Enhanced LMMSE Estimation Capable of Selecting Parameters

TL;DR

This paper introduces an enhanced LMMSE estimation method for OFDM systems that autonomously selects optimal parameters, improving performance by evaluating practical interpolation accuracy and adapting to unpredictable channel conditions.

Contribution

The paper presents a novel self-parameter selection scheme for LMMSE estimation, addressing the challenge of choosing optimal parameters in dynamic channel environments.

Findings

Improved estimation accuracy demonstrated in simulations

Effective parameter selection enhances OFDM performance

Reduced need for manual parameter tuning

Abstract

In the linear minimum mean square error (LMMSE) estimation for orthogonal frequency division multiplexing (OFDM) systems, the problem about the determination of the algorithm's parameters, especially those related with channel frequency response (CFR) correlation, has not been readily solved yet. Although many approaches have been proposed to determine the statistic parameters, it is hard to choose the best one within those approaches in the design phase, since every approach has its own most suitable application conditions and the real channel condition is unpredictable. In this paper, we propose an enhance LMMSE estimation capable of selecting parameters by itself. To this end, sampled noise MSE is first proposed to evaluate the practical performance of interpolation. Based on this evaluation index, a novel parameter comparison scheme is proposed to determine the parameters which can endow LMMSE estimation best performance within a parameter set. After that, the structure of the enhanced LMMSE is illustrated, and it is applied in OFDM systems. Besides, the issues about theoretical analysis on accuracy of the parameter comparison scheme, the parameter set design and algorithm complexity are explained in detail. At last, our analyses and performance of the proposed estimation method are demonstrated by simulation experiments.