Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation

Symbiotic radio (SR) holds promise for large-scale Internet of Things (IoT) networks by allowing both primary and secondary systems to share spectrum resources for mutually beneficial transmission. However, in high-mobility scenarios, rapidly changing channels pose significant challenges for reliable and low-latency communications. To enable SR transmission over high-mobility channels, this paper explores the secondary transmission and equivalent channel estimation problems in intelligent reflecting surface (IRS)-SR systems utilizing orthogonal time frequency space (OTFS) modulation. The IRS is divided into multiple groups, each attached with informative frequencies, for secondary transmission. Exploiting the spectrum structure induced by the secondary information, a deep learning (DL)-based secondary information detector is proposed. Moreover, after transforming the channel estimation problem into a compressed sensing (CS) problem, a DL-sparse Bayesian learning (SBL) network is proposed to improve the estimation accuracy and ease the computational afford by avoiding the large amount of iterations in conventional CS algorithms, e.g. the SBL algorithm. Finally, numerical results are provided to illustrate the superiority of the proposed detector and estimator over several benchmarks.