RIS-Codes-Collection: A Complete Collection contains the Codes for RIS(IRS) Researches.

Joint Transmit Beamforming and Phase Shifts Design with Deep Reinforcement Learning

IEEE WCNC 2023: Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces

Joint Transmit Beamforming and Phase Shifts Design with Deep Reinforcement Learning Under the Phase-Dependent Amplitude Model

This is the code implementation for the GMML algorithm.

Simulation Codes for "Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach"

Multi-RIS-aided Wireless Systems: Statistical Characterization and Performance Analysis, IEEE Transactions on Communications, Sep. 2021.

OpenRIS is an open-source reconfigurable intelligent surface (RIS) project

Aerial Reconfigurable Intelligent Surface-Aided Wireless Communication Systems, IEEE PIMRC, 2021

A PyTorch implementation of the IEEE WCNC 2025 paper "Worst-Case MSE Minimization for RIS-Assisted mmWave MU-MISO Systems With Hardware Impairments and Imperfect CSI"

Matlab code for "A novel channel model for reconfigurable intelligent surfaces with consideration of polarization and switch impairments"

Closed-Form Global Optimization of Beyond Diagonal Reconfigurable Intelligent Surfaces

Simulation code for "On the Secrecy Rate under Statistical QoS Provisioning for RIS-assisted MISO Wiretap Channel," by V. Kumar, M. F. Flanagan, D. W. Kwan Ng, and L. -N. Tran, IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685957.

Optimization of Transmit Beamforming based on Unsupervised Learning With Channel Covariances for MISO Downlink Assisted by Reconfigurable Intelligent Surfaces

This repository is accompanying the papers "Artificial Fast Fading from Reconfigurable Surfaces Enables Ultra-Reliable Communications" (SPAWC 2021), "RIS-Assisted Statistical Channel Shaping for Ultra-High Reliability" (WSA 2021), and "Reconfigurable Intelligent Surface Phase Hopping for Ultra-Reliable Communications" (TWC, DOI:10.1109/TWC.2022.…

Fusion of federated leaerning algorithm and reconfigurable intelligent surface from 6G

Beyond Diagonal Reconfigurable Intelligent Surfaces Utilizing Graph Theory: Modeling, Architecture Design, and Optimization

Simulation code for “On the Impact of Phase Errors in Phase-Dependent Amplitudes of Near-Field RISs,” by K. Wang, C-T. Lam, and B.K. Ng, et al, IEEE TVT, 2025, doi: 10.1109/TVT.2025.3647594.

This project implements a Deep Q-Network (DQN) for optimizing Reconfigurable Intelligent Surface (RIS) configurations in 6G wireless communication systems. The system uses reinforcement learning to select optimal RIS phase configurations to maximize signal quality and user fairness.

Implementation of the performance evaluation of "On the Impact of Control Signaling in RIS-Empowered Wireless Communications"