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5G SLAM with Low-complexity Channel Estimation | IEEE Conference Publication | IEEE Xplore

5G SLAM with Low-complexity Channel Estimation


Abstract:

5G millimeter-wave signals are beneficial for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment. Cha...Show More

Abstract:

5G millimeter-wave signals are beneficial for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment. Channel estimators can exploit received signals to estimate multipath components in terms of delays and angles, which can be used in localization and mapping. Thus, a good channel estimator is essential for 5G SLAM. This paper presents a novel low-complexity multidimensional ESPRIT-based channel estimator and applies it to a 5G SLAM framework. Simulation results demonstrate that the proposed channel estimator can accurately estimate channel information with low computational cost, with limited impact on mapping performance, compared to a tensor-ESPRIT benchmark.
Date of Conference: 22-26 March 2021
Date Added to IEEE Xplore: 27 April 2021
ISBN Information:
Conference Location: Dusseldorf, Germany

Funding Agency:


I. Introduction

5G simultaneous localization and mapping (5G SLAM) utilizes the underlying geometric information of 5G signals to estimate the user location and to create maps of the environment [1]–[5]. 5G cellular systems bring significant advantages to multipath-assisted SLAM due to their large bandwidth and beamforming capability [6], [7]. This means a better resolution in the delay and angular domains, thereby efficiently resolving and identifying multipath components (MPCs) to achieve better positioning and mapping accuracy. An end-to-end 5G SLAM framework was described in [5], which contains downlink data transmission, channel estimation, clustering, and SLAM filtering. It is obvious that the accuracy of the channel estimations directly affects the localization and mapping performance, and the complexity of the channel estimator greatly affects the real-time performance. Therefore, the channel estimator plays a significant role in the 5G SLAM framework, and having a low-complexity and high-accuracy channel estimator is an important problem.

References

References is not available for this document.