  Massive multiple-input multiple-output (MIMO) is expected to be a vital
component in future 5G systems. As such, there is a need for new modeling in
order to investigate the performance of massive MIMO not only at the physical
layer, but also higher up the networking stack. In this paper, we present
general optimization models for massive MIMO, based on mixed-integer
programming and compatible sets, with both maximum ratio combing and zero
forcing precoding schemes. We then apply our models to the case of joint device
scheduling and power control for heterogeneous devices and traffic demands, in
contrast to existing power control schemes that consider only homogeneous users
and saturated scenarios. Our results show substantial benefits in terms of
energy usage can be achieved without sacrificing throughput, and that both
signalling overhead and the complexity of end devices can be reduced by
abrogating the need for uplink power control through efficient scheduling.
