Symbiotic Radio With Orthogonal Time Frequency Space Modulation Over High-Mobility Channels

Symbiotic radio (SR) offers potential benefits for large-scale Internet of Things networks through its spectrum and resource-sharing capabilities between primary and secondary systems. However, in high-mobility scenarios, rapidly changing channels present significant challenges for reliable and low-latency SR communications. In response to this challenge, this paper proposes employing orthogonal time-frequency space (OTFS) modulation for SR systems in hostile environments. By processing signals in the delay-Doppler (DD) domain, the fast-varying channels can be efficiently characterized using a few quasi-static DD domain parameters. To enable joint primary and secondary transmissions in SR, we propose modulating primary information using phase-shift keying modulation, while the secondary information is modulated into the frequencies of the periodic rectangular wave. However, the time-varying secondary signals are intertwined with unknown DD-domain channels, making it difficult to differentiate between secondary information and the original channel state information (CSI). To overcome this challenge, we introduce coherent detection methods for secondary information using both amplitude-based and sparsity-based techniques, leveraging the spectral characteristics of signal combinations with different frequencies. Moreover, recognizing that the periodic rectangular wave of the secondary transmission would reshape the Doppler profile of the equivalent channel in a highly structured manner, we propose an off-grid structured-sparse Bayesian learning-based CSI estimator. With the obtained equivalent CSI, we propose a low-complexity symbol-wise detection algorithm for detecting primary information, leveraging the pilot guards and interference cancellation technique. Finally, numerical results validate the effectiveness and superiority of the proposed estimator and detector.