Parametric Channel Estimation with Short Pilots in RIS-Assisted Near- and Far-Field Communications

TL;DR

This paper introduces a parametric MLE framework for efficient channel estimation in RIS-assisted systems, reducing pilot overhead in near- and far-field conditions, and includes an adaptive RIS configuration strategy.

Contribution

It develops a novel MLE-based channel estimation method for RIS-assisted systems that works in both near- and far-field scenarios with minimal pilot usage.

Findings

Requires only a few pilots for accurate estimates

Performs well under Rician fading

Enables efficient user channel tracking

Abstract

Considering the dimensionality of a typical reconfigurable intelligent surface (RIS), channel state information acquisition in RIS-assisted systems requires lengthy pilot transmissions. Moreover, the large aperture of the RIS may cause transmitters/receivers to fall in its near-field region, where both distance and angles affect the channel structure. This paper proposes a parametric maximum likelihood estimation (MLE) framework for jointly estimating the direct channel between the user and the base station (BS) and the line-of-sight channel between the user and the RIS, in both far-field and near-field scenarios. The MLE framework is first developed for the case of single-antenna BS and later extended to the scenario where the BS is equipped with multiple antennas. A novel adaptive RIS configuration strategy is proposed to select the RIS configuration for the next pilot to actively refine the estimate. We design a minimal-sized codebook of orthogonal RIS configurations to choose from during pilot transmission with a dimension much smaller than the number of RIS elements. To further reduce the required number of pilots, we propose an initialization strategy with two wide beams. We demonstrate numerically that the proposed MLE method needs only a few pilots for achieving accurate channel estimates and further show that the presented framework performs well under Rician fading. We also showcase efficient user channel tracking in near-field and far-field scenarios.