EVM Mitigation With PAPR and ACLR Constraints in Large-Scale MIMO-OFDM Using TOP-ADMM

• Published in: IEEE transactions on wireless communications Vol. 21; no. 11; pp. 9460 - 9481
• Main Authors: Kant, Shashi, Bengtsson, Mats, Fodor, Gabor, Goransson, Bo, Fischione, Carlo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; ACLR; Algorithms; Base stations; Beamforming; Broadcasting; Channels; EVM; Machine learning; MIMO communication; MIMO-OFDM; Nonconvex PAPR reduction; Nonlinear distortion; OFDM; Optimization; Orthogonal Frequency Division Multiplexing; Peak to average power ratio; Receivers; Reduction; Robustness (mathematics); Signal distortion; three-operator ADMM (TOP-ADMM)
• ISSN: 1536-1276; 1558-2248; 1558-2248
• DOI: 10.1109/TWC.2022.3177136

Although signal distortion-based peak-to-average power ratio (PAPR) reduction is a feasible candidate for orthogonal frequency division multiplexing (OFDM) to meet standard/regulatory requirements, the error vector magnitude (EVM) stemming from the PAPR reduction has a deleterious impact on the performance of high data-rate achieving multiple-input multiple-output (MIMO) systems. Moreover, these systems must constrain the adjacent channel leakage ratio (ACLR) to comply with regulatory requirements. Several recent works have investigated the mitigation of the EVM seen at the receivers by capitalizing on the excess spatial dimensions inherent in the large-scale MIMO that assume the availability of perfect channel state information (CSI) with spatially uncorrelated wireless channels. Unfortunately, practical systems operate with erroneous CSI and spatially correlated channels. Additionally, most standards support user-specific/CSI-aware beamformed and cell-specific/non-CSI-aware broadcasting channels. Hence, we formulate a robust EVM mitigation problem under channel uncertainty with nonconvex PAPR and ACLR constraints catering to beamforming/broadcasting. To solve this formidable problem, we develop an efficient scheme using our recently proposed three-operator alternating direction method of multipliers (TOP-ADMM) algorithm and benchmark it against two three-operator algorithms previously presented for machine learning purposes. Numerical results show the efficacy of the proposed algorithm under imperfect CSI and spatially correlated channels.