Toward Intelligent Non-Terrestrial Networks through Symbiotic Radio: A Collaborative Deep Reinforcement Learning Scheme

With the merit of global coverage, non-terrestrial networks (NTNs) with low-earth-orbit (LEO) satellite constellations have been regarded as a promising innovation to support ubiquitous connectivity. However, due to the high orbital velocity, each LEO satellite can only service a different set of ground terminals (GTs) for few minutes, and thus it is difficult to utilize machine learning (ML) based and non-ML based algorithms to tackle high-dimensional resource optimizations within such a short dwell duration. To tackle this challenge, an effective solution lies in constructing collaborations between multiple LEO satellites and GTs to jointly address the resource optimization. In this article, motivated by the mutualism advantage in symbiotic radios, we therefore propose a novel collaborative learning scheme for implementing intelligent NTNs, in which each GT with powerful computing capability operates a ground-tier learning agent to assist LEO satellites tackling GT resource allocation tasks. Each LEO satellite also operates as a space-tier learning agent and the learning model of the LEO satellite can be transferred to its successor satellite as a starting point to continue updating the learning model. Finally, comprehensive simulations are conducted with a Walker-Delta satellite constellation and a channel model composed of free-space path-loss and Rician fading, and the simulation results show that the proposed scheme outperforms DRL benchmarks with more than 5.9% improvements toward the optimal average overall throughput performance over a sufficient number of elevation angle samples from the interval of [31°, 90°].