  Cell-free massive multiple-input multiple-output (MIMO) is a promising
cellular network. In this network, a large number of distributed and
multi-antenna access points (APs) jointly serve many single antenna users using
the same time-frequency resource. Consequently, it possibly provides a uniform
service experience to users regardless of the users' locations by eliminating
interference at cell boundaries via user-centric joint transmission. This joint
transmission, however, requires extremely high signaling overheads for data
sharing via backhaul links and causes a high network-wide power consumption. To
resolve these problems, in this paper, we present a novel joint transmission
method, which is referred to as sparse joint transmission (sparse-JT), for
cell-free massive MIMO networks with finite backhaul capacity constraints.
Sparse-JT jointly identifies the user-centric cooperative APs sets, precoding
vectors for beamforming and compression, and power allocation that maximizes a
lower bound of the sum-spectral efficiency under the constraint that a total
number of active APs per the joint transmission is sparse. The proposed
algorithm guarantees to identify a local-optimal solution for a relaxed
sum-spectral maximization problem. By simulations, we show that sparse-JT
achieves higher ergodic spectral efficiencies than those attained by multi-cell
zero-forcing precoding with the user-centric AP clustering algorithm in all
system configurations.
