9 citations · 18 across the 9 of their papers we have counts for
9 papers
Interpretable Feature Learning for RF Fingerprinting via Polar MKANs
Mikhail Krasnov, Ljupcho Milosheski, Carolina Fortuna
Radio frequency (RF) fingerprinting authenticates wireless devices from hardware-induced I/Q impairments, typically with deep learning feature extractors that are accurate but opaq…
Radio Environment Mapping with World Models for Active Measurement Control: Should Networks Dream of Optimal Control?
Jernej Hribar, Ljupcho Milosheski, Ryoichi Shinkuma
Radio Environment Maps (REMs) have the potential to serve as an important enabler for intelligent modeling and control in emerging AI-native 6G networks. Despite significant progre…
Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation
Ljupcho Milosheski, Fedja Močnik, Mihael Mohorčič +1
Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tatio…
Design Principles of Zero-Shot Self-Supervised Unknown Emitter Detectors
Mikhail Krasnov, Ljupcho Milosheski, Mihael Mohorčič +1
The proliferation of wireless devices necessitates more robust and reliable emitter detection and identification for critical tasks such as spectrum management and network security…
An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI
Ljupcho Milosheski, Kuon Akiyama, Blaž Bertalanič +2
The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, Extended Reality (XR), etc.…
Spectrum Sensing with Deep Clustering: Label-Free Radio Access Technology Recognition
Ljupcho Milosheski, Mihael Mohorčič, Carolina Fortuna
The growth of the number of connected devices and network densification is driving an increasing demand for radio network resources, particularly Radio Frequency (RF) spectrum. Giv…
Deep Feature Learning for Wireless Spectrum Data
Ljupcho Milosheski, Gregor Cerar, Blaž Bertalanič +2
In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning ar…
XAI for Self-supervised Clustering of Wireless Spectrum Activity
Ljupcho Milosheski, Gregor Cerar, Blaž Bertalanič +2
The so-called black-box deep learning (DL) models are increasingly used in classification tasks across many scientific disciplines, including wireless communications domain. In thi…
Self-supervised Learning for Clustering of Wireless Spectrum Activity
Ljupcho Milosheski, Gregor Cerar, Blaž Bertalanič +2
In recent years, much work has been done on processing of wireless spectrum data involving machine learning techniques in domain-related problems for cognitive radio networks, such…