# RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training

arXiv: https://arxiv.org/abs/2003.06923 · HTML: https://mlsanity.com/paper/2003.06923/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection](https://mlsanity.com/paper/2110.02219/) (2021)
- [Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing](https://mlsanity.com/paper/2208.09287/) (2022)
- [Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing](https://mlsanity.com/paper/2310.04956/) (2023)
- [Invariant Transformation and Resampling based Epistemic-Uncertainty Reduction](https://mlsanity.com/paper/2602.23315/) (2026)
- [Dual-Attention Based 3D Channel Estimation](https://mlsanity.com/paper/2604.01769/) (2026)
- [Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing](https://mlsanity.com/paper/2102.03688/) (2021)
- [Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMO](https://mlsanity.com/paper/2102.09322/) (2021)
- [Universal Approximation of Linear Time-Invariant (LTI) Systems through RNNs: Power of Randomness in Reservoir Computing](https://mlsanity.com/paper/2308.02464/) (2023)
- [2D-RC: Two-Dimensional Neural Network Approach for OTFS Symbol Detection](https://mlsanity.com/paper/2311.08543/) (2023)
- [When Learning Hurts: Fixed-Pole RNN for Real-Time Online Training](https://mlsanity.com/paper/2602.21454/) (2026)

## Abstract (arXiv)

In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC). We first introduce the Time-Frequency RC to take advantage of the structural information inherent in OFDM signals. Using the time domain RC and the time-frequency RC as the building blocks, we provide two extensions of the shallow RC to RCNet: 1) Stacking multiple time domain RCs; 2) Stacking multiple time-frequency RCs into a deep structure. The combination of RNN dynamics, the time-frequency structure of MIMO-OFDM signals, and the deep network enables RCNet to handle the interference and nonlinear distortion of MIMO-OFDM signals to outperform existing methods. Unlike most existing NN-based detection strategies, RCNet is also shown to provide a good generalization performance even with a limited training set (i.e, similar amount of reference signals/training as standard model-based approaches). Numerical experiments demonstrate that the introduced RCNet can offer a faster learning convergence and as much as 20% gain in bit error rate over a shallow RC structure by compensating for the nonlinear distortion of the MIMO-OFDM signal, such as due to power amplifier compression in the transmitter or due to finite quantization resolution in the receiver.