New Gridless Method-Based Channel Estimation for Millimeter Wave MIMO-OFDM Systems

Accurate channel estimation is crucial for millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Conventional grid-based compressive sensing approaches encounter the base mismatch problem, which degrades channel estimation accuracy. To address this issue, this letter proposes a novel gridless channel estimation strategy tailored for mmWave MIMO-OFDM systems. Specifically, the channel estimation problem is formulated as a joint sparse signal recovery problem by exploiting the inherent sparsity in the angle-delay domain of the mmWave channel. We then introduce a Hankel-Toeplitz block model-based new atomic norm minimization (NANM) algorithm with multiple measurement vectors (MMV), representing the formulated problem as a semi-definite programming (SDP) problem with structured sparsity. To efficiently solve the SDP problem, we employ a low-complexity alternating direction multiplier method (ADMM). Simulation results verify that the proposed method significantly enhances channel estimation accuracy with reduced pilot overhead, compared with conventional channel estimation techniques.