gain prediction in mobile wireless sensor networks, in a Bayesian setting. We assume that the wireless channel constitutes an observable (by the sensors/network nodes), spatiotemporal, conditionally Gaussian stochastic process, which is statistically dependent on a set of hidden channel parameters, called the channel state. The channel state evolves in time according to a known, non stationary, nonlinear and/or non Gaussian Markov stochastic kernel. This formulation results in a partially observable system, with a temporally varying global state and spatiotemporally varying observations. Recognizing the intractability of general nonlinear state estimation, we advocate the use of grid based approximate filters as an effective and robust means for recursive tracking of the channel state. We also propose a sequential spatiotemporal predictor for tracking the channel gains at any point in time and space, providing real time sequential estimates for the respective channel gain map, for each sensor in the network. Additionally, we show that both estimators converge towards the true respective MMSE optimal estimators, in a common, relatively strong sense. Numerical simulations corroborate the practical effectiveness of the proposed approach.