9 accepted papers
Integrating deep neural networks (DNNs) into wireless receivers can enhance reliability in the presence of hard-to-model channels. However, in order to successfully deploy deep receivers, one must address the rapid channel variations while accounting for the limited availability of data and computin…
In this paper, we consider the problem of estimating the admittance matrix in power systems, accounting for Laplacian and physical constraints. We assume the nonlinear alternating current (AC) model, which accurately represents the power flow model. We develop a closed-form expression for the oracle…
In many practical parameter estimation problems, the observation model is periodic with respect to the unknown parameters. In these cases, the appropriate estimation criterion is periodic in the parameter space, and cyclic performance bounds should be used. However, existing cyclic performance bound…
In this paper, we address the problem of tracking dynamic changes in graph topology under a linear graph filtering random process. We propose a graph-based state-space model (SSM), where the measurements are graph signals and the underlying evolving topology serves as the state variable. The propose…
THz communications are expected to play a profound role in future wireless systems. The current trend of the extremely massive multiple-input multiple-output (MIMO) antenna architectures tends to be costly and power inefficient when implementing wideband THz communications. An emerging THz antenna t…
We consider the problem of recovering random, time-varying graph processes in a nonlinear dynamic system. The Extended Kalman filter (EKF) is a suitable estimator for such dynamics, but its implementation tends to be complex and possibly unstable when tracking high-dimensional graph signals. To tack…
The Kalman filter (KF) is a widely-used algorithm for tracking dynamical systems that can be faithfully captured by state space (SS) models. The need to fully describe an SS model limits its applicability under complex settings, e.g., when tracking based on visual or graphical data. This challenge c…
Admittance matrix estimation in power networks enables faster control actions following emergency scenarios, energy-saving, and other economic and security advantages. In this paper, our goal is to estimate the network admittance matrix, i.e. to learn connectivity and edge weights in the graph repre…
This paper deals with non-Bayesian parameter estimation under the mean-squared-error (MSE), which is a topic of great interest in various engineering fields. Although the unbiasedness condition is commonly used in non-Bayesian MSE estimation, in many cases biased estimation may result in better perf…