Radio frequency (RF) fingerprinting exploits hardware imperfections for device identification, but distinguishing between same-model devices remains challenging due to their minimal hardware variations. Existing datasets for RF fingerprinting are constrained by small device scales and heterogeneous models, which hinder robust training and fair evaluation of machine learning methods. To address this gap, we introduce a large-scale dataset of same-model devices along with an open-source experimental framework. The dataset is built using 123 same-model commercial IEEE 802.11 g devices, which contain 35.42 million raw I/Q samples from the preambles and corresponding 1.85 million RF features. The accompanying framework further provides a fully reproducible pipeline from data collection to performance evaluation. Within this framework, a Random Forest–based algorithm is implemented as a baseline to achieve 88.6% identification accuracy on this dataset.

Radio frequency fingerprinting (RFF) is a technique that identifies wireless devices by exploiting unique hardware-induced imperfections measured in their transmitted RF signals. We propose a lightweight RFF method by leveraging temporal variations in RF signals. By combining multi-scale feature extraction through coarse and fine segmentation with DTW-based similarity features, our approach achieves an accuracy of 0.9882 and a Macro-F1 score of 0.988 in our experiment using the Wi-Fi RF data collected from 120 devices.

This paper demonstrates that thefractal dimension of frame preambleserves as a new radiometric feature that can be used together with other known radiometric features to enhance the identification accuracy in wireless device identification. We first propose a fractal dimension estimation scheme for direct-sequence spread spectrum (DSSS) frame preamble, then provide theoretical analysis to reveal how the fractal dimension is primarily determined by the device hardware imperfections, and thus prove that the fractal dimension serves as an intrinsic radiometric feature. We further show simulation results to verify our theoretical modeling of the fractal dimension and also numerically evaluate the effects of device hardware imperfections and wireless channels on the fractal dimension. Finally, by jointly applying the fractal dimension and the five features reported in the literature, we conduct extensive experiments to demonstrate that the fractal dimension can lead to a further improvement of the state-of-the-art result in the radiometric feature-based device identification.

Edge computing provides a promising solution to the resource-constrained problem of mobile devices running simultaneous localization and mapping (SLAM) systems. However, the existing traditional edge-assisted SLAM systems rely on ideal network conditions and static environment assumptions, which poses challenges for applications in bandwidth constrained networks and dynamic scenes. Aiming at the issue, this paper proposes fast and accurate edge-assisted semantic SLAM (FAES-SLAM) towards dynamic environments. First, an edge-assisted framework is presented by the integration of object detection and dense mapping, which accelerates the SLAM system and maintains accuracy. Then, an adaptive keyframe offloading strategy and an efficient dynamic feature point culling scheme are designed and incorporated into the above framework to improve the speed and accuracy further. The offloading strategy adaptively decreases the excessive latency in terms of various network conditions, and the culling scheme effectively increases the localization and mapping precision for dynamic scenarios. We perform extensive experiments on public datasets and in the actual coal mine laboratory. Compared with classical edge-assisted SLAM system, FAES-SLAM can achieve over 90.9% improvement in localization accuracy with a 13.2% increase in tracking time.

Satellites serve as a key component for the upcoming space-air-ground integrated networks, while their signals are susceptible to spoofing attacks. Radio frequency fingerprinting (RFF) has been recognized as a highly promising authentication approach to counteracting spoofing attacks. Despite extensive RFF schemes proposed for terrestrial networks, RFF for satellites remains largely unexplored except for a recently reported scheme named PAST-AI, which exploits the spatial property of the IQ imbalance of downlink signals to authenticate Iridium satellites. Although PAST-AI demonstrates the potential of RFF for satellite authentication, its authentication accuracy and time are unsatisfactory. To address this issue, this paper proposes a novel 3D convolution-based RFF scheme for Iridium satellite authentication, which exploits not only the spatial property but also the temporal property of the IQ imbalance. The proposed RFF scheme transforms short-period sequences of successive IQ samples into 3D data samples and uses a 3D convolutional neural network (CNN) to train an RFF model. To evaluate the authentication accuracy, we collected over 198000000 IQ samples from all 66 Iridium satellites and generated 1000 3D data samples for each satellite. The results showed that the proposed RFF scheme achieves more accurate authentication than PAST-AI using fewer IQ samples (i.e., shorter time).

Despite extensive efforts on exploring the asymptotic capacity bounds for mobile ad hoc networks (MANETs), the general exact capacity study of such networks remains a challenge. As one step to go further in this direction, this paper considers two classes of Aloha MANETs (A-MANETs) NAand NCthat adopt an aggressive traffic-independent Aloha and the conventional traffic-dependent Aloha, respectively. We first define a notation of successful transmission probability (STP) in NA, and apply queuing theory analysis to derive a general formula for the capacity evaluation of NA. We also prove that NCactually leads to the same throughput capacity as NA, indicating that the throughput capacity of NCcan be evaluated based on the STP of NA as well. With the help of the capacity formula and stochastic geometry analysis on STP, we then derive closed-form expressions for the throughput capacity of an infinite A-MANET under the nearest neighbor/receiver transmission policies. Our further analysis reveals that although it is highly cumbersome to determine the exact throughput capacity expression for a finite A-MANET, it is possible to have an efficient and closed-form approximation to its throughput capacity. Finally, we explore the capacity maximization and provide extensive simulation/numerical results.

We consider the secure transmission of information over Rayleigh fading wiretap channel when the main and eavesdropper channels are correlated there. Assuming that before transmission the transmitter knows the full channel state information (CSI) of both the main and eavesdropper channels, we analyze the average secrecy capacity with limited signal-to-noise ratio (SNR), for which a simple closed-form expression is derived based on the typical Marcum Q function. This is achieved by exploring the symmetry property between the main channel and eavesdropper channel to avoid a complicated integration involved in the secrecy capacity analysis, such that the secrecy capacity derivation can be significantly simplified. Numerical results are also provided to confirm our analytical ones.

This paper considers the transmission of confidential data over a quasi-static fading wiretap channel where the main and eavesdropper channels are correlated. Assuming that before transmission the transmitter knows the channel state information (CSI) of the main channel, we derive the secrecy outage probability in a closed-form expression based on a new secrecy outage probability formula, which gives a more explicit measure on the level of security compared with the previous one. Remarkably, our results, which cover the corresponding results when the main channel and eavesdropper channels are independent as special cases, reveal that channel correlation has a significant impact on secrecy outage probability and such impact can be helpful or harmful depending on the relative channel conditions and transmission SNR threshold.