Age of Information in LEO Satellite Communications Supported by BD-RIS

This study focuses on downlink transmissions of a low earth orbit (LEO) satellite, assisted by a beyond diagonal reconfigurable intelligent surface (BD-RIS) to serve ground terminals. Toward optimizing the performance of this system, we formulate the minimization of the average age of information (AoI) achieved at ground terminals. Our formulation respects the power budget of the LEO satellite and guarantees the quality-of-service of ground terminals by optimizing the downlink transmit power at the LEO satellite and reflection coefficients at the BD-RIS as decision variables. Owing to its non-convex and tightly-coupled nature, we reformulate the problem as a Markov decision process which effectively captures its dynamics. Next, a Q-learning propagation (Q-Prop) agent is trained to optimize the decision variables. In light of the mobility of ground terminals as well as LEO satellite, this communication system is highly dynamic. Therefore, we enhance the trained Q-Prop model with meta-learning strategy, which augments its adaptability and generalization to system variances. Numerical results indicate that, in comparison to RIS-lacking and RIS-assisted counterparts, our optimised solution achieves 38% and 26% lower average AoI, respectively.