Abstract
The paper describes an online deep learning algorithm for the adaptive
modulation and coding in 5G 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 is incrementally retrained by the
service feedback of its own output. We show 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 speed. Compared
with traditional OLLA the proposal shows 10% to 20% improvement of
user throughput in full buffer case.