Uplink RSMA in LEO Satellite Communications: A Perspective From Generative Artificial Intelligence

Consider an uplink rate splitting multiple access (RSMA) transmission in low earth orbit (LEO) terrestrial-satellite networks, by jointly optimizing the user access, transmit power splitting and receive beamforming. Despite advantages of deep reinforcement learning in online optimization, some issues still remain, i.e., the hybrid nature of discrete and continuous actions, low sample quality, and intractable representation of irregular environments. To circumvent these issues, we first formulate a parameterized Markov decision process (PAMDP) to represent the environment evolution, by parameterizing each discrete action as a continuous real vector, upon which the hybrid proximal policy optimization (PPO) is applied. Then, generative diffusion models (GDMs) are embedded into hybrid PPO to output the optimal action distribution, by adding the pure Gaussian noise with states into denoising networks. Experiments results corroborate that GDMs-assisted hybrid PPO achieves larger rewards with stability and faster convergence. Further, the proposed method dominates other benchmarks, under different terminal numbers, transmit power budgets and noise levels.