# A Transformer Inspired AI-based MIMO receiver

arXiv: https://arxiv.org/abs/2510.20363 · HTML: https://mlsanity.com/paper/2510.20363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Adaptive Neural Signal Detection for Massive MIMO](https://mlsanity.com/paper/1906.04610/) (2019)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations](https://mlsanity.com/paper/2010.16283/) (2020)
- [Graph Neural Networks for Massive MIMO Detection](https://mlsanity.com/paper/2007.05703/) (2020)

## Abstract (arXiv)

We present AttDet, a Transformer-inspired MIMO (Multiple Input Multiple Output) detection method that treats each transmit layer as a token and learns inter-stream interference via a lightweight self-attention mechanism. Queries and keys are derived directly from the estimated channel matrix, so attention scores quantify channel correlation. Values are initialized by matched-filter outputs and iteratively refined. The AttDet design combines model-based interpretability with data-driven flexibility. We demonstrate through link-level simulations under realistic 5G channel models and high-order, mixed QAM modulation and coding schemes, that AttDet can approach near-optimal BER/BLER (Bit Error Rate/Block Error Rate) performance while maintaining predictable, polynomial complexity.