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Hybrid precoding design can be challenging for broadband millimeter-wave(mmWave) massive MIMO due to the frequency-flat analog precoder in radiofrequency (RF). Prior broadband hybrid precoding work usually focuses onfully-connected array (FCA), while seldom considers the energy-efficientpartially-connected subarray (PCS) including the fixed subarray (FS) anddynamic subarray (DS). Against this background, this paper proposes a machinelearning based broadband hybrid precoding for mmWave massive MIMO with DS.Specifically, we first propose an optimal hybrid precoder based on principalcomponent analysis (PCA) for the FS, whereby the frequency-flat RF precoder foreach subarray is extracted from the principle component of the optimalfrequency-selective precoders for fully-digital MIMO. Moreover, we extend thePCA-based hybrid precoding to DS, where a shared agglomerative hierarchicalclustering (AHC) algorithm developed from machine learning is proposed to groupthe DS for improved spectral efficiency (SE). Finally, we investigate theenergy efficiency (EE) of the proposed scheme for both passive and activeantennas. Simulations have confirmed that the proposed scheme outperformsconventional schemes in both SE and EE.

Machine Learning Based Hybrid Precoding for MmWave MIMO-OFDM with Dynamic Subarray