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Reconfigurable intelligent surface (RIS)-assisted orthogonal frequencydivision multiplexing (OFDM) systems have aroused extensive research interestsdue to the controllable communication environment and the performance ofcombating multi-path interference. However, as the premise of RIS-assisted OFDMsystems, the accuracy of channel estimation is severely degraded by theincreased possibility of insufficient cyclic prefix (CP) produced by extracascaded channels of RIS and the nonlinear distortion lead by imperfecthardware. To address these issues, an enhanced extreme learning machine (ELM)-based channel estimation (eELM-CE) is proposed in this letter to facilitateaccurate channel estimation. Based on the model-driven mode, least square (LS)estimation is employed to highlight the initial linear features for channelestimation. Then, according to the obtained initial features, an enhanced ELMnetwork is constructed to refine the channel estimation. In particular, westart from the perspective of guiding it to recognize the feature, andnormalize the data after the network activation function to enhance the abilityof identifying non-linear factors. Experiment results show that, compared withexisting methods, the proposed method achieves a much lower normalized meansquare error (NMSE) given insufficient CP and imperfect hardware. In addition,the simulation results indicate that the proposed method possesses robustnessagainst the parameter variations.

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