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

## TL;DR

This paper introduces a new framework using MADDPG to optimize RIS-assisted NOMA systems for better remote state estimation in wireless sensor networks.

## Contribution

A novel MADDPG-based optimization framework for RIS-assisted NOMA to reduce remote state estimation error.

## Key findings

- The MADDPG algorithm effectively reduces remote state estimation error in RIS-assisted NOMA systems.
- Joint optimization of sensor grouping, power allocation, and RIS strategies improves system performance.
- Simulation results validate the effectiveness of the proposed framework.

## 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.