Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm

TL;DR

This paper introduces an online deep learning algorithm for adaptive modulation and coding in massive MIMO systems, demonstrating improved throughput over traditional methods through system-level simulations.

Contribution

It presents a novel online deep learning approach that outperforms existing Q-learning methods for adaptive modulation and coding in massive MIMO.

Findings

10-20% throughput improvement over traditional OLLA

Effective in various channel scenarios and user speeds

Outperforms state-of-the-art Q-learning approach

Abstract

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.