  The recently introduced atomic norm minimization (ANM) framework for
parameter estimation is a promising candidate towards low overhead channel
estimation in wireless communications. However, previous works on ANM-based
channel estimation evaluated performance on channels with artificially imposed
channel path separability, which cannot be guaranteed in practice. In addition,
direct application of the ANM framework for massive MIMO channel estimation is
computationally infeasible due to the large dimensions. In this paper, a
low-complexity ANM-based channel estimator for wideband massive MIMO is
proposed, consisting of two sequential steps, the first estimating the channel
over the spatial and the second over the frequency dimension. Its mean squared
error performance is analytically characterized in terms of tight lower bounds.
It is shown that the proposed algorithm achieves excellent performance that is
close to the best that can be achieved by any unbiased channel estimator in the
regime of low to moderate number of channel paths, without any restrictions on
their separability.
