Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs) are recognized as key technologies for beyond 5G and 6G wireless communications. To address the high computational complexity and non-convex optimization challenges, this letter proposes an optimization framework ba...
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| Published in | Sensors (Basel, Switzerland) Vol. 25; no. 15; p. 4878 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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| Abstract | Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs) are recognized as key technologies for beyond 5G and 6G wireless communications. To address the high computational complexity and non-convex optimization challenges, this letter proposes an optimization framework based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed framework jointly makes use of sensor grouping, power allocation, an RIS computation strategy, and phase shifts to minimize the remote state estimation (RSE) error. Simulation results demonstrate that the MADDPG algorithm, when applied in an RIS-assisted NOMA system, significantly reduces the RSE error. |
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| AbstractList | Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs) are recognized as key technologies for beyond 5G and 6G wireless communications. To address the high computational complexity and non-convex optimization challenges, this letter proposes an optimization framework based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed framework jointly makes use of sensor grouping, power allocation, an RIS computation strategy, and phase shifts to minimize the remote state estimation (RSE) error. Simulation results demonstrate that the MADDPG algorithm, when applied in an RIS-assisted NOMA system, significantly reduces the RSE error. Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs) are recognized as key technologies for beyond 5G and 6G wireless communications. To address the high computational complexity and non-convex optimization challenges, this letter proposes an optimization framework based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed framework jointly makes use of sensor grouping, power allocation, an RIS computation strategy, and phase shifts to minimize the remote state estimation (RSE) error. Simulation results demonstrate that the MADDPG algorithm, when applied in an RIS-assisted NOMA system, significantly reduces the RSE error.Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs) are recognized as key technologies for beyond 5G and 6G wireless communications. To address the high computational complexity and non-convex optimization challenges, this letter proposes an optimization framework based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed framework jointly makes use of sensor grouping, power allocation, an RIS computation strategy, and phase shifts to minimize the remote state estimation (RSE) error. Simulation results demonstrate that the MADDPG algorithm, when applied in an RIS-assisted NOMA system, significantly reduces the RSE error. |
| Author | Xu, Lei Li, Rongzhen |
| AuthorAffiliation | School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; lirongzhen0213@163.com |
| AuthorAffiliation_xml | – name: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; lirongzhen0213@163.com |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40808042$$D View this record in MEDLINE/PubMed |
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| Keywords | wireless sensor networks RIS uplink NOMA RSE error MADDPG algorithm |
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| References | Shi (ref_17) 2011; 44 Kumar (ref_8) 2025; 19 Sharma (ref_1) 2024; 28 Chai (ref_5) 2024; 72 ref_13 Li (ref_3) 2019; 101 Yu (ref_16) 2023; 22 Li (ref_9) 2024; 23 Peng (ref_2) 2022; 11 Wang (ref_11) 2021; 17 Xu (ref_10) 2025; 68 ref_18 Pang (ref_14) 2023; 22 Rito (ref_4) 2023; 27 ref_15 Wu (ref_12) 2024; 11 Li (ref_7) 2025; 43 Xu (ref_6) 2024; 28 |
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| SubjectTerms | Accuracy Adaptability Decision making Energy efficiency Internet of Things MADDPG algorithm Optimization Parameter estimation RIS RSE error Sensors uplink NOMA wireless sensor networks |
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| Title | Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks |
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