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

TL;DR

This paper proposes a robust EVM mitigation method for large-scale MIMO-OFDM systems that effectively handles PAPR and ACLR constraints under imperfect CSI and spatial correlation using the TOP-ADMM algorithm.

Contribution

It introduces a novel robust EVM mitigation framework with nonconvex PAPR and ACLR constraints tailored for practical large-scale MIMO-OFDM systems with channel uncertainties.

Findings

The proposed TOP-ADMM algorithm outperforms benchmarks under imperfect CSI.

Effective EVM mitigation is achieved with spatially correlated channels.

The method complies with regulatory PAPR and ACLR constraints.

Abstract

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.