Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM

TL;DR

This paper introduces efficient algorithms for joint phase noise and channel estimation in OFDM, reformulating the problem in the time domain and using dimensionality reduction to improve accuracy and reduce computational complexity.

Contribution

The paper presents a novel time-domain reformulation and a dimensionality reduction approach for joint phase noise and channel estimation in OFDM, improving efficiency and accuracy.

Findings

Outperforms benchmarks in mean squared error across various SNRs.

Achieves lower computational complexity compared to existing methods.

Effectively adapts to different phase noise levels using BIC.

Abstract

Phase noise correction is crucial to exploit full advantage of orthogonal frequency division multiplexing (OFDM) in modern high-data-rate communications. OFDM channel estimation with simultaneous phase noise compensation has therefore drawn much attention and stimulated continuing efforts. Existing methods, however, either have not taken into account the fundamental properties of phase noise or are only able to provide estimates of limited applicability owing to considerable computational complexity. In this paper, we have reformulated the joint estimation problem in the time domain as opposed to existing frequency-domain approaches, which enables us to develop much more efficient algorithms using the majorization-minimization technique. In addition, we propose a method based on dimensionality reduction and the Bayesian Information Criterion (BIC) that can adapt to various phase noise levels and accomplish much lower mean squared error than the benchmarks without incurring much additional computational cost. Several numerical examples with phase noise generated by free-running oscillators or phase-locked loops demonstrate that our proposed algorithms outperform existing methods with respect to both computational efficiency and mean squared error within a large range of signal-to-noise ratios.