Sum rate maximization in downlink HAP-RSMA-based THz systems: A generative diffusion model enabled RL approach
Loading...
Files
Accepted Version
Date
2025
Authors
Le, Mai
Pham, Quoc-Viet
O’Sullivan, Barry
Nguyen, Hoang D.
Journal Title
Journal ISSN
Volume Title
Publisher
Published Version
Abstract
This paper investigates the maximization of the achievable rate for users served by a high-altitude platform (HAP) acting as a flying base station in the downlink of ratesplitting multiple access (RSMA)-based terahertz (THz) communication systems. Considering the dynamic and uncertain environment caused by user mobility and molecular absorption effects, we propose a generative diffusion model (DM)-based deep reinforcement learning approach to address this challenge. The problem is formulated as a Markov decision process, aiming to maximize the long-term achievable rate for all users by jointly optimizing power allocation and the common rate splitting ratio. Moreover, the generative DM significantly improves the decisionmaking capabilities of a deep reinforcement learning algorithm, namely the deep deterministic policy gradient (DDPG). Experimental simulations demonstrate the effectiveness of the proposed DM-DDPG algorithm compared to alternative schemes.
Description
Keywords
Deep learning , Generative diffusion model , High altitude platform , Resource allocation , Rate spilling multiple access , Reinforcement learning , THz communications
Citation
Le, M., Pham, Q.-V., O’Sullivan, B. and Nguyen, H. D. (2025) 'Sum rate maximization in downlink HAP-RSMA-based THz systems: A generative diffusion model enabled RL approach', IEEE Global Communications Conference, Taipei, Taiwan, 8-12 December 2025.