Work

Secure beamforming via machine learning-driven joint optimization in IRS-assisted wireless systems

Year: 2025

Type: article

Abstract: This paper proposes a Machine Learning-enhanced Gradient-Method Based Linear Optimization (ML-GMBLO) framework for secure beamforming in intelligent reflecting surface (IRS)-assisted 6G wireless networks. The method jointly optimizes beamforming vectors, artificial noise, IRS phase shifts, and user associations through an iterative process. A hybrid offline–online architecture is employed, where a deep neural network (DNN) trained on representative channel features provides near-optimal initializations. This reduces convergence iterations by approximately 40% compared to Alternating Optimization with Semi-Definite Programming (AO-SDP) under identical 4-bit phase quantization. Under the same channel estimation errors and Doppler conditions, ML-GMBLO achieves around 30% higher secrecy rate (5.2 vs. 4.0 bps/Hz), 35% lower latency, and near-linear scalability with respect to IRS size. This integrated approach enhances physical layer security while maintaining compatibility with hardware constraints.

Source: ICT Express

DOI: 10.1016/j.icte.2025.10.014

Authors: Sivasankar S., Markkandan S.

Institution: Vellore Institute of Technology University

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500