# A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints arXiv: https://arxiv.org/abs/2111.06555 ยท HTML: https://mlsanity.com/paper/2111.06555/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 43 references that are in the map. - First posted on arXiv 12 Nov 2021. ## Builds on (most cited first, 30 of 30) - [Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations](https://mlsanity.com/paper/1704.00648/) (2017) - [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020) - [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019) - [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019) - [Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications](https://mlsanity.com/paper/2002.12271/) (2020) - [Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning](https://mlsanity.com/paper/1904.10136/) (2019) - [Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends](https://mlsanity.com/paper/1911.12296/) (2019) - [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019) - [Deep Task-Based Quantization](https://mlsanity.com/paper/1908.06845/) (2019) - [Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-user Communications: Achievable Rates with Limited Discrete Phase Shifts](https://mlsanity.com/paper/1910.14328/) (2019) - [Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1905.07726/) (2019) - [Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface with Discrete Phase Shifts](https://mlsanity.com/paper/1906.03165/) (2019) - [Unsupervised Learning for Passive Beamforming](https://mlsanity.com/paper/2001.02348/) (2020) - [Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation](https://mlsanity.com/paper/2002.11101/) (2020) - [A New Wireless Communication Paradigm through Software-controlled Metasurfaces](https://mlsanity.com/paper/1806.01792/) (2018) - [Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization](https://mlsanity.com/paper/1909.03272/) (2019) - [Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces](https://mlsanity.com/paper/1912.01497/) (2019) - [Multicell MIMO Communications Relying on Intelligent Reflecting Surface](https://mlsanity.com/paper/1907.10864/) (2019) - [Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication](https://mlsanity.com/paper/1910.01573/) (2019) - [Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications](https://mlsanity.com/paper/2008.01459/) (2020) - [Beamforming Optimization for Intelligent Reflecting Surface with Discrete Phase Shifts](https://mlsanity.com/paper/1810.10718/) (2018) - [Channel Estimation and Passive Beamforming for Intelligent Reflecting Surface: Discrete Phase Shift and Progressive Refinement](https://mlsanity.com/paper/1912.10646/) (2019) - [An Interpretable Neural Network for Configuring Programmable Wireless Environments](https://mlsanity.com/paper/1905.02495/) (2019) - [Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks](https://mlsanity.com/paper/1905.07920/) (2019) - [Intelligent Reflecting Surface Assisted Wireless Communication: Modeling and Channel Estimation](https://mlsanity.com/paper/1906.02360/) (2019) - [Intelligent Reflecting Surface Assisted Secrecy Communication: Is Artificial Noise Helpful or Not?](https://mlsanity.com/paper/1907.12839/) (2019) - [Machine Learning Empowered Beam Management for Intelligent Reflecting Surface Assisted MmWave Networks](https://mlsanity.com/paper/2003.01306/) (2020) - [Joint Training of the Superimposed Direct and Reflected Links in Reconfigurable Intelligent Surface Assisted Multiuser Communications](https://mlsanity.com/paper/2105.14484/) (2021) - [Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks](https://mlsanity.com/paper/2008.12938/) (2020) - [Robust and Secure Sum-Rate Maximization for Multiuser MISO Downlink Systems with Self-sustainable IRS](https://mlsanity.com/paper/2101.10549/) (2021) ## Built on by (most cited first, 3 of 3) - [Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems](https://mlsanity.com/paper/2205.08788/) (2022) - [Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS](https://mlsanity.com/paper/2409.11270/) (2024) - [GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising](https://mlsanity.com/paper/2511.06663/) (2025) ## Abstract (arXiv) Reconfigurable intelligent surface (RIS) has become a promising technology to improve wireless communication in recent years. It steers the incident signals to create a favorable propagation environment by controlling the reconfigurable passive elements with less hardware cost and lower power consumption. In this paper, we consider a RIS-aided multiuser multiple-input single-output downlink communication system. We aim to maximize the weighted sum-rate of all users by joint optimizing the active beamforming at the access point and the passive beamforming vector of the RIS elements. Unlike most existing works, we consider the more practical situation with the discrete phase shifts and imperfect channel state information (CSI). Specifically, for the situation that the discrete phase shifts and perfect CSI are considered, we first develop a deep quantization neural network (DQNN) to simultaneously design the active and passive beamforming while most reported works design them alternatively. Then, we propose an improved structure (I-DQNN) based on DQNN to simplify the parameters decision process when the control bits of each RIS element are greater than 1 bit. Finally, we extend the two proposed DQNN-based algorithms to the case that the discrete phase shifts and imperfect CSI are considered simultaneously. Our simulation results show that the two DQNN-based algorithms have better performance than traditional algorithms in the perfect CSI case, and are also more robust in the imperfect CSI case.