Millimeter-wave based localization using a two-stage channel estimation relying on few-bit ADCs

Abstract

Benefiting from the high resolution in beamspace, millimeter wave (mmwave) communication has been regarded as a high-accuracy localization solution, where the location information is embedded in the channel via angle and time delay, for example. In this paper, to locate a user equipment (UE) and scatterers, we present the localization model in mmwave communications as a compressed sensing assisted channel estimation problem, which is solved using a proposed two-stage channel estimation based localization scheme. During the first stage, a sparse Bayesian learning (SBL) algorithm is operated to attain a coarse estimation. Then during the second stage, a multi-stage grid refinement assisted fine estimation is achieved by a distributed compressed sensing simultaneous orthogonal matching pursuit (DCS-SOMP) algorithm. Moreover, in our approach, the few-bit analog to digital converters (ADCs) are utilized by the receiver of UE so as to attain a good trade-off among performance, complexity and energy-efficiency. Finally, the performance of channel estimation and positioning is comprehensively investigated and compared. It can be shown that our proposed two-stage approach is capable of achieving centimeter-level accuracy with the required number of quantization bits of ADCs less than four.

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Identifiers

ORCID for Kunlun Li: orcid.org/0000-0002-5797-6560

ORCID for Mohammed El-Hajjar: orcid.org/0000-0002-7987-1401

ORCID for Lie-Liang Yang: orcid.org/0000-0002-2032-9327

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