MIMO Detection under Hardware Impairments: Data Augmentation With Boosting

TL;DR

This paper introduces a novel data augmentation and boosting approach for MIMO detection under hardware impairments, improving likelihood function estimation and detection accuracy without explicit impairment knowledge.

Contribution

It develops new likelihood function estimation methods using data augmentation and boosting, enhancing MIMO detection performance under hardware impairments.

Findings

Significant performance gains over existing methods.

Effectiveness increases with larger augmented datasets.

Both EM and KDE-based methods are effective.

Abstract

This paper addresses a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown hardware impairments, we develop novel likelihood function (LF) estimation methods based on data augmentation and boosting. The core idea of our methods is to generate multiple augmented datasets by injecting noise with various distributions into seed data consisting of online received signals. We then estimate the LF using each augmented dataset based on either the expectation maximization (EM) algorithm or the kernel density estimation (KDE) method. Inspired by boosting, we further refine the estimated LF by linearly combining the multiple LF estimates obtained from the augmented datasets. To determine the weights for this linear combination, we develop three methods that take different approaches to measure the reliability of the estimated LFs. Simulation results demonstrate that both the EM- and KDE-based LF estimation methods offer significant performance gains over existing LF estimation methods. Our results also show that the effectiveness of the proposed methods improves as the size of the augmented data increases.