  Typical massive multiple-input multiple-output (MIMO) architectures consider
a centralized approach, in which all baseband data received by each antenna has
to be sent to a central processing unit (CPU) to be processed. Due to the
enormous amount of antennas expected in massive MIMO base stations (BSs), the
number of connections to the CPU required in centralized massive MIMO is not
scalable. In recent literature decentralized approaches have been proposed to
reduce the number of connections between the antennas and the CPU. However, the
reduction in the connections to the CPU requires more outputs per antenna to be
generated. We study the trade-off between number of connections to the CPU and
number of outputs per antenna. We propose a generalized architecture that
allows exploitation of this trade-off, and we define a novel matrix
decomposition that allows lossless linear equalization within our proposed
architecture.
