Channel Estimation and Detection for Symbiotic Radio Systems Over High-Mobility Channels

In symbiotic radio (SR), the secondary system not only shares the spectrum and power of the primary system but also enhances its performance by providing multipath gains, fostering a cooperative mutualism between the two systems. However, in high-mobility channels, time-frequency selective fading presents significant challenges for reliable SR communications. The recently introduced orthogonal time-frequency space (OTFS) technique, which processes signals in the delay-Doppler (DD) domain, is expected to improve SR communication performance in high-speed mobile scenarios. In this paper, we propose embedding primary information symbols in the DD domain using amplitude-phase modulation, while employing a combinatorial frequency (CF) modulation strategy for secondary information transmission. To obtain channel state information (CSI) and detect secondary symbols, for some special scenarios, we propose an off-grid sparse Bayesian learning (SBL)-based method. This method first estimates the equivalent CSI and then detects the symbols by leveraging the highly structured Doppler shifts. For more general scenarios, we introduce a model-driven equivalent CSI estimation-net (ECSIEst-Net) and a data-driven secondary symbol detection-Net (SSymDet-Net). Numerical results are provided to guide parameter selection and demonstrate the effectiveness of the proposed methods.