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Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks.

Rongzhen Li¹, Lei Xu¹

¹School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This study introduces a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework for reconfigurable intelligent surfaces (RISs) and non-orthogonal multiple access (NOMA) systems. The proposed method significantly reduces remote state estimation error in advanced wireless communications.

Keywords:

MADDPG algorithm RIS RSE error uplink NOMA wireless sensor networks

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Area of Science:

Wireless Communications
Artificial Intelligence
Signal Processing

Background:

Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs) are critical for future wireless networks (beyond 5G, 6G).
High computational complexity and non-convex optimization hinder the efficient integration of NOMA and RIS.
Minimizing remote state estimation (RSE) error is crucial for effective system performance.

Purpose of the Study:

To propose an optimization framework to address the challenges in RIS-assisted NOMA systems.
To minimize the remote state estimation (RSE) error by jointly optimizing system parameters.
To leverage advanced AI techniques for efficient resource management in next-generation wireless networks.

Main Methods:

Development of an optimization framework utilizing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm.
Joint optimization of sensor grouping, power allocation, RIS computation strategy, and phase shifts.
Application of MADDPG for intelligent resource management in RIS-assisted NOMA systems.

Main Results:

The proposed MADDPG-based framework effectively reduces computational complexity and tackles non-convex optimization problems.
Significant reduction in remote state estimation (RSE) error demonstrated through simulations.
The framework shows superior performance in RIS-assisted NOMA systems compared to conventional methods.

Conclusions:

The MADDPG algorithm provides an efficient solution for optimizing RIS-assisted NOMA systems.
The proposed framework enhances the performance of future wireless communication systems by minimizing RSE error.
This research contributes to the advancement of beyond 5G and 6G technologies through intelligent resource allocation.