Online Deep Neural Networks for MmWave Massive MIMO Channel Estimation With Arbitrary Array Geometry

In this paper, we propose an online training framework for mmWave Massive MIMO channel estimation (CE) with limited pilots, where the training is based on real-time received pilot samples from the base station without requiring knowledge of the true channel. To realize this, we propose four axioms for a legitimate online loss function, based on which we develop a model-free online training algorithm with convergence analysis. Simulation shows that the proposed online deep neural network (DNN) achieves comparable CE accuracy to model-based compressive sensing (CS) algorithms, while enjoying much faster computation. In addition, the proposed method is robust to various model mismatches and can adapt to the change of the underlying propagation environment.