Semi-supervised learning based hybrid beamforming under time-varying propagation environments

Abstract

Hybrid precoding is considered as a promising low-cost technique for millimeter wave (mm-wave) massive Multi-Input Multi-Output (MIMO) systems. In this work, referring to the time-varying propagation circumstances, with semi-supervised Incremental Learning (IL), we propose an online hybrid beamforming scheme. Firstly, given the constraint of constant modulus on analog beamformer and combiner, we propose a new broad-network-based structure for the design model of hybrid beamforming. Compared with the existing network structure, the proposed network structure can achieve better transmission performance and lower complexity. Moreover, to enhance the efficiency of IL further, by combining the semi-supervised graph with IL, we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning, where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk. Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update, all transmissions, even the ones during the model update, would make contributions to model update in the proposed method. During the model update, the amount of unlabelled transmissions is very large and they also carry some information, the prediction performance can be enhanced to some extent by these unlabelled channel data. Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach. Besides, we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.