  Massive MIMO is a variant of multiuser MIMO, where the number of antennas $M$
at the base-station is large, and generally much larger than the number of
spatially multiplexed data streams to/from the users. It has been observed that
in many realistic propagation scenarios as well as in spatially correlated
channel models used in standardizations, although the user channel vectors have
a very high-dim $M$, they lie on low-dim subspaces due to their limited angular
spread. This low-dim subspace structure remains stable across many coherence
blocks and can be exploited in several ways to improve the system performance.
A main challenge, however, is to estimate this signal subspace from samples of
users' channel vectors as fast and efficiently as possible. In a recent work,
we addressed this problem and proposed a very effective novel algorithm
referred to as Approximate Maximum-Likelihood (AML), which was formulated as a
semi-definite program (SDP). In this paper, we address two problems left open
in our previous work: computational complexity and tracking. The algorithm
proposed in this paper is reminiscent of Multiple Measurement Vectors (MMV)
problem in Compressed Sensing and is proved to be equivalent to the AML
Algorithm for sufficiently dense angular grids. It has also a very low
computational complexity and is able to track sharp transitions in the channel
statistics very quickly. Although mainly motivated by massive MIMO
applications, our proposed algorithm is of independent interest in other
related subspace estimation applications. We assess the estimation/tracking
performance of our proposed algorithm empirically via numerical simulations,
especially in practically relevant situations where a direct implementation of
the SDP would be infeasible in real-time. We also compare the performance of
our algorithm with other related subspace estimation algorithms in the
literature.
