Enhanced LMMSE Estimation Capable of Selecting Parameters
Kai Mei, Jun Liu, Xiaoran Liu, Jun Xiong, Xiaoying Zhang, Jibo Wei

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…
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Taxonomy
TopicsAdvanced Wireless Communication Techniques · PAPR reduction in OFDM · Advanced Adaptive Filtering Techniques
