Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.

Estimation of carrier frequency offset (CFO) is a challenging task in practical systems specifically in the uplink of multiuser systems where multiple CFOs are present in the received signal. Massive MIMO as a multiuser technique has recently attracted a great deal of attention among researchers. However, to the best of our knowledge, there is no study looking into the joint estimation of CFOs and wireless channel in orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. Therefore, in this paper, we propose joint estimation of multiple CFOs and the users' channel responses based on the maximum likelihood (ML) criteria in such systems. We propose to use the zadoff-chu (ZC) training sequences to reduce the implementation complexity. Additionally, utilization of ZC sequences for training simplifies the multidimensional grid search problem of estimating multiple CFOs and converts it into a set of line search problems, i.e., one line search problem per user. Also this sequence has a low peak to average ratio (PAPR). Finally, we show the efficacy of our proposed algorithm through numerical simulations.

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Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.

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