# Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing

arXiv: https://arxiv.org/abs/2102.03688 · HTML: https://mlsanity.com/paper/2102.03688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 6 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G](https://mlsanity.com/paper/1907.07862/) (2019)
- [Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning](https://mlsanity.com/paper/1904.10136/) (2019)
- [Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays](https://mlsanity.com/paper/1902.07678/) (2019)
- [RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training](https://mlsanity.com/paper/2003.06923/) (2020)
- [Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling](https://mlsanity.com/paper/1911.03359/) (2019)
- [Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions](https://mlsanity.com/paper/2006.03377/) (2020)
- [Communication Through a Large Reflecting Surface With Phase Errors](https://mlsanity.com/paper/1906.10751/) (2019)

## Abstract (arXiv)

This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems. By modeling radio-frequency (RF) impairments inside the "meta-atoms" of IRS (including nonlinearity and memory effects), we present an approach that generalizes the entire IRS-aided system as a reservoir computing (RC) system, an efficient recurrent neural network (RNN) operating in a state near the "edge of chaos". This framework enables us to take advantage of the nonlinearity of this "fabricated" wireless environment to overcome link degradation due to model mismatch. Accordingly, the randomness of the wireless channel and RF imperfections are naturally embedded into the RC framework, enabling the internal RC dynamics lying on the edge of chaos. Furthermore, several practical issues, such as channel state information acquisition, passive beamforming design, and physical layer reference signal design, are discussed.