Ax-Csi

Gringoli, Francesco; Cominelli, Marco; Blanco, Alejandro; Widmer, Joerg

doi:10.1145/3477086.3480833

Cited by 17 publications

(1 citation statement)

References 80 publications

(98 reference statements)

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“…CSI measured in WLANs was typically not reported to the access points and could not be directly retrieved by the user, so this information was not available for further use. In the last decade, however, commercially available chipsets [27] have been upgraded with an API to extract the estimated CSI of a wireless link due to advantages that CSI offered over RSSI for various wireless sensing applications [28] and to enable advanced signal processing techniques for optimised WLAN performance.…”

Section: Equipment Characteristicsmentioning

confidence: 99%

System Architecture for Intelligent Environment-Aware Wireless Communications

Morano,

Švigelj,

Hrovat

et al. 2023

Advances in Digital Transformation - Rise of Ultra-Smart Fully Automated Cyberspace

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The integration of communications and wireless sensing using the same frequency spectrum is likely to be one of the key features beyond 5G wireless systems, enabling digital transformation to its full potential. Therefore, future wireless communication networks will have to become intelligent, meaning environment-aware, adaptive and parsimonious in terms of radio resources and energy. In this chapter, we propose a system architecture that enables the introduction of intelligent environment-aware wireless communications. Special attention has been paid to the description of the wireless channel in the database with a set of radio rays, generated by the calibrated ray tracing using the ray bouncing method. The proposed approach enables straightforward calculation of the channel impulse response enriched with information about the angle of arrival and angle of departure for the band-limited multiple antenna wireless communication systems. The proposed architecture introduces the concept of local and global agents, used for retrieving associated parameters from the user requirements and the existing radio channel characteristics data.

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Section: Equipment Characteristicsmentioning

confidence: 99%

System Architecture for Intelligent Environment-Aware Wireless Communications

Morano,

Švigelj,

Hrovat

et al. 2023

Advances in Digital Transformation - Rise of Ultra-Smart Fully Automated Cyberspace

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Review of few-shot learning application in CSI human sensing

Wang,

Li,

Wang

et al. 2024

Artif Intell Rev

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Wi-Fi sensing has garnered increasing interest for its significant advantages, primarily leveraging Wi-Fi signal fluctuations induced by human activities and advanced neural network algorithms. However, its application faces challenges due to limited generalizability, necessitating frequent data recollection and neural network retraining for adaptation to new environments. To address these limitations, some researchers introduced few-shot learning into Wi-Fi sensing applications because it offers a promising solution with its ability to achieve remarkable performance in novel scenarios using minimal training samples. Despite its potential, a comprehensive review of its applications within this domain remains absent. This study endeavors to fill this gap by exploring prominent Wi-Fi sensing applications that incorporate few-shot learning, aiming to delineate their key features. We categorize few-shot learning approaches into three distinct methodologies: transfer learning, metric learning, and meta-learning, based on their neural network training strategies. Through this classification, we examine representative systems from an application perspective and elucidate the principles of few-shot learning implementation. These systems are evaluated in terms of learning methodology, data modality, and recognition accuracy. Finally, this paper highlights the challenges and future directions for few-shot learning in Channel State Information (CSI) based human sensing, providing a valuable resource for researchers in the field of Wi-Fi human sensing leveraging few-shot learning.

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