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Large-scale MIMO systems with a massive number N of individually controlled antennas pose significant challenges for minimum mean square error (MMSE) channel estimation, based on uplink pilots. The major ones arise from the computational complexity, which scales with $N^3$, and from the need for accurate knowledge of the channel statistics. This paper aims to address both challenges by introducing reduced-complexity channel estimation methods that achieve the performance of MMSE in terms of estimation accuracy and uplink spectral efficiency while demonstrating improved robustness in practical scenarios where channel statistics must be estimated. This is achieved by exploiting the inherent structure of the spatial correlation matrix induced by the array geometry. Specifically, we use a Kronecker decomposition for uniform planar arrays and a well-suited circulant approximation for uniform linear arrays. By doing so, a significantly lower computational complexity is achieved, scaling as $N\sqrt{N}$ and $N\log N$ for squared planar arrays and linear arrays, respectively., Comment: To appear on IEEE Transactions on Wireless Communications, 11 figures, 3 tables

Holographic MIMO refers to a (possibly large) array with a large number of individually controlled and densely deployed antennas. The objective of this paper is to provide further insight into the use of closely spaced antennas in the uplink and downlink of a multi-user Holographic MIMO system. To this end, we utilize multiport communication theory, which ensures physically consistent uplink and downlink models. We first consider a simple uplink scenario with two side-by-side half-wavelength dipoles, two users, and single-path line-of-sight propagation, and show both analytically and numerically that the array gain and average spectral efficiency strongly depend on the directions from which the signals are received and on the array matching network used. The numerical results are then used to extend the analysis to more practical scenarios involving larger arrays of dipoles (arranged in a uniform linear array) and a larger number of users. The case where the antennas are densely packed in a space-constrained factor form is also considered. It is found that the spectral efficiency increases with decreasing antenna spacing only for arrays of moderate size, e.g. in the order of a few wavelengths. In comparison, larger arrays with closely spaced antennas show only marginal improvements in spectral efficiency compared to half-wavelength arrays., Comment: 16 pages, 13 figures, submitted to IEEE Transactions on Wireless Communications

Holographic MIMO (hMIMO) systems with a massive number of individually controlled antennas N make minimum mean square error (MMSE) channel estimation particularly challenging, due to its computational complexity that scales as $N^3$ . This paper investigates uniform linear arrays and proposes a low-complexity method based on the discrete Fourier transform (DFT) approximation, which follows from replacing the covariance matrix by a suitable circulant matrix. Numerical results show that, already for arrays with moderate size (in the order of tens of wavelengths), it achieves the same performance of the optimal MMSE, but with a significant lower computational load that scales as $N \log N$. Interestingly, the proposed method provides also increased robustness in case of imperfect knowledge of the covariance matrix., Comment: 5 pages,4 figures, Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, Nov. 2023

This letter considers the wavenumber-division-multiplexing (WDM) scheme that was recently proposed in [1] for line-of-sight communications between parallel (side-by-side) spatially-continuous electromagnetic segments. Our aim is to analyze the performance of WDM, combined with different digital processing architectures, when the electromagnetic segments have an arbitrary orientation and position. To this end, we first show how the general electromagnetic MIMO (multiple-input multiple-output) model from [1] can be particularized to the case of interest and then use numerical results to evaluate the impact of geometric parameters (e.g., horizontal and vertical distances, azimuth and elevation orientations). It turns out that WDM performs satisfactorily also when the transmit and receive segments are not in boresight direction of each other., Comment: 5 pages, 7 figures, IEEE Wireless Communications Letters

Multiple antenna arrays play a key role in wireless networks for communications but also localization and sensing. The use of large antenna arrays pushes towards a propagation regime in which the wavefront is no longer plane but spherical. This allows to infer the position and orientation of an arbitrary source from the received signal without the need of using multiple anchor nodes. To understand the fundamental limits of large antenna arrays for localization, this paper fusions wave propagation theory with estimation theory, and computes the Cram{\'e}r-Rao Bound (CRB) for the estimation of the three Cartesian coordinates of the source on the basis of the electromagnetic vector field, observed over a rectangular surface area. To simplify the analysis, we assume that the source is a dipole, whose center is located on the line perpendicular to the surface center, with an orientation a priori known. Numerical and asymptotic results are given to quantify the CRBs, and to gain insights into the effect of various system parameters on the ultimate estimation accuracy. It turns out that surfaces of practical size may guarantee a centimeter-level accuracy in the mmWave bands., Comment: 5 pages, 4 figures, Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, Nov. 2021