A Transformer Inspired AI-based MIMO receiver
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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.
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References · 6
- 2017Attention Is All You Needcited by 60,361
- 2018Learning to Detectcited by 63
- 2019Adaptive Neural Signal Detection for Massive MIMOcited by 27
- 2017Deep MIMO Detectioncited by 26
- 2020DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformationscited by 14
- 2020Graph Neural Networks for Massive MIMO Detectioncited by 9
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