A Transformer Inspired AI-based MIMO receiver

• URL: http://arxiv.org/abs/2510.20363v1
• Date: Thu, 23 Oct 2025 09:05:10 GMT
• Title: A Transformer Inspired AI-based MIMO receiver
• Authors: András Rácz, Tamás Borsos, András Veres, Benedek Csala,
• Abstract summary: The AttDet design combines model-based interpretability with data-driven flexibility.<n>We demonstrate through link-level simulations under 5G channel models and high-order, mixed QAM modulation and coding schemes.<n>AttDet can approach near-optimal BER/BLER performance while maintaining predictable, realistic complexity.
• Score: 0.5039813366558306
• License: http://creativecommons.org/licenses/by/4.0/
• 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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