Baseband processing algorithms often require knowledge of the noise power, signal power,
or signal-to-noise ratio (SNR). In practice, these parameters are typically unknown and must …

We propose blind estimators for the average noise power, receive signal power, signal-to-
noise ratio (SNR), and mean-square error (MSE), suitable for multi-antenna millimeter wave …

Theoretical studies on nondata aided (blind) parameter estimation algorithms for signals
have been carried out over the past decades. However, most of them have not been …

In this paper, we derive the Cramér–Rao lower bounds (CRLB) for direction of arrival (DoA)
estimation by using sparse Bayesian learning (SBL) and the Laplace prior. CRLB is a lower …

We study the maximum likelihood problem for the blind estimation of massive mmWave
MIMO channels while taking into account their underlying sparse structure, the temporal …

We present herein a novel blind channel estimator for multicarrier (MC) systems in the
presence of unmodeled narrowband interference. A generalized multichannel minimum …

We present a blind estimation algorithm for multi-input and multi-output (MIMO) systems with
sparse common support. Key to the proposed algorithm is a matrix generalization of the …

In many estimation problems of interest, the unknown parameters reside on spherical
manifolds. As most common filtering algorithms assume that parameters have Gaussian …

Existing methods for sparse channel estimation typically provide an estimate computed as
the solution maximizing an objective function defined as the sum of the log-likelihood …

We derive low complexity versions of a wide range of algorithms for sparse Bayesian
learning (SBL) in underdetermined linear systems. The proposed algorithms are obtained by …