# RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection

arXiv: https://arxiv.org/abs/2110.02219 · HTML: https://mlsanity.com/paper/2110.02219/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Adaptive Neural Signal Detection for Massive MIMO](https://mlsanity.com/paper/1906.04610/) (2019)
- [Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G](https://mlsanity.com/paper/1907.07862/) (2019)
- [Neural Network Detection of Data Sequences in Communication Systems](https://mlsanity.com/paper/1802.02046/) (2018)
- [Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks](https://mlsanity.com/paper/1810.07181/) (2018)
- [Deep HyperNetwork-Based MIMO Detection](https://mlsanity.com/paper/2002.02750/) (2020)
- [RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training](https://mlsanity.com/paper/2003.06923/) (2020)
- [Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems](https://mlsanity.com/paper/2008.03977/) (2020)
- [Deep Neural Network Symbol Detection for Millimeter Wave Communications](https://mlsanity.com/paper/1907.11294/) (2019)
- [Learning for Integer-Constrained Optimization through Neural Networks with Limited Training](https://mlsanity.com/paper/2011.05399/) (2020)
- [Learning to Equalize OTFS](https://mlsanity.com/paper/2107.08236/) (2021)
- [Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection](https://mlsanity.com/paper/2012.00711/) (2020)

## Built on by (most cited first, 6 of 6)

- [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)
- [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)
- [Neural Network-based Two-Dimensional Filtering for OTFS Symbol Detection](https://mlsanity.com/paper/2406.16868/) (2024)
- [When Learning Hurts: Fixed-Pole RNN for Real-Time Online Training](https://mlsanity.com/paper/2602.21454/) (2026)

## Abstract (arXiv)

In this paper, we introduce a structure-based neural network architecture, namely RC-Struct, for MIMO-OFDM symbol detection. The RC-Struct exploits the temporal structure of the MIMO-OFDM signals through reservoir computing (RC). A binary classifier leverages the repetitive constellation structure in the system to perform multi-class detection. The incorporation of RC allows the RC-Struct to be learned in a purely online fashion with extremely limited pilot symbols in each OFDM subframe. The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity. Experiments show that the introduced RC-Struct outperforms both the conventional model-based symbol detection approaches and the state-of-the-art learning-based strategies in terms of bit error rate (BER). The advantages of RC-Struct over existing methods become more significant when rank and link adaptation are adopted. The introduced RC-Struct sheds light on combining communication domain knowledge and learning-based receive processing for 5G/5G-Advanced and Beyond.