  In this paper, we study the problem of decentralized scheduling in
Interference Channels (IC). In this setting, each Transmitter (TX) receives an
arbitrary amount of feedback regarding the global multi-user channel state
based on which it decides whether to transmit or to stay silent without any
form of communication with the other TXs. While many methods have been proposed
to tackle the problem of link scheduling in the presence of reliable Channel
State Information (CSI), finding the optimally robust transmission strategy in
the presence of arbitrary channel uncertainties at each TX has remained elusive
for the past years. In this work, we recast the link scheduling problem as a
decentralized classification problem and we propose the use of Collaborative
Deep Neural Networks (C-DNNs) to solve this problem. After adequate training,
the scheduling obtained using the C-DNNs flexibly adapts to the decentralized
CSI configuration to outperform other scheduling algorithms.
