Complex Bayesian Compressive Sensing of Channel Estimation for OTFS Modulation

Orthogonal time frequency space (OTFS) modulation can meet the challenges in high mobility environment. The signal detection for OTFS system is necessary, so that the precise channel state information to improve detection accuracy is essential. Therefore, we propose a complex Bayesian compressive sensing (CBCS) algorithm, which utilizes the sparsity of the channel. We first model the OTFS signal by employing a hierarchical Laplace prior. Then, based on marginal likelihood of the sparse signal, we separate the current sparse hyperparameter and derive CBCS iterative updating expressions. Thirdly, we optimize processing the key parameter operations per iteration to promote computational efficiency. The simulation results showcase the CBCS scheme achieves higher accuracy in channel estimation compared with other algorithms.