Multilevel MIMO Detection with Deep Learning
This work addresses signal detection in wireless communications, presenting an incremental improvement with a novel neural structure.
The paper tackles the problem of detecting multilevel modulation symbols in MIMO systems using deep neural networks, achieving near-maximum-likelihood performance with a relatively reasonable number of parameters.
A quasi-static flat multiple-antenna channel is considered. We show how real multilevel modulation symbols can be detected via deep neural networks. A multi-plateau sigmoid function is introduced. Then, after showing the DNN architecture for detection, we propose a twin-network neural structure. Batch size and training statistics for efficient learning are investigated. Near-Maximum-Likelihood performance with a relatively reasonable number of parameters is achieved.