Coupled Prior-Based Sparse Bayesian Channel Estimation for Superimposed Pilot OTFS Systems

We propose a novel coupled prior-based sparse Bayesian framework for superimposed pilot (SP)-aided OTFS systems. The SP-OTFS system, wherein low-powered pilot signals are superimposed on to data signals, reduces the large pilot overhead, which is common in the existing OTFS systems. In the proposed coupled prior, sparsity of each channel gain is controlled not only by its own sparsity, but by also that of all other channel gains. OTFS channel has an inherent two-dimensional sparsity which can be exploited to increase its estimation accuracy. Using the proposed prior, we design a multiple received frame-based coupled prior sparse Bayesian learning (M-CPSBL) OTFS channel estimation algorithm, which is shown to outperform its state-of-the-art counterparts.