6G OFDM Communications with High Mobility Transceivers and Scatterers via Angle-Domain Processing and Deep Learning

TL;DR

This paper introduces a novel OFDM receiver architecture for high-mobility 6G communications that uses angle-domain processing and deep learning to effectively estimate Dopplers and mitigate intercarrier interference, ensuring reliable performance at extreme speeds.

Contribution

It proposes a new receiver design combining angle-domain multipath separation with deep learning-based Doppler estimation for high-mobility OFDM systems.

Findings

DL-based Doppler estimation maintains constant BER up to 1000 km/h.

Angle-domain processing effectively separates multipaths in high-mobility scenarios.

The proposed approach outperforms EVM-based methods in dynamic environments.

Abstract

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.