As a crucial part of ubiquitous networking and computing (UNC) technologies, low earth orbit (LEO) satellite communications aim at providing internet connectivity services everywhere. To improve the spectrum efficiency of satellite-to-ground communications, adaptive modulation and coding (AMC) are widely used, which can adjust the modulation and coding types according to the varying channel condition. However, satellite-to-ground communication channels have the characterizations such as fast

more »

... amic change, fast switching, and significant fading. These characterizations make it challenging to predict the channel state information accurately and, thus, to perform accurate AMC. For example, rain loss is one of the crucial factors in satellite-to-ground channel fading. In general, it is difficult to build an integrated global model for rain loss because it varies in different regions around the world. Moreover, for the emerging applications of multiple antennas on satellites, the conventional look-up table method cannot cope with the high-dimensional inputs of the multiple antennas. To tackle the above challenges, we propose an AMC method based on deep learning (DL) and deep reinforcement learning (DRL) for ubiquitous satellite-to-ground networks. The proposed method directly processes real-time global weather and location information in the environment and intelligently selects encoding schemes to maximize system throughput. Simulation results show that the proposed method can increase the total throughput. The total number of correctly transmitted bits per unit time is improved, and the efficiency of the satellite-to-ground communication is enhanced.