Time and Frequency Offset Estimation and Intercarrier Interference Cancellation for AFDM Systems

TL;DR

This paper introduces ML estimators for time and frequency offsets in AFDM systems, utilizing inherent signal properties to improve synchronization without extra pilots, and proposes a scheme to reduce intercarrier interference.

Contribution

It presents novel ML estimators for AFDM synchronization that exploit chirp-periodic structures, and introduces a mirror-mapping scheme to mitigate residual interference.

Findings

Estimators accurately determine offsets without additional pilots.

Mirror-mapping effectively reduces intercarrier interference.

Numerical results confirm the proposed methods' effectiveness.

Abstract

Affine frequency division multiplexing (AFDM) is an emerging multicarrier waveform that offers a potential solution for achieving reliable communications over time-varying channels. This paper proposes two maximum-likelihood (ML) estimators of symbol time offset and carrier frequency offset for AFDM systems. One is called joint ML estimator, which evaluates the arrival time and carrier frequency offset by comparing the correlations of samples. Moreover, we propose the other so-called stepwise ML estimator to reduce the complexity. Both proposed estimators exploit the redundant information contained within the chirp-periodic prefix inherent in AFDM symbols, thus dispensing with any additional pilots. To further mitigate the intercarrier interference resulting from the residual frequency offset, we design a mirror-mapping-based scheme for AFDM systems. Numerical results verify the effectiveness of the proposed time and carrier frequency offset estimation criteria and the mirror-mapping-based modulation for AFDM systems.