  In the next generation of wireless systems, Massive MIMO offers high angular
resolution for localization. By virtue of large number of antennas, the Angle
of Arrival (AoA) of User Terminals (UTs) can be estimated with high accuracy.
According to Dense Multipath Component (DMC) channel model, local scatters
around UTs can create different multipath signals for each antenna at the Base
Station (BS). We obtain a deterministic form for the Cramer-Rao Lower Bound
(CRLB) in a multi-user scenario when the contribution of the multipath signals
is considered. We do this when the multipath signals are independent and
identically distributed (i.i.d) with arbitrary distribution. Then, we redefine
a localization efficiency function for a multi-user scenario and numerically
optimize it with respect to (w.r.t) the number of antennas. We prove when only
a subset of the available antennas is used, CRLB can be minimized w.r.t which
set of antennas is used. Then, an antenna selection strategy that minimizes
CRLB is proposed. As a benchmark, we apply the proposed antenna selection
scheme to the MUltiple SIgnal Classification (MUSIC) algorithm and study its
efficiency. Numerical results validate the accuracy of our analysis and show
significant improvement in efficiency when the proposed antenna selection
strategy is employed.
