In this paper, a Reinforcement Learning (RL) algorithm is presented to speed up the selection process of spatial beams to maximize the mean data rate of a multiantenna wireless system that implements hybrid beamforming in Millimeter Wave (mmWave) frequency bands. In the proposed hybrid beamforming architecture, the analog beamforming layer is codebook-based, and is implemented using a simple array of phase-shifters that delay the RF signal in the different transmit antennas using a fixed number of discrete steps. In contrast, the digital beamforming layer is much more flexible, and implements a fully adaptive (i.e., non-quantized) digital precoding scheme that enables the simultaneous transmission of few independent baseband data streams in the spatial domain. Obtained simulation results show that the use of RL-based techniques reduces the iterations that are needed to find the most convenient analog beamformers and digital precoders to be used in transmission, without affecting notably the upper bound data rate that is achieved when brute-force search is utilized.

N-Continuous OFDM systems have been proposed to achieve an important reduction of the out-of-band emitted power compared to conventional OFDM. However, system complexity has been increased and some resource demanding operations are necessary. So, this work considers the implementation in FPGA of the transmitter and also provides a novel analysis on the influence of the IFFT length in the representation of the continuity condition. Spectral measurements are practiced in the model to evaluate the performance.

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This paper proposes a Machine Learning (ML) algorithm for hybrid beamforming in millimeter-wave wireless systems with multiple users. The time-varying nature of the wireless channels is taken into account when training the ML agent, which identifies the most convenient hybrid beamforming matrix with the aid of an algorithm that keeps the amount of signaling information low, avoids sudden changes in the analog beamformers radiation patterns when scheduling different users (flashlight interference), and simplifies the hybrid beamformer update decisions by adjusting the phases of specific analog beamforming vectors. The proposed hybrid beamforming algorithm relies on Deep Reinforcement Learning (DRL), which represents a practical approach to embed the online adaptation feature of the hybrid beamforming matrix into the channel states of continuous nature in which the multiuser MIMO system can be. Achievable data rate curves are used to analyze performance results, which validate the advantages of DRL algorithms with respect to solutions relying on conventional/deterministic optimization tools.