The paper describes an online deep learning algorithm for the 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 is 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 our algorithm shows 10\% to 20\% improvement of user throughput in the full buffer case of continuous traffic. This is a very valuable result that allows us to significantly improve the quality of wireless MIMO communications.

翻译：本文描述了大规模 MIMO 适应性调制和编码的在线深层次学习算法。 算法基于一个完全连接的神经网络, 该网络最初接受传统算法输出的培训, 然后通过输出的服务反馈逐步得到再培训。 我们展示了我们解决方案相对于最先进的Q- 学习方法的优势。 我们以不同频道特点和不同用户速度的不同情景提供系统级模拟结果来支持这一结论。 与传统的 OLLA 相比, 我们的算法显示, 在连续交通的全缓冲案例中,用户的吞吐量在10到20个之间得到了改善。 这是一个非常有价值的结果,使我们能够大幅提高无线MIMO 通信的质量。