Localization Efficiency in Massive MIMO Systems

In the next generation of wireless systems, Massive MIMO offers high angular resolution for localization. By virtue of large number of antennas, users' angle of arrival can be estimated with high accuracy. As Massive MIMO antenna array can be very large, the channels seen by different antennas might differ from each other, however, this does not rule out the possibility of the Angle of Arrival (AoA) estimation. We show that Cramer-Rao Lower Bound (CRLB) in multi-user independent, identically distributed (i.i.d) channels does exist and regardless of channel distribution, it converges toward a closed-form expression. Then, we redefine a localization efficiency function for a multi-user scenario and numerically optimize it with respect to the number of antennas. We prove when only a subset of the available antennas is used, CRLB can be minimized with respect to which set of antennas is used. 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.