Sparse Bayesian estimation and signal recovery of fractional channels in OTFS systems

In high-speed mobile scenarios, fractional delay and fractional Doppler effects are pervasive, disrupting the inherent sparse structure of the Orthogonal Time Frequency Space (OTFS) system in the delay-Doppler (DD) domain. This leads to the spreading of channel response energy, which not only significantly increases the complexity of channel estimation(CE) algorithms, but also reduces the accuracy of CE. In response to this issue, a two-stage CE scheme is proposed based on Sparse Adaptive Matching Pursuit (SAMP) and Off-Grid Sparse Bayesian Learning (OGSBL) algorithms in this paper. In the signal recovery stage, a doubly fractional CE model is established. On this basis, sparse signals are recovered by using the Sparsity Adaptive Matching Pursuit (SAMP) algorithm to achieve rapid channel path location and preliminary estimation, thus making more effective use of the sparsity of the DD domain channel. In the Bayesian optimization stage, the recovered signal is used as the initial input, the CE is further optimized by SBL. Meanwhile, to effectively mitigate the system’s Peak-to-Average Power Ratio (PAPR), Zadoff–Chu (ZC) sequences are incorporated into the pilot structure design. Numerical simulations were conducted to compare the PAPR and Normalized Mean Square Error (NMSE) performance across various pilot patterns, confirming the efficacy of the proposed approach. Simulation results demonstrate that the suggested approach enhances CE precision and speeds up the algorithm’s convergence.