High-mobility communications, which are crucial for next-generation wireless systems, cause the orthogonal frequency division multiplexing (OFDM) waveform to suffer from strong intercarrier interference (ICI) due to the Doppler effect. In this work, we propose a novel receiver architecture for OFDM that leverages the angular domain to separate multipaths. A block-type pilot is sent to estimate direction-of-arrivals (DoAs), propagation delays, and channel gains of the multipaths. Subsequently, a decision-directed (DD) approach is employed to estimate and iteratively refine the Dopplers. Two different approaches are investigated to provide initial Doppler estimates: an error vector magnitude (EVM)-based method and a deep learning (DL)-based method. Simulation results reveal that the DL-based approach allows for constant bit error rate (BER) performance up to the maximum 6G speed of 1000 km/h.
@article{arxiv.2601.12970,
title = {6G OFDM Communications with High Mobility Transceivers and Scatterers via Angle-Domain Processing and Deep Learning},
author = {Mauro Marchese and Musa Furkan Keskin and Henk Wymeersch and Pietro Savazzi},
journal= {arXiv preprint arXiv:2601.12970},
year = {2026}
}
Comments
Accepted for presentation at IEEE International Conference on Communications (ICC) 2026