Yang, S., Xie, C., Lyu, W., Ning, B., Zhang, Z., & Yuen, C. (2024). Near-Field Channel Estimation for Extremely Large-Scale Reconfigurable Intelligent Surface (XL-RIS)-Aided Wideband mmWave Systems. IEEE Journal on Selected Areas in Communications, 42(6), 1567–1582. https://doi.org/10.1109/jsac.2024.3389120
Abstract:
Near-field communications present new opportunities
over near-field channels, however, the spherical wavefront
propagation makes near-field signal processing challenging.
In this context, this paper proposes efficient near-field channel
estimation methods for wideband MIMO mmWave systems with
the aid of extremely large-scale reconfigurable intelligent surfaces
(XL-RIS). For the wideband signals reflected by the analog RIS,
we characterize their near-field beam squint effect in both angle
and distance domains. Based on the mathematical analysis of
the near-field beam patterns over all frequencies, a wideband
spherical-domain dictionary is constructed by minimizing the
coherence of two arbitrary beams. In light of this, we formulate
a two-dimensional compressive sensing problem to recover
the channel parameter based on the spherical-domain sparsity
of mmWave channels. To this end, we present a correlation
coefficient-based atom matching method within our proposed
multi-frequency parallelizable subspace recovery framework for
efficient solutions. Additionally, we propose a two-dimensional
oracle estimator as a benchmark and derive its lower bound
across all subcarriers. Our findings emphasize the significance
of system hyperparameters and the sensing matrix of each
subcarrier in determining the accuracy of the estimation. Finally,
numerical results show that our proposed method achieves
considerable performance compared with the lower bound and
has a time complexity linear to the number of RIS elements.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Key Research and Development Program of China - NA
Grant Reference no. : 2023YFB4503002
This research / project is supported by the Key Research and Development Program of Zhejian - NA
Grant Reference no. : 2024SSYS0094
This research / project is supported by the Ministry of Education, Singapore - Academic Research Fund Tier 2
Grant Reference no. : Award MOE-T2EP50220-0019
This research / project is supported by the Science and Engineering Research Council of A*STAR (Agency for Science, Technology and Research) Singapore - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M22L1b0110