A Structured Sparse Bayesian Channel Estimation Approach for Orthogonal Time-Frequency Space Modulation

Orthogonal time-frequency space (OTFS) modulation has been advocated as a promising waveform for achieving integrated sensing and communication (ISAC) due to its superiority in high-mobility adaptability and spectral efficiency. In OTFS modulation-based ISAC systems, accurate channel acquisition is critical for both communication reception and sensing parameter estimation. However, the existence of the fractional Doppler frequency shift spreads the effective channels of the OTFS signal significantly, making efficient channel acquisition very challenging. In this paper, we first derive the sparse structure of the channel in the delay Doppler (DD) domain according to the input and output relationship of OTFS signals. On this basis, a new structured Bayesian learning approach is proposed for accurate channel estimation, which includes a novel structured prior model for the delay-Doppler channel and a successive majorization-minimization (SMM) algorithm for efficient posterior channel estimate computation. Simulation results show that the proposed approach significantly outperforms the reference schemes, especially in the low signal-to-noise ratio (SNR) region.

Automated summary

This paper investigates orthogonal time-frequency space (OTFS) modulation as a promising waveform for achieving integrated sensing and communication (ISAC) due to its advantages in high-mobility adaptability and spectral efficiency. Accurate channel acquisition is critical in OTFS modulation-based ISAC systems for both communication reception and sensing parameter estimation. However, the fractional Doppler frequency shift significantly spreads the effective channels of the OTFS signal, making efficient channel acquisition challenging. To address this issue, a new structured Bayesian learning approach is proposed in this paper, which includes a novel structured prior model for the delay-Doppler channel and a successive majorization-minimization (SMM) algorithm for efficient posterior channel estimate computation. Simulation results show that the proposed approach outperforms the reference schemes, especially in the low signal-to-noise ratio (SNR) region.