A Full Performance Analysis of Channel Estimation Methods for Time Varying OFDM Systems

TL;DR

This paper comprehensively evaluates various channel estimation methods for OFDM systems under time-varying conditions, comparing their performance across different modulation schemes and channel dynamics.

Contribution

It provides a detailed analysis of multiple estimation algorithms tailored for both slow and fast time-varying channels, highlighting their effectiveness and suitability.

Findings

Different algorithms perform optimally under specific channel conditions.

Some methods are better suited for stationary channels, others for highly dynamic environments.

The study offers insights into the trade-offs between estimation accuracy and computational complexity.

Abstract

In this paper, we have evaluated various methods of time-frequency-selective fading channels estimation in OFDM system and some of them improved under time varying conditions. So, these different techniques will be studied through different algorithms and for different schemes of modulations (16 QAM, BPSK, QPSK, ...). Channel estimation gathers different schemes and algorithms, some of them are dedicated for slowly time varying (such as block type arrangement insertion, Bayesian Cramer-Rao Bound, Kalman estimator, Subspace estimator, ...) whereas the others concern highly time varying channels (comb type insertion, ...). There are others methods that are just suitable for stationary channels like blind or semi blind estimators. For this aim, diverse algorithms were used for these schemes such as Least Squares estimator LS, Least Minimum Squares LMS, Minimum Mean-Square-Error MMSE, Linear Minimum Mean-Square-Error LMMSE, Maximum Likelihood ML, ... to refine estimators shown previously.