Sum rate maximization in downlink HAP-RSMA-based THz systems: A generative diffusion model enabled RL approach

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dc.contributor.authorLe, Maien
dc.contributor.authorPham, Quoc-Vieten
dc.contributor.authorO'Sullivan, Barryen
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.funderResearch Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2025-12-18T09:34:42Z
dc.date.available2025-12-18T09:34:42Z
dc.date.issued2025en
dc.description.abstractThis 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.en
dc.description.sponsorshipResearch Ireland (Grant number 12-RC-2289-P2; CHIST-ERA SHIELD project Project No. 216449, Award No. 19226)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLe, 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.en
dc.identifier.endpage6en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/18352
dc.language.isoenen
dc.relation.ispartofIEEE Global Communications Conference, Taipei, Taiwan, 8-12 December 2025en
dc.relation.projectinfo:eu-repo/grantAgreement/SNSF/Science communication::Scientific Exchanges/216449/CH/Toward a Future Worth Wanting (Contesting Computer-Anthropologies)/en
dc.rights© 2025, the Authors. For the purpose of Open Access, a CC BY licence applies to any Author Accepted Manuscript from this submission.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectDeep learningen
dc.subjectGenerative diffusion modelen
dc.subjectHigh altitude platformen
dc.subjectResource allocationen
dc.subjectRate spilling multiple accessen
dc.subjectReinforcement learningen
dc.subjectTHz communicationsen
dc.titleSum rate maximization in downlink HAP-RSMA-based THz systems: A generative diffusion model enabled RL approachen
dc.typeConference itemen
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