  This paper investigates a multiuser scheduling problem within an uplink
multiple-input multi-output (MIMO) status update network, consisting of a
multi-antenna base station (BS) and multiple single-antenna devices. The
presence of multiple antennas at the BS introduces spatial degrees-of-freedom,
enabling concurrent transmission of status updates from multiple devices in
each time slot. Our objective is to optimize network-wide information
freshness, quantified by the age of information (AoI) metric, by determining
how the BS can best schedule device transmissions, while taking into account
the random arrival of status updates at the device side.To address this
decision-making problem, we model it as a partially observable Markov decision
process (POMDP) and establish that the evolution of belief states for different
devices is independent.We also prove that feasible belief states can be
described by finite-dimensional vectors. Building on these observations, we
develop a dynamic scheduling (DS) policy to solve the POMDP, and then derive an
upper bound of its AoI performance, which is used to optimize the parameter
configuration. To gain more design insights, we investigate a symmetric
network, and put forth a fixed scheduling (FS) policy with lower computational
complexity. An action space reduction strategy is applied to further reduce the
computational complexity of both DS and FS policies. Our numerical results
validate our analyses and indicate that the DS policy with the reduced action
space performs almost identically to the original DS policy, and both
outperform the baseline policies.
