  Cell-free massive multiple-input multiple-output (CF mMIMO) systems are
characterized by having many more access points (APs) than user equipments
(UEs). A key challenge is to determine which APs should serve which UEs.
Previous work has tackled this combinatorial problem heuristically. This paper
proposes a sparse large-scale fading decoding (LSFD) design for CF mMIMO to
jointly optimize the association and LSFD. We formulate a group sparsity
problem and then solve it using a proximal algorithm with block-coordinate
descent. Numerical results show that sparse LSFD achieves almost the same
spectral efficiency as optimal LSFD, thus achieving a higher energy efficiency
since the processing and signaling are reduced.
