Simultaneous Learning and Inferencing of DNN-Based mmWave Massive MIMO Channel Estimation in IoT Systems With Unknown Nonlinear Distortion

In this article, we propose an online training framework for deep neural network (DNN)-based mmWave massive multiple-input multiple-output (MIMO) channel estimation (CE) in Internet-of-Things (IoT) systems with nonlinear amplifier distortions. The DNN-based channel estimator is trained online in the IoT device based on real-time received pilot measurements from the base station (BS) without knowledge of the true channels, and can simultaneously generate CE in real time. To realize this, we first propose three axioms for a legitimate online loss function under known nonlinearity, based on which we develop a channel model-free online training algorithm with convergence analysis. For unknown nonlinearity, we propose a two-stage DNN structure with nonlinear modules, for which the DNN-based CE and nonlinear functions can be jointly trained online based on real-time received pilots. Simulation results show that the proposed solution achieves better CE accuracy than traditional compressive sensing (CS) algorithms while enjoying a much faster computational efficiency. In addition, the proposed method is robust to various nonlinear channel model mismatches and is able to track the change of the nonlinear channel model.

Automated summary

This article proposes an online training framework based on deep neural network (DNN) for mmWave massive multiple-input multiple-output (MIMO) channel estimation in Internet-of-Things (IoT) systems. The framework takes into account the nonlinear amplifier distortions and trains the DNN-based channel estimator online using real-time received pilot measurements. The proposed framework achieves high estimation accuracy and computational efficiency, even in the presence of unknown nonlinearity and model mismatches.