A novel sparse channel state information (CSI) estimation scheme is proposed for orthogonal time frequency space (OTFS) modulated systems, in which the pilots are directly transmitted over time-frequency (TF)-domain grid estimating delay-Doppler (DD)-domain CSI. The CSI model leads to a reduction pilot overhead as well training duration required. Furthermore, it does not require DD-domain guard...

We consider channel estimation within pulseshaping multicarrier multiple-input multiple-output (MIMO) systems transmitting over doubly selective MIMO channels. This setup includes MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems as a special case. We show that the component channels tend to exhibit an approximate joint group sparsity structure in the delay-Doppler domain. We ...

Sparse channel estimation problem is one of challenge technical issues in stable broadband wireless communications. Based on square error criterion (SEC), adaptive sparse channel estimation (ASCE) methods, e.g., zero-attracting least mean square error (ZA-LMS) algorithm and reweighted ZA-LMS (RZA-LMS) algorithm, have been proposed to mitigate noise interferences as well as to exploit the inhere...

A new framework is proposed for deriving adaptive algorithms for sparse channel estimation under the presence of Symmetric α-Stable (SαS) noise. The algorithmic framework employs the natural gradient and incorporates both the Lp norm of the channel prediction error and the L0 norm of the complex-valued channel taps. Based on this framework, a novel affine projection sign algorithm is derived an...

In this work, we address the problem of estimating sparse communication channels in OFDM systems in the presence of carrier frequency offset (CFO) and unknown noise variance. To this end, we consider a convex optimization problem, including a probability function, accounting for the sparse nature of the communication channel. We use the Expectation-Maximization (EM) algorithm to solve the corre...