MIMO Detection under Hardware Impairments: Data Augmentation With Boosting

TL;DR

This work tackles MIMO data detection when hardware impairments are unknown and nonlinear. It introduces a data augmentation framework with boosting to estimate the likelihood functions needed for maximum-likelihood detection, using online seed data augmented with multiple noise distributions and combining LF estimates via boosting. The approach is extended to time-varying channels through a sub-block, online-learning strategy that preserves effectiveness without extra pilot overhead. Results show significant SER improvements over conventional LF estimation methods, particularly as the augmented data size grows, and validate the method under both time-invariant and time-varying impairments such as non-ideal PAs and low-resolution ADCs.

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.

Figures (8)

• Figure 1: An illustration of a MIMO communication system under hardware impairments.
• Figure 2: Block diagram of the data detection process with the proposed LF estimation method for time-invariant channels.
• Figure 3: Block diagram of the data detection process with the proposed LF estimation method for time-varying channels.
• Figure 4: SER comparison of various detection methods for a different number of receive antennas with ${\sf SNR} = 20$dB in time-invariant channels.
• Figure 5: SER comparison of various detection methods for different SNRs with $(N_t, N_r)=(2,6)$ in time-invariant channels.