5 accepted papers
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…
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…
Future wireless services, such as the metaverse require high information rate, reliability, and low latency. Multi-user wireless systems can meet such requirements by utilizing the abundant terahertz bandwidth with a massive number of antennas, creating narrow beamforming solutions. However, existin…
In many applications such as massive multi-input multi-output (MIMO) radar, massive MIMO channel estimation, speech processing, image and video processing, the received signals are tensors. In such applications, utilizing techniques from tensor algebra can be beneficial since it retains the tensoria…
In this paper, we address the fundamental problem of Sparse Bayesian Learning (SBL), where the received signal is a high-order tensor. We furthermore consider the problem of dictionary learning (DL), where the tensor observations are assumed to be generated from a Kronecker structured (KS) dictionar…