Practical Deep Learning-Based Block Demapping for mmWave MIMO Systems

arXiv:2611.11629v1 Announce Type: new Abstract: Deep learning is expected to play a central role in 6G systems, by enabling data-driven physical layer designs that can adapt to practical wireless impairments. This is particularly important for millimetre-wave (mmWave) multiple-input multiple-output (MIMO) systems, which are strongly affected by residual channel distortions, interference, and hardware non-idealities that are difficult to capture with analytical models. In this paper, we propose a deep learning-based soft demapper that performs block-wise processing of received symbols instead of conventional symbol-wise detection.