Application of Quantum Computing to Accurate Positioning in 6G Indoor Scenarios

Urgelles, Helen; Picazo-Martinez, Pablo; Monserrat, José F.

doi:10.1109/icc45855.2022.9838523

Cited by 12 publications

(2 citation statements)

References 12 publications

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“…Realtime tracking of people and objects indoors will be possible, which will be beneficial in a variety of applications such as asset tracking, interior navigation, and security monitoring. This work [251] proposed a case study in a 6G scenario with a large number of devices and AP where an Euclidean distance on the quantum version is exploited to analyze the performance and explore new horizons in this field.…”

Section: B Future Directionsmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

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Indoor localization (IL) is a significant topic of study with several practical applications, particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods within IoT environments. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, the survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of IoT-related industries, such as evacuationegress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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Section: B Future Directionsmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj,

Himeur,

Sohail

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Real-time tracking of people and objects indoors will be possible, which will be beneficial in a variety of applications such as asset tracking, interior navigation, and security monitoring. This work [234] proposed a case study in a 6G scenario with a large number of devices and an AP where an euclidean distance on the quantum version is exploited to analyze the performance and explore new horizons in this field.…”

Section: Future Directionsmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj¹,

Himeur²,

Sohail³

et al. 2023

Preprint

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Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

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In‐Network Quantum Computing for Future 6G Networks

Urgelles,

Maheshwari,

Nande

et al. 2024

Adv Quantum Tech

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In light of the imperative for expeditious data processing and enhanced global connectivity, the domain of communication technology is experiencing a rapid progression from the Fifth Generation (5G) to the forthcoming Sixth Generation (6G) within the research community. Furthermore, 6G promises to significantly augment the synergy between the human, digital, and physical realms, thereby necessitating the formulation of novel Key Performance Indicators (KPIs) as well as Key Values Indicators (KVIs), and the assimilation of commensurate technologies. Among these technologies, quantum computing is evolving rapidly due to its inherent advantages from quantum mechanics. Nevertheless, scant attention is directed toward a comprehensive exploration of the consequences attendant to its present‐day application. The principal objective of this article resides in its endeavor to underscore, from a compensatory perspective, the convergence of 6G and quantum computing while concurrently considering the Sustainable Development Goals and its KVIs.

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Application of Quantum Computing to Accurate Positioning in 6G Indoor Scenarios

Cited by 12 publications

References 12 publications

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

In‐Network Quantum Computing for Future 6G Networks

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