Zewei Guo | Searcharxiv

arXiv2026

Optimal Beamforming for Uplink Covert Communication in MIMO GEO Satellite-Terrestrial Systems

This paper investigates the uplink covert communication in a multiple-input multiple-output (MIMO) satellite-terrestrial system consisting of an Earth station transmitter Alice, a geosynchronous Earth orbit (GEO) satellite receiver Bob, and multiple GEO satellite wardens around Bob, where each node in the system is equipped with an array of directional antennas. Based on beamforming and the default antenna orientation setting, we first propose a scheme for covert Alice-Bob uplink transmission. Under the perfect channel estimation scenario, we provide theoretical modeling for the system performance in terms of detection error probability (DEP), transmission outage probability (TOP) and covert rate (CR), and then explore the optimal beamforming (OB) design as well as the joint optimal beamforming and antenna orientation (JO-BA) design for CR maximization. We then extend our study to the imperfect channel estimation scenario, and conduct related performance modeling and OB/JO-BA designs for CR maximization. We also apply the techniques of semidefinite relaxation, alternating optimization, Rodrigues' rotation formula and 1-D search algorithm to develop efficient algorithms to solve the above optimization problems. Finally, extensive numerical results are presented to verify our theoretical results and to illustrate the efficiency of beamforming and antenna orientation design for supporting the uplink covert communication in MIMO GEO satellite-terrestrial systems.

cs.IT↗

arXiv2025

SMoRFFI: A Large-Scale Same-Model 2.4 GHz Wi-Fi Dataset and Reproducible Framework for RF Fingerprinting

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 802.11g 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 89.06% identification accuracy on this dataset.

cs.NI↗