  To exploit the benefits of massive multiple-input multiple-output (M-MIMO)
technology in scenarios where base stations (BSs) need to be cheap and equipped
with simple hardware, the computational complexity of classical signal
processing schemes for spatial multiplexing of users shall be reduced. This
calls for suboptimal designs that perform well the combining/precoding steps
and simultaneously achieve low computational complexities. An approach based on
the iterative Kaczmarz algorithm (KA) has been recently investigated, assuring
well execution without the knowledge of second order moments of the wireless
channels in the BS, and with easiness, since no tuning parameters, besides the
number of iterations, are required. In fact, the randomized version of KA (rKA)
has been used in this context due to global convergence properties. Herein,
modifications are proposed on this first rKA-based attempt, aiming to improve
its performance-complexity trade-off solution for M-MIMO systems. We observe
that long-term channel effects degrade the rate of convergence of the rKA-based
schemes. This issue is then tackled herein by means of a hybrid rKA
initialization proposal that lands within the region of convexity of the
algorithm and assures fairness to the communication system. The effectiveness
of our proposal is illustrated through numerical results which bring more
realistic system conditions in terms of channel estimation and spatial
correlation than those used so far. We also characterize the computational
complexity of the proposed rKA scheme, deriving upper bounds for the number of
iterations. A case study focused on a dense urban application scenario is used
to gather new insights on the feasibility of the proposed scheme to cope with
the inserted BS constraints.
