Joint Optimization of Radio and Computational Resource Allocation in Uplink NOMA-Based Remote State Estimation
In industrial wireless networks beyond 5G and toward 6G, combining uplink non-orthogonal multiple access (NOMA) with the Kalman filter (KF) effectively reduces interruption risks and transmission delays in remote state estimation. However, the complexity of wireless environments and concurrent multi...
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MDPI AG
2025-07-01
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| author | Rongzhen Li Lei Xu |
| author_facet | Rongzhen Li Lei Xu |
| author_sort | Rongzhen Li |
| collection | DOAJ |
| description | In industrial wireless networks beyond 5G and toward 6G, combining uplink non-orthogonal multiple access (NOMA) with the Kalman filter (KF) effectively reduces interruption risks and transmission delays in remote state estimation. However, the complexity of wireless environments and concurrent multi-sensor transmissions introduce significant interference and latency, impairing the KF’s ability to continuously obtain reliable observations. Meanwhile, existing remote state estimation systems typically rely on oversimplified wireless communication models, unable to adequately handle the dynamics and interference in realistic network scenarios. To address these limitations, this paper formulates a novel dynamic wireless resource allocation problem as a mixed-integer nonlinear programming (MINLP) model. By jointly optimizing sensor grouping and power allocation—considering sensor available power and outage probability constraints—the proposed scheme minimizes both estimation outage and transmission delay. Simulation results demonstrate that, compared to conventional approaches, our method significantly improves transmission reliability and KF estimation performance, thus providing robust technical support for remote state estimation in next-generation industrial wireless networks. |
| format | Article |
| id | doaj-art-bd1023d90d0d40f6bc49fcf8b3510677 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Sensors |
| spelling | doaj-art-bd1023d90d0d40f6bc49fcf8b35106772025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-07-012515468610.3390/s25154686Joint Optimization of Radio and Computational Resource Allocation in Uplink NOMA-Based Remote State EstimationRongzhen Li0Lei Xu1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaIn industrial wireless networks beyond 5G and toward 6G, combining uplink non-orthogonal multiple access (NOMA) with the Kalman filter (KF) effectively reduces interruption risks and transmission delays in remote state estimation. However, the complexity of wireless environments and concurrent multi-sensor transmissions introduce significant interference and latency, impairing the KF’s ability to continuously obtain reliable observations. Meanwhile, existing remote state estimation systems typically rely on oversimplified wireless communication models, unable to adequately handle the dynamics and interference in realistic network scenarios. To address these limitations, this paper formulates a novel dynamic wireless resource allocation problem as a mixed-integer nonlinear programming (MINLP) model. By jointly optimizing sensor grouping and power allocation—considering sensor available power and outage probability constraints—the proposed scheme minimizes both estimation outage and transmission delay. Simulation results demonstrate that, compared to conventional approaches, our method significantly improves transmission reliability and KF estimation performance, thus providing robust technical support for remote state estimation in next-generation industrial wireless networks.https://www.mdpi.com/1424-8220/25/15/4686uplink NOMAoutage riskremote state estimationcoalitioin gamedinkelbach methodsuccessive convex approximation method |
| spellingShingle | Rongzhen Li Lei Xu Joint Optimization of Radio and Computational Resource Allocation in Uplink NOMA-Based Remote State Estimation Sensors uplink NOMA outage risk remote state estimation coalitioin game dinkelbach method successive convex approximation method |
| title | Joint Optimization of Radio and Computational Resource Allocation in Uplink NOMA-Based Remote State Estimation |
| title_full | Joint Optimization of Radio and Computational Resource Allocation in Uplink NOMA-Based Remote State Estimation |
| title_fullStr | Joint Optimization of Radio and Computational Resource Allocation in Uplink NOMA-Based Remote State Estimation |
| title_full_unstemmed | Joint Optimization of Radio and Computational Resource Allocation in Uplink NOMA-Based Remote State Estimation |
| title_short | Joint Optimization of Radio and Computational Resource Allocation in Uplink NOMA-Based Remote State Estimation |
| title_sort | joint optimization of radio and computational resource allocation in uplink noma based remote state estimation |
| topic | uplink NOMA outage risk remote state estimation coalitioin game dinkelbach method successive convex approximation method |
| url | https://www.mdpi.com/1424-8220/25/15/4686 |
| work_keys_str_mv | AT rongzhenli jointoptimizationofradioandcomputationalresourceallocationinuplinknomabasedremotestateestimation AT leixu jointoptimizationofradioandcomputationalresourceallocationinuplinknomabasedremotestateestimation |