Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm

IEEE Communications Letters. 2022. Vol. 26. No. 4. P. 818–822.

Bobrov E., Kropotov Dmitry, Lu H., Zaev D.

IEEEThe 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.

Keywords: training, neural networks, interference, reinforcement learning, deep learning, wireless communications, throughput, signal to noise ratio, online training, Adaptive Modulation and Coding, Link Adaptation, Massive MIMO, Olla