Least-squares based iterative multipath super-resolution technique

TL;DR

This paper introduces the LIMS algorithm, a new iterative least-squares method for multipath channel estimation that outperforms traditional techniques like MUSIC and ESPRIT, especially in challenging scenarios with limited data or correlated paths.

Contribution

The paper presents a novel iterative least-squares based super-resolution algorithm that improves multipath resolution and tracking capabilities over existing methods.

Findings

LIMS can resolve closely arriving multipaths with lower mean square error.

LIMS outperforms conventional techniques in critical scenarios.

Suitable for recursive multipath tracking.

Abstract

In this paper, we study the problem of multipath channel estimation for direct sequence spread spectrum signals. To resolve multipath components arriving within a short interval, we propose a new algorithm called the least-squares based iterative multipath super-resolution (LIMS). Compared to conventional super-resolution techniques, such as the multiple signal classification (MUSIC) and the estimation of signal parameters via rotation invariance techniques (ESPRIT), our algorithm has several appealing features. In particular, even in critical situations where the conventional super-resolution techniques are not very powerful due to limited data or the correlation between path coefficients, the LIMS algorithm can produce successful results. In addition, due to its iterative nature, the LIMS algorithm is suitable for recursive multipath tracking, whereas the conventional super-resolution techniques may not be. Through numerical simulations, we show that the LIMS algorithm can resolve the first arrival path among closely arriving independently faded multipaths with a much lower mean square error than can conventional early-late discriminator based techniques.