Deep Learning-Empowered RF Sensing in Outdoor Environments: Recent Advances, Challenges, and Future Directions
Year: 2024
Type: article
Abstract: Recently, with advancements in Deep Learning (DL) technology, Radio Frequency (RF) sensing has seen substantial improvements, particularly in outdoor applications. Motivated by these developments, this survey presents a comprehensive review of state-of-the-art RF sensing techniques in challenging outdoor scenarios with practical issues such as fading, interference, and environmental dynamics. We first investigate the characteristics of outdoor environments and explore potential wireless technologies. Then, we study the current trends in applying DL to RF-based systems and highlight its advantages in dealing with large-scale and dynamic outdoor environments. Furthermore, this paper provides a detailed comparison between discriminative and generative DL models in support of RF sensing, offering insights into both the theoretical underpinnings and practical applications of these technologies. Finally, we discuss the research challenges and present future directions of leveraging DL in outdoor RF sensing.
Source: Electronics
Authors: Duc Minh Quang Nguyen, William D. Lukito, Xuemeng Liu, Chang Liu
Institutions: La Trobe University, The University of Sydney
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4405945926