5G Synchronization, Positioning, and Mapping From Diffuse Multipath

Wen, Fuxi; Wymeersch, Henk

doi:10.1109/lwc.2020.3020116

Cited by 38 publications

(12 citation statements)

References 21 publications

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“…The transition matrix in (39) indicates that the two states q u and q v in the transition process have only two same CVTs in specified place, which will ensure the relativity in the transition process according to the observation space defined in (35).…”

Section: ) Transition Matrixmentioning

confidence: 99%

“…Our framework also includes wake-up positioning and synchronization 1 aided by radio geometrization, which provides an accurate initial position and time synchronization input for cooperative SLAM. Though the works [20,[35][36][37] also consider synchronization under Channel-SLAM framework, the information of reflecting surfaces is not considered together in their synchronization bias estimation process. This also makes it more robust to satellite-signal conditions that typically impede accurate GPS-based localization in real-life situations, such as the presence of high-rise buildings or other obstructions.…”

Section: Introductionmentioning

confidence: 99%

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Joint Vehicular Localization and Reflective Mapping Based on Team Channel-SLAM

Chu¹,

Lu²,

Gesbert³

et al. 2022

Preprint

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This paper addresses high-resolution vehicle positioning and tracking. In recent work, it was shown that a fleet of independent but neighboring vehicles can cooperate for the task of localization by capitalizing on the existence of common surrounding reflectors, using the concept of Team Channel-SLAM. This approach exploits an initial (e.g. GPS-based) vehicle position information and allows subsequent tracking of vehicles by exploiting the shared nature of virtual transmitters associated to the reflecting surfaces. In this paper, we show that the localization can be greatly enhanced by joint sensing and mapping of reflecting surfaces. To this end, we propose a combined approach coined Team Channel-SLAM Evolution (TCSE) which exploits the intertwined relation between (i) the position of virtual transmitters, (ii) the shape of reflecting surfaces, and (iii) the paths described by the radio propagation rays, in order to achieve high-resolution vehicle localization. Overall, TCSE yields a complete picture of the trajectories followed by dominant paths together with a mapping of reflecting surfaces. While joint localization and mapping is a well researched topic within robotics using inputs such as radar and vision, this paper is first to demonstrate such an approach within mobile networking framework based on radio data.

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Section: ) Transition Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Vehicular Localization and Reflective Mapping Based on Team Channel-SLAM

Chu¹,

Lu²,

Gesbert³

et al. 2022

Preprint

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“…Although the specular MPCs are the ones contributing more to the overall channel gain and exploited in the localization process, the diffuse MPCs can also be used for increasing the localization accuracy. Recently, it was demonstrated that the localization of a TN can be conducted even in the absence of the DPCs and specular IPCs [176,177]. Furthermore, it was demonstrated that exploiting the diffuse IPCs improves not only the localization accuracy but also the synchronization [157].…”

Section: ) Exploitation Of Multipath Componentsmentioning

confidence: 99%

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

Najarro¹,

Song²,

Kim³

2021

Preprint

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With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.

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“…Building on this, we present a positioning and mapping method that accurately localizes the user and maps the environment by exploiting the diffuse multipath, rather than considering it as a disturbance. As an extension, [23] provides insights into cases not covered by this paper, namely synchronization and orientation estimation are important in practice. Our main contributions are the following:…”

Section: Introductionmentioning

confidence: 99%

5G Positioning and Mapping With Diffuse Multipath

Wen

Kulmer

Witrisal

et al. 2021

IEEE Trans. Wireless Commun.

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5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensorbased method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach.

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5G Synchronization, Positioning, and Mapping From Diffuse Multipath

Cited by 38 publications

References 21 publications

Joint Vehicular Localization and Reflective Mapping Based on Team Channel-SLAM

Joint Vehicular Localization and Reflective Mapping Based on Team Channel-SLAM

Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

5G Positioning and Mapping With Diffuse Multipath

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