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.

In wireless networks, we study the downlink scenario of a non-orthogonal multiple access (NOMA) network based on scalable video coding techniques. The resource allocation problem based on wireless NOMA network is modeled as a mixed-integer nonlinear programming problem, which is subject to video coding characteristics, sub-channel capacity, system available power, and real-time requirements of wireless network. In order to solve the resource allocation problem of wireless NOMA network, three sub-problems are proposed, e.g., user grouping sub-problem, power allocation sub-problem, and video layer allocation sub-problem. Firstly, we propose an auction-based solution for the user grouping sub-problem. Then, for the power allocation sub-problem, we choose successive convex approximation and Lagrangian dual decomposition method. Finally, for the video layer allocation sub-problem, we propose a greedy strategy. Simulation experiments show that our proposed algorithms ensure the real-time and reliability requirements of the wireless network and also improve the quality of the video experience.