  The paper describes an online deep learning algorithm (ODL) for adaptive
modulation and coding in massive MIMO. The algorithm is based on a fully
connected neural network, which is initially trained on the output of the
traditional algorithm and then incrementally retrained by the service feedback
of its output. We show the advantage of our solution over the state-of-the-art
Q-learning approach. We provide system-level simulation results to support this
conclusion in various scenarios with different channel characteristics and
different user speeds. Compared with traditional OLLA, the algorithm shows a
10\% to 20\% improvement in user throughput in the full-buffer case.
