Low Time Complexity Near-Field Channel and Position Estimations

TL;DR

This paper introduces a low time complexity joint estimation scheme for near-field channel and position estimation, effectively decoupling AoA and CoA, and demonstrating improved accuracy and efficiency in high-frequency XL-MIMO systems.

Contribution

It proposes the JAC scheme and two algorithms, JAC-ISF and JAC-GD, to decouple AoA and CoA estimation in near-field scenarios, enhancing performance and reducing computational time.

Findings

JAC scheme effectively decouples AoA and CoA in near-field estimation.

JAC-GD algorithm outperforms existing methods in accuracy across various conditions.

Proposed algorithms reduce time overhead while maintaining high estimation precision.

Abstract

With the application of high-frequency communication and extremely large MIMO (XL-MIMO), the near-field effect has become increasingly apparent. The near-field channel estimation and position estimation problems both rely on the Angle of Arrival (AoA) and the Curvature of Arrival (CoA) estimation. However, in the near-field channel model, the coupling of AoA and CoA information poses a challenge to the estimation of the near-field channel. This paper proposes a Joint Autocorrelation and Cross-correlation (JAC) scheme to decouple AoA and CoA estimation. Based on the JAC scheme, we propose two specific near-field estimation algorithms, namely Inverse Sinc Function (JAC-ISF) and Gradient Descent (JAC-GD) algorithms. Finally, we analyzed the time complexity of the JAC scheme and the cramer-rao lower bound (CRLB) for near-field position estimation. The simulation experiment results show that the algorithm designed based on JAC scheme can solve the problem of coupled CoA and AoA information in near-field estimation, thereby improving the algorithm performance. The JAC-GD algorithm shows significant performance in channel estimation and position estimation at different SNRs, snapshot points, and communication distances compared to other algorithms. This indicates that the JAC-GD algorithm can achieve more accurate channel and position estimation results while saving time overhead.