OTFS Channel Estimation Based on Sparse Bayesian Learning

doi:10.16183/j.cnki.jsjtu.2024.141

Abstract

Abstract:

The acquisition of channel state information (CSI) has a significant impact on the performance of orthogonal time frequency space (OTFS) modulated communication systems. By exploiting the sparsity in the delay-Doppler domain, the channel estimation problem of OTFS can be transformed into a two-dimensional sparse representation problem. However, the computational complexity of solving the two-dimensional sparse signal is extremely high, and existing methods generally cannot effectively mitigate errors caused by fractional Doppler/delay. To address these issues, this paper proposes a non-uniform grid-based sparse representation method to model the OTFS channel estimation as a lower-dimensional sparse signal recovery problem. Sparse Bayesian learning is then used to infer the optimal solution in the maximum a posteriori (MAP) sense, enabling two-dimensional sparse signal recovery with low computational complexity. To counteract the fractional Doppler/delay errors, the coordinates of each grid point are treated as adjustable variables and adaptively iterated. Simulation results illustrate that the proposed method can effectively improve the performance of OTFS channel estimation.

Key words: channel estimation, orthogonal time frequency space (OTFS) modulation, sparse representation, sparse Bayesian learning, non-uniform grid

CLC Number:

TN929.5

SUN Fujun, MA Ming, FU Haijun, DAI Jisheng. OTFS Channel Estimation Based on Sparse Bayesian Learning[J]. Journal of Shanghai Jiao Tong University, 2026, 60(5): 743-750.

Figures/Tables 5

References 26

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方法	计算复杂度
On-grid SBL	O(${N}_{0}^{2}{M}_{0}^{2}$NML)
Delay SBL	O(${N}_{0}^{2}{M}_{0}^{2}$NML)
Doppler SBL	O(${N}_{0}^{2}{M}_{0}^{2}$NML)
Off-grid SBL	O(${N}_{0}^{2}{M}_{0}^{2}$NML')
所提方法	O(E²NML)