1 citations · 1 across the 3 of their papers we have counts for
5 papers
Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction
Khoren Petrosyan, Artashes Mkrtchyan, Rafayel Mkrtchyan +2
Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pretraining offers a way forward…
On the Physical Plausibility and Distribution Alignment for Sim-to-Real RF Positioning
Ararat Saribekyan, Armen Manukyan, Hrant Khachatrian +1
Reliable radio frequency (RF) positioning from cellular measurements is limited by the high cost and limited coverage of real drive-test data, especially when models must work on s…
Measurement-Driven O-RAN Diagnostics with Tail Latency and Scheduler Indicators
Theofanis P. Raptis, Weronika Maria Bachan, Roberto Verdone
We investigate cross-layer performance diagnostics for an O-RAN instance by jointly analyzing application-level latency and radio-layer behavior from a real measurement campaign. M…
Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities
Rafayel Mkrtchyan, Armen Manukyan, Hrant Khachatrian +1
In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source…
On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments
Armen Manukyan, Hrant Khachatrian, Edvard Ghukasyan +1
We study the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in central Rome. We use a real measurement set of 1,664 user-equipments (UEs) and six nominal base-stat…