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With the rapid development of Internet of Things (IoT), low earth orbit (LEO)satellite IoT is expected to provide low power, massive connectivity and widecoverage IoT applications. In this context, this paper provides a massivegrant-free random access (GF-RA) scheme for LEO satellite IoT. This scheme doesnot need to change the transceiver, but transforms the received signal to atensor decomposition form. By exploiting the characteristics of the tensorstructure, a Bayesian learning algorithm for joint active device detection andchannel estimation during massive GF-RA is designed. Theoretical analysis showsthat the proposed algorithm has fast convergence and low complexity. Finally,extensive simulation results confirm its better performance in terms of errorprobability for active device detection and normalized mean square error forchannel estimation over baseline algorithms in LEO satellite IoT. Especially,it is found that the proposed algorithm requires short preamble sequences andsupport massive connectivity with a low power, which is appealing to LEOsatellite IoT.

Exploiting Tensor-based Bayesian Learning for Massive Grant-Free Random Access in LEO Satellite Internet of Things