A Novel Cross-Domain Channel Estimation Scheme for OFDM

TL;DR

This work tackles OFDM channel estimation in high-mobility scenarios where Doppler disrupts subcarrier orthogonality. It introduces a cross-domain channel estimation (CDCE) framework that first extracts coarse delay-Doppler (DD) information via a twisted-convolution correlation in the DD domain and then performs sparse TF-domain channel recovery using a dictionary built from the DD estimates. The approach combines DD-domain correlation with an $\ell_1$-regularized LS (LASSO) solver to identify active TF-domain paths, achieving lower NMSE than conventional estimators while avoiding matrix inversions and needing no a priori channel information. Numerical results show NMSE improvements of roughly $4$–$5$ dB over strong benchmarks, with robust performance even when data interference is present, highlighting the practical impact for high-mobility OFDM systems.

Abstract

In this paper, we propose a novel cross-domain channel estimation (CDCE) algorithm for orthogonal frequency division multiplexing (OFDM) systems, leveraging the unique characteristics of the delay-Doppler (DD) domain channel. Specifically, the proposed algorithm transforms the time-frequency (TF) domain pilot sequence of OFDM into the DD domain and applies a two-dimensional (2D) twisted-convolution for acquiring a coarse estimation of the underlying channel delay and Doppler. Then, the OFDM channel estimation is formulated as a sparse signal recovery problem in the TF domain according to the dictionary derived based on the obtained delay and Doppler estimates. Furthermore, a low-complexity $\ell_1$-regularized least-square estimator is proposed to effectively solve this problem. Moreover, we further develop a performance analysis framework of the proposed scheme based on the ambiguity function (AF) of the adopted pilot sequence. Our numerical results demonstrate noticeable estimation performance improvement compared to conventional OFDM channel estimation methods, particularly in the presence of high channel mobility.

Figures (5)

• Figure 1: Pilot arrangement in the TF domain.
• Figure 2: DD domain illustration of different TF domain pilot sequences ($M=N=16$).
• Figure 3: AFs of different TF domain pilot sequences ($M=N=16$).
• Figure 4: NMSE comparison for different estimation schemes.
• Figure 5: NMSE comparison for different estimation schemes with data.