arXiv subjects

S. A. Vorobyov

Publications and source records attributed to S. A. Vorobyov.

3 recordsLinked to original sources

arXiv2019

On the Spectral Efficiency for Massive MIMO Systems With Imperfect Spacial Covariance Information

This paper studies the impact of imperfect channel covariance information on the uplink (UL) and downlink (DL) spectral efficiencies (SEs) of a time-division duplexed (TDD) massive multiple-input multiple-output (MIMO) system. We derive closed-form expressions for the UL and DL average SEs by considering linear minimum mean squared (LMMSE)-type and element-wise LMMSE-type channel estimation that represent LMMSE and element-wise LMMSE with estimated covariance matrices, respectively. The closed-form expressions of these average SEs are functions of the number of observations used for estimating the spatial covariance matrices of individual and contaminated channels of a target user, and thus enable us to select these key parameters to achieve the desired SE. We present a theoretical analysis of SE behavior for different values of these parameters, followed by simulations, which also demonstrate and validate this behavior. Specifically, we present the SEs computed using estimated covariance matrices and show the accurate agreement between the theoretical and simulated SEs as functions of the number of observations for estimating the covariance matrices of individual and contaminated channels of a user. We also compare these SEs across channel estimation techniques using analytical and simulation studies.

cs.IT↗

arXiv2019

A Fast Dictionary Learning Method for Coupled Feature Space Learning

In this letter, we propose a novel computationally efficient coupled dictionary learning method that enforces pairwise correlation between the atoms of dictionaries learned to represent the underlying feature spaces of two different representations of the same signals, e.g., representations in different modalities or representations of the same signals measured with different qualities. The jointly learned correlated feature spaces represented by coupled dictionaries are used in sparse representation based classification, recognition and reconstruction tasks. The presented experimental results show that the proposed coupled dictionary learning method has a significantly lower computational cost. Moreover, the visual presentation of jointly learned dictionaries shows that the pairwise correlations between the corresponding atoms are ensured.

cs.LG↗

arXiv2019

Non-Iterative Subspace-Based DOA Estimation in the Presence of Nonuniform Noise

The uniform white noise assumption is one of the basic assumptions in most of the existing directional-of-arrival (DOA) estimation methods. In many applications, however, the non-uniform white noise model is more adequate. Then the noise variances at different sensors have to be also estimated as nuisance parameters while estimating DOAs. In this letter, different from the existing iterative methods that address the problem of non-uniform noise, a non-iterative two-phase subspace-based DOA estimation method is proposed. The first phase of the method is based on estimating the noise subspace via eigendecomposition (ED) of some properly designed matrix and it avoids estimating the noise covariance matrix. In the second phase, the results achieved in the first phase are used to estimate the noise covariance matrix, followed by estimating the noise subspace via generalized ED. Since the proposed method estimates DOAs in a non-iterative manner, it is computationally more efficient and has no convergence issues as compared to the existing methods. Simulation results demonstrate better performance of the proposed method as compared to other existing state-of-the-art methods.

cs.IT↗