Nuclear Atomic Norm for parametric estimation of sparse channels

Parametric channel estimation in mmWave not only enables the anticipated large spectral efficiency gains of \acs{MIMO} systems but also reveals important propagation parameters, allowing for a low complexity representation of the channel matrix. In this work, we propose to use atomic norm as a gridless multidimensional spectral estimation approach to address parametric channel estimation where both AoD and AoA are identified. The conditions for recovery of the propagation parameters are given depending on properties of the measurement matrix, and on structural features such as the antenna geometry or the number of scatters to resolve. The proposed methodology is compared against several state-of-the-art parametric approaches.

Estimation of Interference Correlation in mmWave Cellular Systems

We consider a cellular network, where the uplink transmissions to a base station (BS) are interferenced by other devices, a condition that may occur, e.g., in cell-free networks or when using non-orthogonal multiple access (NOMA) techniques. Assuming that the BS treats this interference as additional noise, we focus on the problem of estimating the interference correlation matrix from received signal samples. We consider a BS equipped with multiple antennas and operating in the millimeter-wave (mmWave) bands and propose techniques exploiting the fact that channels comprise only a few reflections at these frequencies. This yields a specific structure of the interference correlation matrix that can be decomposed into three matrices, two rectangular depending on the angle of arrival (AoA) of the interference and the third square with smaller dimensions. We resort to gridless approaches to estimate the AoAs and then project the least square estimate of the interference correlation matrix into a subspace with a smaller dimension, thus reducing the estimation error. Moreover, we derive two simplified estimators, still based on the gridless angle estimation that turns out to be convenient when estimating the interference over a larger number of samples.