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In this paper, we consider a system with K single-antenna client users, nB base stations (each base station has nB antennas) as well as a centralized controller. All the base stations operate at the same frequency and have optimal multi-user detection per base-station. A client user could be associated with a single base station at any time. We consider a general problem of uplink macroscopic scheduling where the centralized controller dynamically determines an appropriate association mapping of the K users with respect to the nB base stations over a macroscopic time scale. We propose a novel analytical framework for the above macroscopic scheduling problems. A simple rule is to associate a user with the strongest base station (camp-on-the-strongest-cell) and this has been widely employed in conventional cellular systems. However, based on the optimization framework, we found that this conventional approach is in fact not optimal when multi-user detection is employed at the base station. We show that the optimal macroscopic scheduling algorithm is of exponential complexity and we propose a simple greedy algorithm as a feasible solution. It is shown that the macroscopic scheduling gain relative to the conventional approach increases with increasing nB (due to the extra degree of freedom introduced by multiple antennas) and decreasing path loss exponent (due to large area of overlapping).