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Multiple-input multiple-output (MIMO) communication is expected to play a central role in future wireless systems through the deployment of a large number of antennas at the transmitters and receivers. In low-frequency systems, massive MIMO offers high multiplexing gains that boost system spectral efficiency. In millimeter wave (mmWave) systems, the deployment of large antenna arrays at both the base station and mobile users is necessary to guarantee sufficient received signal power. Realizing these systems in practice, however, requires addressing several key challenges: (i) fully-digital solutions are costly and power hungry, (ii) channel training and estimation process has high overhead, and (iii) precoders design optimization problems are non-trivial. In this dissertation, precoding and channel estimation strategies that address these challenges are proposed for both mmWave and massive MIMO systems. The proposed solutions adopt hybrid analog/digital architectures that divide precoding/combining processing between RF and baseband domains and lead to savings in cost and power consumption. Further, the developed techniques leverage the structure and characteristics of mmWave and massive MIMO channels to reduce the training overhead and precoders design complexity. The main contributions of this dissertation are (i) developing a channel estimation solution for hybrid architecture based mmWave systems, exploiting the sparse nature of the mmWave channels, (ii) designing hybrid precoding algorithm for multi-user mmWave and massive MIMO systems, (iii) proposing a multi-layer precoding framework for massive MIMO cellular systems, and (iv) developing hybrid precoding and codebook solutions for frequency selective mmWave systems. Mathematical analysis as well as numerical simulations illustrate the promising performance of the proposed solutions, marking them as enabling technologies for mmWave and massive MIMO systems.