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arXiv · 2510.20363

A Transformer Inspired AI-based MIMO receiver

András Rácz·Tamás Borsos·András Veres·Benedek Csala
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Abstract

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.

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András Rácz, Tamás Borsos, András Veres, Benedek Csala. 2025-10-23. A Transformer Inspired AI-based MIMO receiver. https://arxiv.org/abs/2510.20363

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