  Cell-Free Massive MIMO is a highly promising approach to enhance network
capacity by moving a large number of distributed access points (AP) closer to
mobile users while utilizing simple matched filtering and conjugate
beamforming. Recent work using minimum mean-squared-error (MMSE) receiver that
suppress multi-user interference (MUI) shows significant capacity increase, but
at the cost of high computational complexity and residual MUI enhancement. We
propose a significantly lower complexity adaptive approach where central
processing unit (CPU) removes MUI without amplifying the residual interference.
It does so dynamically by using available knowledge of channel estimates to
perform joint process of combining selected strongest AP signals for each user
and subtracting the sum of interference estimates from other users at the same
time. We provide signalto-interference plus noise-ratio (SINR) and complexity
analyses backed by numerical results to show the superiority of this approach
compared with the state-of-the-art techniques.
