A Comprehensive Performance Analysis for mm-Wave Massive MIMO Hybrid Beamforming under PA Nonlinearities

Abstract: In this paper, we develop a framework to investigate the performances of different hybrid beamforming architectures for massive multiple input multiple output (MIMO) systems impaired by power amplifier (PA) nonlinearities. Indirect learning architecture based on feedback after anti-beamforming is adopted in design of digital pre-distortion (DPD) in order to compensate the nonlinear distortion caused by PA. In addition, we propose a novel analog beamformer design for partially connected architecture based on generalized eigen-beamformer (GEB) approach. In literature, the effects of nonlinear PA's on the out-of-band (OOB) radiation and achieved signal-to-interfence-plus-noise ratio (SINR) are investigated. However, these studies are limitted to fully digital or partially connected hybrid beamforming architectures while deploying Bussgang decompostion on a PA basis without considering the array architecture type in performance analysis. In this study, we derived an analytical bit-error-rate (BER) expression based on spatio-temporal Bussgang decompostion in matrix form, and mismatched decoding capacity via Generalized Mutual Information (GMI) is obtained under PA nonlinearity for different hybrid Massive MIMO architectures. Analytical results show that the nonlinear distortion significantly affects the system performance, and DPD can reduce these effects to some extend. Finally, obtained analytical BER expression is verified via numerical results.