# AI-Driven Real-Time Phase Optimization for Energy Harvesting-Enabled Dual-IRS Cooperative NOMA Under Non-Line-of-Sight Conditions

## TL;DR

This paper proposes an AI-based system to improve energy efficiency and communication reliability in wireless networks using smart surfaces and energy harvesting.

## Contribution

A lightweight machine learning model for real-time phase optimization in dual-IRS C-NOMA systems under NLoS conditions.

## Key findings

- The proposed AI model achieves low-complexity phase shift optimization for dual IRSs.
- Integration of energy harvesting and relay-based communication improves spectral efficiency and service reliability.
- Numerical results show significant performance gains over conventional systems.

## Abstract

In this paper, a wireless network architecture is considered that combines double intelligent reflecting surfaces (IRSs), energy harvesting (EH), and non-orthogonal multiple access (NOMA) with cooperative relaying (C-NOMA) to leverage the performance of non-line-of-sight (NLoS) communication mainly and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase shift configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behavior and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication compared with conventional systems, thereby facilitating green communication systems.