A Sparsity-Agnostic SL0 Channel Estimation Approach for OTFS Systems

Orthogonal time frequency space (OTFS) modulation has been shown to support reliable communication in high mobility scenarios. Due to the sparsity of the delay-Doppler (DD) domain, most existing algorithms utilize compressed sensing (CS) for OTFS channel estimation, while requiring a known channel sparsity. However, in cases that the sparsity is not readily accessible, the estimation accuracy of these CS algorithms decreases dramatically. In this letter, we propose a Smoothed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> (SL0) algorithm free of prior knowledge about channel sparsity for OTFS channel estimation. We obtain a novel vector expression of the input-output relationship in the DD domain and formulate it as a sparse signal recovery problem. In the case of unknown sparsity, a low-complexity SL0 algorithm is introduced to solve the problem with a faster reconstruction speed. Simulation results show that the proposed algorithm has significant advantages in estimation accuracy and complexity compared to algorithms that also do not require channel sparsity.