Bayesian Learning-Aided Channel Estimators for Superimposed-Pilot-Based OTFS Systems

Orthogonal time-frequency space (OTFS) transmission is a promising multi-carrier modulation scheme for high-mobility communication scenarios. We consider a novel multiple frame-based superimposed pilot (SP)-OTFS system, which exhibits two-dimensional (2-D) delay-Doppler domain channel sparsity. We design a multiple-frame-based coupled prior sparse Bayesian learning (M-CPSBL) algorithm to exploit the 2-D channel sparsity while estimating it. This algorithm, however, inverts a large-dimensional matrix in each iteration with a high complexity. We next design its low-complexity version, which works on a smaller dimensional problem. The two proposed algorithms are used for SP-OTFS systems, which superpose low-powered pilots on to data signals. The combination of M-CPSBL and SP-OTFS frameworks is shown to outperform existing OTFS systems in terms of channel estimation normalized mean squared error, and spectral efficiency (SE). We derive a closed-form expression to optimize pilot and data powers to maximize the signal-to-interference-plus-noise ratio of SP-OTFS systems. We numerically show that optimal power allocation reduces the bit error rate, and increases the SE.