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Cell-Free Massive MIMO is a highly promising approach to enhance networkcapacity by moving a large number of distributed access points (AP) closer tomobile users while utilizing simple matched filtering and conjugatebeamforming. Recent work using minimum mean-squared-error (MMSE) receiver thatsuppress multi-user interference (MUI) shows significant capacity increase, butat the cost of high computational complexity and residual MUI enhancement. Wepropose a significantly lower complexity adaptive approach where centralprocessing unit (CPU) removes MUI without amplifying the residual interference.It does so dynamically by using available knowledge of channel estimates toperform joint process of combining selected strongest AP signals for each userand subtracting the sum of interference estimates from other users at the sametime. We provide signalto-interference plus noise-ratio (SINR) and complexityanalyses backed by numerical results to show the superiority of this approachcompared with the state-of-the-art techniques.

Joint Access Point Selection and Interference Cancellation for Cell-Free Massive MIMO