  In this paper, we investigate a new machine learning-based transmission
strategy called progressive transmission or ProgTr. In ProgTr, there are b
variables that should be transmitted using at most T channel uses. The
transmitter aims to send the data to the receiver as fast as possible and with
as few channel uses as possible (as channel conditions permit) while the
receiver refines its estimate after each channel use. We use recurrent neural
networks as the building block of both the transmitter and receiver where the
SNR is provided as an input that represents the channel conditions. To show how
ProgTr works, the proposed scheme was simulated in different scenarios
including single/multi-user settings, different channel conditions, and for
both discrete and continuous input data. The results show that ProgTr can
achieve better performance compared to conventional modulation methods. In
addition to performance metrics such as BER, bit-wise mutual information is
used to provide some interpretation to how the transmitter and receiver operate
in ProgTr.
