# Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming

arXiv: https://arxiv.org/abs/1810.03961 · HTML: https://mlsanity.com/paper/1810.03961/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2018.

## Built on by (most cited first, 30 of 46)

- [Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels](https://mlsanity.com/paper/2112.13603/) (2021)
- [Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning](https://mlsanity.com/paper/2207.08057/) (2022)
- [Differentially Private Federated Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2203.17028/) (2022)
- [Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning](https://mlsanity.com/paper/2111.04971/) (2021)
- [Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces](https://mlsanity.com/paper/2104.00075/) (2021)
- [Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System](https://mlsanity.com/paper/2402.09441/) (2024)
- [Channel Estimation and Hybrid Architectures for RIS-Assisted Communications](https://mlsanity.com/paper/2104.07115/) (2021)
- [Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2203.14152/) (2022)
- [Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems](https://mlsanity.com/paper/2205.08788/) (2022)
- [Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and Imperfect CSI](https://mlsanity.com/paper/2211.09702/) (2022)
- [Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers](https://mlsanity.com/paper/2301.03371/) (2022)
- [Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2305.10931/) (2023)
- [A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks](https://mlsanity.com/paper/2410.12320/) (2024)
- [DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation](https://mlsanity.com/paper/2410.14481/) (2024)
- [Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems](https://mlsanity.com/paper/2506.22448/) (2025)
- [BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding](https://mlsanity.com/paper/2603.18855/) (2026)
- [AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation](https://mlsanity.com/paper/2606.08173/) (2026)
- [Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing](https://mlsanity.com/paper/2102.03688/) (2021)
- [Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet](https://mlsanity.com/paper/2109.07729/) (2021)
- [DEFORM: A Practical, Universal Deep Beamforming System](https://mlsanity.com/paper/2203.09727/) (2022)
- [Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods](https://mlsanity.com/paper/2204.13372/) (2022)
- [Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications](https://mlsanity.com/paper/2205.01647/) (2022)
- [RIS-ADMM: A RIS and ADMM-Based Passive and Sparse Sensing Method With Interference Removal](https://mlsanity.com/paper/2206.06172/) (2022)
- [Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments](https://mlsanity.com/paper/2211.09726/) (2022)
- [Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System](https://mlsanity.com/paper/2212.12744/) (2022)
- [Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2304.12184/) (2023)
- [Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach](https://mlsanity.com/paper/2308.13616/) (2023)
- [HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers](https://mlsanity.com/paper/2401.05420/) (2023)
- [Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2401.12149/) (2024)
- [Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2402.06663/) (2024)

## Abstract (arXiv)

Intelligent reflecting surface (IRS) is envisioned to be a new and revolutionizing technology for achieving spectrum and energy efficient wireless communication networks cost-effectively in the future. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and/or interference suppression at designated receivers. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to multiple single-antenna users. As such, each user receives the superposed signals from the AP as well as the IRS via its reflection. We aim to minimize the total transmit power at the AP by jointly optimizing the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the IRS, subject to users' individual signal-to-interference-plus-noise ratio (SINR) constraints. However, the formulated problem is non-convex and difficult to be solved optimally.