Multi-Band Covariance Interpolation with Applications in Massive MIMO
Saeid Haghighatshoar, Mahdi Barzegar Khalilsarai, and Giuseppe Caire

TL;DR
This paper addresses the challenge of frequency-dependent covariance matrix distortion in massive MIMO systems and introduces a novel interpolation method to accurately recover downlink covariance from uplink estimates, enhancing FDD system performance.
Contribution
It proposes a new UL-DL covariance interpolation technique for massive MIMO, with mathematical analysis and simple algorithms demonstrating robustness and effectiveness.
Findings
The covariance matrix distortion is significant in massive MIMO with large M.
The proposed interpolation method accurately recovers DL covariance from UL estimates.
Numerical simulations confirm the robustness and performance of the algorithms.
Abstract
In this paper, we study the problem of multi-band (frequency-variant) covariance interpolation with a particular emphasis towards massive MIMO applications. In a massive MIMO system, the communication between each BS with antennas and each single-antenna user occurs through a collection of scatterers in the environment, where the channel vector of each user at BS antennas consists in a weighted linear combination of the array responses of the scatterers, where each scatterer has its own angle of arrival (AoA) and complex channel gain. The array response at a given AoA depends on the wavelength of the incoming planar wave and is naturally frequency dependent. This results in a frequency-dependent distortion where the second order statistics, i.e., the covariance matrix, of the channel vectors varies with frequency. In this paper, we show that although this effect is generally…
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