# Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network

arXiv: https://arxiv.org/abs/2201.02834 · HTML: https://mlsanity.com/paper/2201.02834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 62 references that are in the map.
- First posted on arXiv 8 Jan 2022.

## Builds on (most cited first, 30 of 32)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2004.05843/) (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)
- [Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays](https://mlsanity.com/paper/1902.07678/) (2019)
- [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019)
- [Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO](https://mlsanity.com/paper/1905.07948/) (2019)
- [A New Wireless Communication Paradigm through Software-controlled Metasurfaces](https://mlsanity.com/paper/1806.01792/) (2018)
- [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)
- [A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network](https://mlsanity.com/paper/2002.03744/) (2020)
- [A Framework of Robust Transmission Design for IRS-aided MISO Communications with Imperfect Cascaded Channels](https://mlsanity.com/paper/2001.07054/) (2020)
- [Intelligent Reflecting Surface Assisted Wireless Communication: Modeling and Channel Estimation](https://mlsanity.com/paper/1906.02360/) (2019)
- [Reconfigurable Intelligent Surface Aided NOMA Networks](https://mlsanity.com/paper/1912.10044/) (2019)
- [Weighted Sum-Rate Maximization for Multi-IRS Aided Cooperative Transmission](https://mlsanity.com/paper/2002.04900/) (2020)
- [Wireless 2.0: Towards an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence](https://mlsanity.com/paper/2002.11040/) (2020)
- [Residual Neural Networks for Digital Predistortion](https://mlsanity.com/paper/2005.05655/) (2020)
- [Learning Reflection Beamforming Codebooks for Arbitrary RIS and Non-Stationary Channels](https://mlsanity.com/paper/2109.14909/) (2021)
- [Intelligent Reflecting Surface for Downlink Non-Orthogonal Multiple Access Networks](https://mlsanity.com/paper/1906.09434/) (2019)
- [Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems](https://mlsanity.com/paper/1909.04606/) (2019)
- [IRS-Enhanced Wideband MU-MISO-OFDM Communication Systems](https://mlsanity.com/paper/1909.11314/) (2019)
- [Spatially-Stationary Model for Holographic MIMO Small-Scale Fading](https://mlsanity.com/paper/1911.04853/) (2019)
- [Reconfigurable Intelligent Surface for MISO Systems with Proportional Rate Constraints](https://mlsanity.com/paper/2001.10845/) (2020)
- [Reconfigurable Intelligent Surfaces-Enabled Vehicular Networks: A Physical Layer Security Perspective](https://mlsanity.com/paper/2004.11288/) (2020)
- [On Spatial Multiplexing Using Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.07064/) (2020)

## Abstract (arXiv)

Reconfigurable intelligent surface (RIS) is an emerging technology for future wireless communication systems. In this work, we consider downlink spatial multiplexing enabled by the RIS for weighted sum-rate (WSR) maximization. In the literature, most solutions use alternating gradient-based optimization, which has moderate performance, high complexity, and limited scalability. We propose to apply a fully convolutional network (FCN) to solve this problem, which was originally designed for semantic segmentation of images. The rectangular shape of the RIS and the spatial correlation of channels with adjacent RIS antennas due to the short distance between them encourage us to apply it for the RIS configuration. We design a set of channel features that includes both cascaded channels via the RIS and the direct channel. In the base station (BS), the differentiable minimum mean squared error (MMSE) precoder is used for pretraining and the weighted minimum mean squared error (WMMSE) precoder is then applied for fine-tuning, which is nondifferentiable, more complex, but achieves a better performance. Evaluation results show that the proposed solution has higher performance and allows for a faster evaluation than the baselines. Hence it scales better to a large number of antennas, advancing the RIS one step closer to practical deployment.