Localization in reconfigurable intelligent surface aided mmWave systems: a multiple measurement vector based channel estimation method
Localization in reconfigurable intelligent surface aided mmWave systems: a multiple measurement vector based channel estimation method
The sparsity of millimeter wave (mmWave) channels in the angular and temporal domains is beneficial to channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage poses a significant challenge on the localization in mmWave systems, potentially leading to substantial positioning errors. A promising solution is to employ reconfigurable intelligent surface (RIS) to generate the virtual line-of-sight (VLoS) paths to aid localization. Consequently, wireless localization in the RIS assisted mmWave systems has become the essential research issue. In this paper, a multiple measurement vector (MMV) model is constructed and a two-stage channel estimation based localization scheme is proposed. During the first stage, by exploiting the beamspace sparsity and employing a random RIS phase shift matrix, the channel parameters are estimated, based on which the precoder at base station and combiner at user equipment (UE) are designed. Then, in the second stage, based on the designed precoding and combining matrices, the optimal phase shift matrix for RIS is designed using the proposed modified temporally correlated multiple sparse Bayesian learning (TMSBL) algorithm. Afterwards, the channel parameters, such as angle of reflection, time-of-arrival, etc., embedding location information are estimated for finally deriving the location of UE. We demonstrate the achievable performance of the proposed algorithm and compare it with the state-of-the-art algorithms. Our studies show that the proposed localization scheme is capable of achieving centimeter level localization accuracy, when LoS path is blocked. Furthermore, the proposed algorithm has a low computational complexity and outperforms the legacy algorithms in different perspectives.
Channel estimation, Location awareness, Matching pursuit algorithms, Millimeter wave communication, OFDM, Radio frequency, SBL algorithm, Sparse matrices, channel estimation, localization/positioning, mmWave, reconfigurable intelligent surfaces
13326-13339
Li, Kunlun
6cb29fc3-c9d5-474b-ad21-a8fb79dc47ae
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
He, Jiguang
bc174f31-a646-42a7-bd7e-4988b54c2543
24 April 2024
Li, Kunlun
6cb29fc3-c9d5-474b-ad21-a8fb79dc47ae
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Yang, Lie-Liang
ae425648-d9a3-4b7d-8abd-b3cfea375bc7
He, Jiguang
bc174f31-a646-42a7-bd7e-4988b54c2543
Li, Kunlun, El-Hajjar, Mohammed, Yang, Lie-Liang and He, Jiguang
(2024)
Localization in reconfigurable intelligent surface aided mmWave systems: a multiple measurement vector based channel estimation method.
IEEE Transactions on Vehicular Technology, 73 (9), .
(doi:10.1109/TVT.2024.3393022).
Abstract
The sparsity of millimeter wave (mmWave) channels in the angular and temporal domains is beneficial to channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage poses a significant challenge on the localization in mmWave systems, potentially leading to substantial positioning errors. A promising solution is to employ reconfigurable intelligent surface (RIS) to generate the virtual line-of-sight (VLoS) paths to aid localization. Consequently, wireless localization in the RIS assisted mmWave systems has become the essential research issue. In this paper, a multiple measurement vector (MMV) model is constructed and a two-stage channel estimation based localization scheme is proposed. During the first stage, by exploiting the beamspace sparsity and employing a random RIS phase shift matrix, the channel parameters are estimated, based on which the precoder at base station and combiner at user equipment (UE) are designed. Then, in the second stage, based on the designed precoding and combining matrices, the optimal phase shift matrix for RIS is designed using the proposed modified temporally correlated multiple sparse Bayesian learning (TMSBL) algorithm. Afterwards, the channel parameters, such as angle of reflection, time-of-arrival, etc., embedding location information are estimated for finally deriving the location of UE. We demonstrate the achievable performance of the proposed algorithm and compare it with the state-of-the-art algorithms. Our studies show that the proposed localization scheme is capable of achieving centimeter level localization accuracy, when LoS path is blocked. Furthermore, the proposed algorithm has a low computational complexity and outperforms the legacy algorithms in different perspectives.
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paper
- Accepted Manuscript
More information
Accepted/In Press date: 15 April 2024
e-pub ahead of print date: 24 April 2024
Published date: 24 April 2024
Additional Information:
Publisher Copyright:
IEEE
Keywords:
Channel estimation, Location awareness, Matching pursuit algorithms, Millimeter wave communication, OFDM, Radio frequency, SBL algorithm, Sparse matrices, channel estimation, localization/positioning, mmWave, reconfigurable intelligent surfaces
Identifiers
Local EPrints ID: 489286
URI: http://eprints.soton.ac.uk/id/eprint/489286
ISSN: 0018-9545
PURE UUID: 55d8043a-6f18-4d55-9824-76a2144d396f
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Date deposited: 19 Apr 2024 16:33
Last modified: 12 Dec 2024 05:01
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