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Deep Learning for the Degraded Broadcast Channel

Machine learning has shown promising results for communications system problems. We present results for using deep auto-encoders for the multiuser degraded broadcast channel. The auto encoder is able to learn to communicate on this channel using superposition coding. Additionally, the deep neural net is also able to determine a bit labeling and optimize the per user power allocation that depends on the per user SNR.