Related papers: Radio Frequency Fingerprinting via Deep Learning: …

The application of machine learning in wireless communications has been extensively explored, with deep unfolding emerging as a powerful model-based technique. Deep unfolding enhances interpretability by transforming complex iterative…

As the scale and complexity of integrated circuits continue to increase, traditional modeling methods are struggling to address the nonlinear challenges in radio frequency (RF) chips. Deep learning has been increasingly applied to RF device…

Radio frequency (RF) fingerprinting techniques provide a promising supplement to cryptography-based approaches but rely on dedicated equipment to capture in-phase and quadrature (IQ) samples, hindering their wide adoption. Recent advances…

In recent years, the rapid growth of the Internet of Things technologies and the widespread adoption of 5G wireless networks have led to an exponential increase in the number of radiation devices operating in complex electromagnetic…

The deep learning (DL) technology has been widely used for image classification in many scenarios, e.g., face recognition and suspect tracking. Such a highly commercialized application has given rise to intellectual property protection of…

Radio frequency (RF) fingerprint technology is utilized for wireless device identification, extensively employed in the internet of things (IoT). The operating environment for IoT devices is challenging, with pervasive noise and distortion…

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be…

The intrinsic hardware imperfection of WiFi chipsets manifests itself in the transmitted signal, leading to a unique radiometric fingerprint. This fingerprint can be used as an additional means of authentication to enhance security. In…

This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend…

The study identifies a clear evolution from traditional methods to more advanced machine learning approaches. Current algorithms face persistent challenges, including degraded image quality, damaged ridge structures, and background noise,…

Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation…

RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a potential solution for automated network access authentication. Traditional approaches are commonly susceptible to the domain adaptation…

The rapid proliferation of wireless devices makes robust identity authentication essential. Radio Frequency Fingerprinting (RFF) exploits device-specific, hard-to-forge physical-layer impairments for identification, and is promising for IoT…

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio-frequency device by analyzing the signal feature differences caused by the inevitable minor…

Radio frequency fingerprint (RFF) identification technology, which exploits relatively stable hardware imperfections, is highly susceptible to constantly changing channel effects. Although various channel-robust RFF feature extraction…

The identification of the devices from which a message is received is part of security mechanisms to ensure authentication in wireless communications. Conventional authentication approaches are cryptography-based, which, however, are…

Radio frequency (RF)-based indoor localization offers significant promise for applications such as indoor navigation, augmented reality, and pervasive computing. While deep learning has greatly enhanced localization accuracy and robustness,…

With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been…

Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate. There are several implementation issues in the OWC which…

Conventional methods for outdoor environment reconstruction rely predominantly on vision-based techniques like photogrammetry and LiDAR, facing limitations such as constrained coverage, susceptibility to environmental conditions, and high…