  This paper compares centralized and distributed methods to solve the power
minimization problem with quality-of-service (QoS) constraints in the downlink
(DL) of multi-cell Massive multiple-input multiple-output (MIMO) systems. In
particular, we study the computational complexity, number of parameters that
need to be exchanged between base stations (BSs), and the convergence of
iterative implementations. Although a distributed implementation based on dual
decomposition (which only requires statistical channel knowledge at each BS)
typically converges to the global optimum after a few iterations, many
parameters need to be exchanged to reach convergence.
