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- Publisher Website: 10.1109/JSAC.2021.3126079
- Scopus: eid_2-s2.0-85121876822
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Article: Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks
Title | Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks |
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Authors | |
Keywords | Distributed optimization MADRL MIMO-NOMA Reconfigurable intelligent surface THz |
Issue Date | 2022 |
Citation | IEEE Journal on Selected Areas in Communications, 2022, v. 40, n. 1, p. 259-275 How to Cite? |
Abstract | With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed data rate and massive connectivity requirements. Nevertheless, the unreliability of THz transmissions and the extreme heterogeneity of device requirements pose critical challenges for practical applications. To address these challenges, we propose a novel smart reconfigurable THz MIMO-NOMA framework, which can realize customizable and intelligent communications by flexibly and coordinately reconfiguring hybrid beams through the cooperation between access points (APs) and reconfigurable intelligent surfaces (RISs). The optimization problem is formulated as a decentralized partially-observable Markov decision process (Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the diversified users' performance, via a joint RIS element selection, coordinated discrete phase-shift control, and power allocation strategy. To solve the above non-convex, strongly coupled, and highly complex mixed integer nonlinear programming (MINLP) problem, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm, namely graph-embedded value-decomposition actor-critic (GE-VDAC), that embeds the interaction information of agents, and learns a locally optimal solution through a distributed policy. Numerical results demonstrate that the proposed algorithm achieves highly customized communications and outperforms traditional MADRL algorithms. |
Persistent Identifier | http://hdl.handle.net/10722/349655 |
ISSN | 2023 Impact Factor: 13.8 2023 SCImago Journal Rankings: 8.707 |
DC Field | Value | Language |
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dc.contributor.author | Xu, Xiaoxia | - |
dc.contributor.author | Chen, Qimei | - |
dc.contributor.author | Mu, Xidong | - |
dc.contributor.author | Liu, Yuanwei | - |
dc.contributor.author | Jiang, Hao | - |
dc.date.accessioned | 2024-10-17T06:59:59Z | - |
dc.date.available | 2024-10-17T06:59:59Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | IEEE Journal on Selected Areas in Communications, 2022, v. 40, n. 1, p. 259-275 | - |
dc.identifier.issn | 0733-8716 | - |
dc.identifier.uri | http://hdl.handle.net/10722/349655 | - |
dc.description.abstract | With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed data rate and massive connectivity requirements. Nevertheless, the unreliability of THz transmissions and the extreme heterogeneity of device requirements pose critical challenges for practical applications. To address these challenges, we propose a novel smart reconfigurable THz MIMO-NOMA framework, which can realize customizable and intelligent communications by flexibly and coordinately reconfiguring hybrid beams through the cooperation between access points (APs) and reconfigurable intelligent surfaces (RISs). The optimization problem is formulated as a decentralized partially-observable Markov decision process (Dec-POMDP) to maximize the network energy efficiency, while guaranteeing the diversified users' performance, via a joint RIS element selection, coordinated discrete phase-shift control, and power allocation strategy. To solve the above non-convex, strongly coupled, and highly complex mixed integer nonlinear programming (MINLP) problem, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm, namely graph-embedded value-decomposition actor-critic (GE-VDAC), that embeds the interaction information of agents, and learns a locally optimal solution through a distributed policy. Numerical results demonstrate that the proposed algorithm achieves highly customized communications and outperforms traditional MADRL algorithms. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Journal on Selected Areas in Communications | - |
dc.subject | Distributed optimization | - |
dc.subject | MADRL | - |
dc.subject | MIMO-NOMA | - |
dc.subject | Reconfigurable intelligent surface | - |
dc.subject | THz | - |
dc.title | Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JSAC.2021.3126079 | - |
dc.identifier.scopus | eid_2-s2.0-85121876822 | - |
dc.identifier.volume | 40 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 259 | - |
dc.identifier.epage | 275 | - |
dc.identifier.eissn | 1558-0008 | - |