# Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm

arXiv: https://arxiv.org/abs/2105.12827 · HTML: https://mlsanity.com/paper/2105.12827/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN](https://mlsanity.com/paper/2606.23110/) (2026)
- [Variational Autoencoders for Precoding Matrices with High Spectral Efficiency](https://mlsanity.com/paper/2111.15626/) (2021)
- [Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems](https://mlsanity.com/paper/2112.14423/) (2021)
- [Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey](https://mlsanity.com/paper/2302.13876/) (2023)

## Abstract (arXiv)

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.