  This paper studies learning-based decentralized power control methods for
cell-free massive multiple-input multiple-output (MIMO) systems where a central
processor (CP) controls access points (APs) through fronthaul coordination. To
determine the transmission policy of distributed APs, it is essential to
develop a network-wide collaborative optimization mechanism. To address this
challenge, we design a cooperative learning (CL) framework which manages
computation and coordination strategies of the CP and APs using dedicated deep
neural network (DNN) modules. To build a versatile learning structure, the
proposed CL is carefully designed such that its forward pass calculations are
independent of the number of APs. To this end, we adopt a parameter reuse
concept which installs an identical DNN module at all APs. Consequently, the
proposed CL trained at a particular configuration can be readily applied to
arbitrary AP populations. Numerical results validate the advantages of the
proposed CL over conventional non-cooperative approaches.
