Maximum Likelihood Algorithms for Joint Estimation of Synchronization Impairments and Channel in MIMO-OFDM System

TL;DR

This paper develops and compares maximum likelihood algorithms for jointly estimating synchronization impairments and channel characteristics in MIMO-OFDM systems, addressing coupling effects and reducing computational complexity.

Contribution

It introduces a joint ML estimation framework with a grid search, a modified ML algorithm with reduced complexity, and a stage-wise ML approach, advancing joint impairment estimation techniques.

Findings

ML and MML algorithms outperform SML in estimation accuracy.

Proposed algorithms effectively handle coupling among impairments.

Numerical results demonstrate improved performance over existing methods.

Abstract

Maximum Likelihood (ML) algorithms, for the joint estimation of synchronization impairments and channel in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, are investigated in this work. A system model that takes into account the effects of carrier frequency offset, sampling frequency offset, symbol timing error, and channel impulse response is formulated. Cram\'{e}r-Rao Lower Bounds for the estimation of continuous parameters are derived, which show the coupling effect among different impairments and the significance of the joint estimation. We propose an ML algorithm for the estimation of synchronization impairments and channel together, using grid search method. To reduce the complexity of the joint grid search in ML algorithm, a Modified ML (MML) algorithm with multiple one-dimensional searches is also proposed. Further, a Stage-wise ML (SML) algorithm using existing algorithms, which estimate fewer number of parameters, is also proposed. Performance of the estimation algorithms is studied through numerical simulations and it is found that the proposed ML and MML algorithms exhibit better performance than SML algorithm.