Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Joint Active and Passive Precoding Design
Not on the map. Why?
Abstract (arXiv)
Millimeter wave (MmWave) communications is capable of supporting multi-gigabit wireless access thanks to its abundant spectrum resource. However, the severe path loss and high directivity make it vulnerable to blockage events, which can be frequent in indoor and dense urban environments. To address this issue, in this paper, we introduce intelligent reflecting surface (IRS) as a new technology to provide effective reflected paths to enhance coverage of mmWave signals. In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver. Our objective is to maximize the received signal power by jointly optimizing the transmit precoding vector at the BS and the phase shift parameters used by IRSs for passive beamforming. Although such an optimization problem is generally non-convex, we show that, by exploiting some important characteristics of mmWave channels, an optimal closed-form solution can be derived for the single IRS case and a near-optimal analytical solution can be obtained for the multi-IRS case. Our analysis reveals that the received signal power increases quadratically with the number of reflecting elements for both the single IRS and multi-IRS cases. Simulation results are included to verify the optimality and near-optimality of our proposed solutions. Results also show that IRSs can help create effective virtual LOS paths and thus substantially improve robustness against blockages in mmWave communications.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Aug 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 4; 2025: 2.
8 papers.
- 2024Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication Systemcited by 2
- 2025DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shiftcited by 2
- 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMAcited by 1
- 2024Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC Systemcited by 1
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approachcited by 0
- 2024Multi-stream Transmission for Directional Modulation Network via Distributed Multi-UAV-aided Multi-active-IRScited by 0
- 2024Optimal Beamforming of RIS-Aided Wireless Communications: An Alternating Inner Product Maximization Approachcited by 0
- 2025Programmable Photonic Unitary Processor Enables Parametrized Differentiable Long-Haul Spatial Division Multiplexed Transmissioncited by 0
- 2025DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift3 Jul 2025
- 2025Programmable Photonic Unitary Processor Enables Parametrized Differentiable Long-Haul Spatial Division Multiplexed Transmission23 May 2025
- 2024Optimal Beamforming of RIS-Aided Wireless Communications: An Alternating Inner Product Maximization Approach10 May 2024
- 2024Multi-stream Transmission for Directional Modulation Network via Distributed Multi-UAV-aided Multi-active-IRS26 Mar 2024
- 2024Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System29 Jan 2024
- 2024Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System29 Jan 2024
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach11 Apr 2023
- 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA3 Apr 2021