Joint Phase Noise Estimation and Data Detection in Coded MIMO Systems

TL;DR

This paper introduces a joint phase noise estimation and data detection method for MIMO systems using an iterative EM-based receiver with an EKFS, significantly improving performance in the presence of phase noise.

Contribution

It proposes a novel EM-based MIMO receiver with an EKFS for joint phase noise estimation and data detection, reducing complexity and enhancing accuracy.

Findings

EKFS effectively tracks phase noise in MIMO systems.

Iterative receiver with LDPC codes outperforms existing algorithms.

Significant performance gains in high SNR scenarios.

Abstract

In this paper, the problem of joint oscillator phase noise (PHN) estimation and data detection for multi-input multi-output (MIMO) systems using bit-interleaved coded modulation (BICM) is analyzed. A new MIMO receiver that iterates between the estimator and the detector, based on the expectation-maximization (EM) framework, is proposed. It is shown that at high signal-to-noise ratios, a maximum a posteriori estimator (MAP) can be used to carry out the maximization step of the EM algorithm. Moreover, to reduce the computational complexity of the proposed EM algorithm, a soft decision-directed extended Kalman filter-smoother (EKFS) is applied instead of the MAP estimator to track the PHN parameters. Numerical results show that by combining the proposed EKFS based approach with an iterative detector that employs low density parity check (LDPC) codes, PHN can be accurately tracked. Simulations also demonstrate that compared to existing algorithms, the proposed iterative receiver can significantly enhance the performance of MIMO systems in the presence of PHN.