Resumen
Channel estimation is an important component of orthogonal frequency division multiplexing (OFDM) systems. The existence of virtual subcarriers leads to energy spreading in the time-domain when using Inverse Fast Fourier Transform (IFFT), resulting in poor noise reduction by the conventional Discrete Fourier Transform (DFT)-based channel estimation algorithm. To tackle this problem, this paper first proposes a segmental threshold-assisted DFT-based channel estimation algorithm. The key idea is that, by utilizing the distribution characteristics of the channel and the noise components of the channel impulse response in the time-domain, different thresholds for channel estimation under different SNR conditions are set. Compared with the traditional single-threshold DFT-based algorithm, the performance of the proposed algorithm is improved. However, it still has an estimation performance floor under high SNR. Motivated by the fact that the discrete wavelet transform (DWT)-based channel estimation algorithm can achieve better estimation performance under high SNR, we propose a joint channel estimation algorithm based on DFT and DWT, which can achieve dynamic optimal selection of the two estimation methods without any prior information. Simulation results of the Wi-Fi 6 system show that the mean square error (MSE) simulation performance of the joint channel estimation algorithm is close to its theoretical approximation. It achieves the optimal estimation of MSE and BER performance across the entire SNR range compared with the separated DFT-based or DWT-based channel estimation algorithms.