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Register a new userDoA-LF: A Location Fingerprint Positioning Algorithm with Millimeter-Wave
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Added by
Zhiqing Wei
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Location fingerprint (LF) has been widely applied in indoor positioning. However, the existing studies on LF mostly focus on the fingerprint of WiFi below 6 GHz, bluetooth, ultra wideband (UWB), etc. The LF with millimeter-wave (mmWave) was rarely addressed. Since mmWave has the characteristics of narrow beam, fast signal attenuation and wide bandwidth, etc., the positioning error can be reduced. In this paper, an LF positioning method with mmWave is proposed, which is named as DoA-LF. Besides received signal strength indicator (RSSI) of access points (APs), the fingerprint database contains direction of arrival (DoA) information of APs, which is obtained via DoA estimation. Then the impact of the number of APs, the interval of reference points (RPs), the channel model of mmWave and the error of DoA estimation algorithm on positioning error is analyzed with Cramer-Rao lower bound (CRLB). Finally, the proposed DoA-LF algorithm with mmWave is verified through simulations. The simulation results have proved that mmWave can reduce the positioning error due to the fact that mmWave has larger path loss exponent and smaller variance of shadow fading compared with low frequency signals. Besides, accurate DoA estimation can reduce the positioning error.
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The direction of arrival (DOA) estimation in array signal processing is an important research area. The effectiveness of the direction of arrival greatly determines the performance of multi-input multi-output (MIMO) antenna systems. The multiple signal classification (MUSIC) algorithm, which is the most canonical and widely used subspace-based method, has a moderate estimation performance of DOA. However, in hybrid massive MIMO systems, the received signals at the antennas are not sent to the receiver directly, and spatial covariance matrix, which is essential in MUSIC algorithm, is thus unavailable. Therefore, the spatial covariance matrix reconstruction is required for the application of MUSIC in hybrid massive MIMO systems. In this article, we present a quantum algorithm for MUSIC-based DOA estimation in hybrid massive MIMO systems. Compared with the best-known classical algorithm, our quantum algorithm can achieve an exponential speedup on some parameters and a polynomial speedup on others under some mild conditions. In our scheme, we first present the quantum subroutine for the beam sweeping based spatial covariance matrix reconstruction, where we implement a quantum singular vector transition process to avoid extending the steering vectors matrix into the Hermitian form. Second, a variational quantum density matrix eigensolver (VQDME) is proposed for obtaining signal and noise subspaces, where we design a novel objective function in the form of the trace of density matrices product. Finally, a quantum labeling operation is proposed for the direction of arrival estimation of the signal.
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