Joint Sensing, Compression and Communication for Satellite-Terrestrial Integrated Networks

Satellite-terrestrial integrated network (STIN) has been regarded as a key paradigm in future 6G networks for providing seamless sensing and communication capabilities. In particular, satellites can be leveraged to remotely sense the environment on the earth and deliver the sensing data to the terrestrial base station (BS) for analysis. In this work, we study the joint sensing, compression and communication strategies for STIN, with the objective of maximizing the collected sensing data that can be forwarded to the terrestrial BS. Specifically, we formulate a joint optimization of the sensing rate, compression ratio, CPU frequency and the transmission power of the satellite. We further propose a layered yet efficient algorithm to solve the formulated non-convex joint optimization problem. Numerical results are provided to validate the performance of our proposed joint sensing, compression and communication strategies for STIN.