7 accepted papers
WiFi-based human pose estimation (HPE) has attracted increasing attention due to its resilience to occlusion and privacy-preserving compared to camera-based methods. However, existing WiFi-based HPE approaches often employ regression networks that directly map WiFi channel state information (CSI) to…
In this work, we study uplink communication in cell-free (CF) massive multiple-input multiple-output (MaMIMO) systems, a promising architecture for next-generation networks. To address the challenge of pilot contamination, we employ semi-blind transmission structures that enable joint channel and da…
When derived from the Bethe Free Energy (BFE) of the Generalized Linear Model (GLM), Approximate Message Passing (AMP) algorithms combine two asymptotic Large System Limit (LSL) simplifications which are asymptotic Gaussianity of extrinsics and large random matrix theory based asymptotic variance co…
Grid-based methods in sparse signal reconstruction (SSR) are well-regarded for their efficacy in direction-of-arrival (DoA) estimation. This paper presents the EP (Expectation Propagation)-SURE (Stein's Unbiased Risk Estimate)-SBL (Sparse Bayesian Learning) algorithm, designed for single snapshot Do…
In the context of the expectation-maximization (EM) algorithm, which often faces challenges due to intractable posterior distributions, this study explores an innovative approach by integrating the EM algorithm with expectation consistent (EC) approximate inference. Our method involves the incorpora…
Many signal processing problems involve a Generalized Linear Model (GLM), which is a type of linear model where the unknowns may be non-identically independently distributed (n.i.i.d.). Vector Approximate Message Passing for Generalized Linear Models (GVAMP) is a computationally efficient belief pro…
Generalized Approximate Message Passing (GAMP) allows for Bayesian inference in linear models with non-identically independently distributed (n.i.i.d.) priors and n.i.i.d. measurements of the linear mixture outputs. It represents an efficient technique for approximate inference, which becomes accura…