# Over-the-Air mmWave MIMO Dataset

Soft demapping in mmWave MIMO receivers no longer treats each symbol on its own. Prior learned demappers used multilayer perceptrons on single symbols, while classical demappers rely on analytical decision rules and a Gaussian noise assumption, so neither exploits residual distortion structure across neighboring symbols. This work applies a one-dimensional convolutional network to whole blocks of equalized symbols; on over-the-air data from a 60 GHz, four-stream, 1.76 GHz-per-stream link, the single-stream variant reaches up to 2.6× BER gain over the conventional soft demapper for 16QAM and up to 17.6× for 64QAM, and a fixed-point RFSoC implementation runs about 34.3× faster.

Soft demapping in mmWave MIMO receivers no longer treats each symbol on its own. Prior learned demappers used multilayer perceptrons on single symbols, while classical demappers rely on analytical decision rules and a Gaussian noise assumption, so neither exploits residual distortion structure across neighboring symbols. This work applies a one-dimensional convolutional network to whole blocks of equalized symbols; on over-the-air data from a 60 GHz, four-stream, 1.76 GHz-per-stream link, the single-stream variant reaches up to 2.6× BER gain over the conventional soft demapper for 16QAM and up to 17.6× for 64QAM, and a fixed-point RFSoC implementation runs about 34.3× faster.