Off-Grid Channel Estimation Using Grid Refinement and Adjustment for ODDM Systems

Recently, orthogonal delay-Doppler (DD) division multiplexing (ODDM) has been proposed as a newly multicarrier modulation scheme to tackle the high-Doppler spread in high-mobility environments. The existing studies have shown that ODDM outperforms the other DD plane modulation schemes related the out-of-band emission and the bit error rate. Accurate channel estimation is of paramount importance to guarantee reliable communication for the ODDM systems, especially when the delays and Dopplers of the propagation paths are off-grid. In this paper, we propose a grid refinement and grid adjustment-based off-grid channel estimation scheme for ODDM systems. Specifically, we first formulate the channel estimation problem in ODDM systems as a sparse signal recovery problem. Then, an iterative process to estimate the channel parameters while refining and adjusting the virtual grid points is proposed. Unlike the previous sparse Bayesian learning (SBL)-based channel estimation schemes where the virtual grid is fixed or updated after the SBL iteration, the proposed scheme extends the SBL to simultaneously update the grid points and the channel estimates via the maximum likelihood principle. Finally, simulation results are provided to demonstrate the efficiency of the proposed channel estimation scheme.