A Low-Complexity Joint Fractional Delay and Doppler Frequency Estimator for AFDM-Enabled Vehicular LEO-ICAN Systems

TL;DR

This paper introduces a low-complexity joint fractional delay and Doppler frequency estimator for AFDM waveforms, optimized for high-mobility LEO-ICAN systems, achieving near-optimal accuracy with reduced computational load.

Contribution

It exploits the spectrum-wrapping-induced envelope structure of AFDM responses to develop a joint estimator that balances high accuracy with low complexity.

Findings

Approaches the root Cramér--Rao lower bound (RCRLB) in accuracy.

Achieves RMSE comparable to matched filtering methods.

Requires significantly less computational complexity and runtime.

Abstract

Low-Earth-orbit (LEO) satellites and vehicle-to-everything (V2X) networks are driving integrated communication and navigation (ICAN) toward next-generation intelligent transportation. Affine frequency division multiplexing (AFDM) is a promising waveform for high-mobility LEO scenarios owing to its Doppler robustness, simple modulation, and low pilot overhead. However, applying existing high-accuracy AFDM fractional delay-Doppler estimators to LEO-ICAN entails substantial search or inference complexity, while the spectrum-wrapping-induced envelope structure in line-of-sight (LOS)-dominated channels remains underexploited. This paper analyzes and exploits the spectrum-wrapping-induced envelope structure of the fractional AFDM response, and proposes a low-complexity joint estimator that combines minimum-entropy fractional Doppler estimation with closed-form fractional delay estimation. Simulation results show that the proposed estimator approaches the root Cram\'er--Rao lower bound (RCRLB) and achieves root-mean-square error (RMSE) performance comparable to that of matched filtering (MF), matched filtering with generalized Fibonacci search (MF-GFS), and off-grid sparse Bayesian learning (OG-SBL), while requiring substantially lower computational complexity and runtime. This favorable accuracy-complexity profile highlights the potential of the proposed estimator for real-time ICAN processing in high-mobility LEO-assisted vehicular networks.