# Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing

arXiv: https://arxiv.org/abs/2208.09287 · HTML: https://mlsanity.com/paper/2208.09287/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks](https://mlsanity.com/paper/1810.07181/) (2018)
- [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)
- [RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection](https://mlsanity.com/paper/2110.02219/) (2021)
- [Deep-Unfolding Neural-Network Aided Hybrid Beamforming Based on Symbol-Error Probability Minimization](https://mlsanity.com/paper/2109.06574/) (2021)
- [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)
- [Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach](https://mlsanity.com/paper/1903.07831/) (2019)
- [Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming](https://mlsanity.com/paper/2206.03755/) (2022)

## Built on by (most cited first, 4 of 4)

- [Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG](https://mlsanity.com/paper/2403.02651/) (2024)
- [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)

The limited over-the-air (OTA) pilot symbols in multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems presents a major challenge for detecting transmitted data symbols at the receiver, especially for machine learning-based approaches. While it is crucial to explore effective ways to exploit pilots, one can also take advantage of the data symbols to improve detection performance. Thus, this paper introduces an online attention-based approach, namely RC-AttStructNet-DF, that can efficiently utilize pilot symbols and be dynamically updated with the detected payload data using the decision feedback (DF) mechanism. Reservoir computing (RC) is employed in the time domain network to facilitate efficient online training. The frequency domain network adopts the novel 2D multi-head attention (MHA) module to capture the time and frequency correlations, and the structural-based StructNet to facilitate the DF mechanism. The attention loss is designed to learn the frequency domain network. The DF mechanism further enhances detection performance by dynamically tracking the channel changes through detected data symbols. The effectiveness of the RC-AttStructNet-DF approach is demonstrated through extensive experiments in MIMO-OFDM and massive MIMO-OFDM systems with different modulation orders and under various scenarios.