Enhanced ELM Based Channel Estimation for RIS-Assisted OFDM systems with Insufficient CP and Imperfect Hardware

TL;DR

This paper introduces an enhanced ELM-based channel estimation method for RIS-assisted OFDM systems that effectively handles challenges from insufficient cyclic prefix and hardware imperfections, improving accuracy and robustness.

Contribution

It proposes a novel model-driven ELM-based channel estimation approach that refines initial LS estimates to combat CP insufficiency and hardware non-linearities in RIS-assisted OFDM systems.

Findings

Achieves lower NMSE compared to existing methods under challenging conditions.

Demonstrates robustness against parameter variations.

Improves channel estimation accuracy with enhanced ELM network.

Abstract

Reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems have aroused extensive research interests due to the controllable communication environment and the performance of combating multi-path interference. However, as the premise of RIS-assisted OFDM systems, the accuracy of channel estimation is severely degraded by the increased possibility of insufficient cyclic prefix (CP) produced by extra cascaded channels of RIS and the nonlinear distortion lead by imperfect hardware. To address these issues, an enhanced extreme learning machine (ELM)- based channel estimation (eELM-CE) is proposed in this letter to facilitate accurate channel estimation. Based on the model-driven mode, least square (LS) estimation is employed to highlight the initial linear features for channel estimation. Then, according to the obtained initial features, an enhanced ELM network is constructed to refine the channel estimation. In particular, we start from the perspective of guiding it to recognize the feature, and normalize the data after the network activation function to enhance the ability of identifying non-linear factors. Experiment results show that, compared with existing methods, the proposed method achieves a much lower normalized mean square error (NMSE) given insufficient CP and imperfect hardware. In addition, the simulation results indicate that the proposed method possesses robustness against the parameter variations.