arXiv Machine Learning

Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators

arXiv AI

Jun 15

Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities

arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: 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 platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.

By Rafayel Mkrtchyan, Armen Manukyan, Hrant Khachatrian, Theofanis P. Raptis

llms computer-vision

arXiv AI

Jul 3

Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

arXiv:2607. 01777v1 Announce Type: cross Abstract: Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization.

By Lizhou Liu, Xiaohui Chen, Zihan Tang, Mengyao Ma, Wenyi Zhang

diffusion

arXiv AI

Sep 15

Data driven approach for Outdoor Channel Prediction in 5G and Beyond

arXiv:2605.01777v2 Announce Type: replace-cross Abstract: An evolution of Wireless Communications towards 5G and beyond provides improved user experience in terms of quality of services. Understandin...

By A. Sathi Babu, V. Udaya Sankar, Vishnu Ram OV

arXiv AI

Jul 7

Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations

arXiv:2605. 12569v2 Announce Type: replace-cross Abstract: Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning, especially in indoor and multipath-rich environments where source localization is highly challenging.

By M. Shamail J. Khan, Nisha L. Raichur, Lucas Heublein, Christian Wielenberg, Alexander Mattick, Tobias Feigl, Christopher Mutschler, Felix Ott

agents reinforcement-learning

arXiv Machine Learning

Sep 2

BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Management

arXiv:2609.00615v1 Announce Type: cross Abstract: The evolution toward sixth-generation (6G) wireless networks is driving larger antenna arrays and highly directional multi-beam transmission, making...

By Yue Zhang, Xiucheng Wang, Wenshuo Chen, Nan Cheng

diffusion

arXiv Machine Learning

Jul 7

Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches

arXiv:2604. 22056v3 Announce Type: replace Abstract: Optimal wireless transmitter placement is a central task in radio-network planning, and exhaustive search becomes prohibitively expensive at scale.

By \c{C}a\u{g}kan Yapar

diffusion benchmarks

arXiv AI

Jun 19

On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments

arXiv:2507. 19653v2 Announce Type: replace-cross Abstract: We study the realism of Sionna v1.

By Armen Manukyan, Hrant Khachatrian, Edvard Ghukasyan, Theofanis P. Raptis

arXiv Machine Learning

Jul 10

RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization

arXiv:2607. 08045v1 Announce Type: cross Abstract: Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks.

By Xiucheng Wang, Junxi Huang, Nan Cheng

llms diffusion safety

arXiv AI

Aug 6

MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation

arXiv:2608. 05076v1 Announce Type: cross Abstract: Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals.

By Blessed Guda, Kayley Sze, Carlee Joe-Wong

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jul 7

Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios

arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.

By Nisha L. Raichur, Lucas Heublein, Dominik Seu{\ss}, Frank Deinzer, Felix Ott

arXiv Machine Learning

Aug 14

Self-Localizing MIMO Beam Mapping for Intelligent Open RAN with Continuously Evolving Channel Memory

arXiv:2511. 17007v2 Announce Type: replace-cross Abstract: Open and intelligent radio access networks (RANs) envisioned for 6G require accurate and reusable wireless channel knowledge for intelligent inference and control.

By Wangqian Chen, Junting Chen, Shuguang Cui

rag diffusion

arXiv Machine Learning

Sep 14

The Vienna 4G/5G Drive-Test Dataset

The Vienna 4G/5G Drive-Test Dataset is a city‑scale open dataset of georeferenced LTE and 5G NR measurements collected across Vienna, Austria. It combines passive wideband scanner observations with active handset logs, offering complementary network‑side and user‑side views of deployed radio access networks. The dataset includes inferred base‑station deployment descriptors, high‑resolution building and terrain models, and is organized into scanner, handset, estimated cell information, and city‑model components to support reproducible benchmarking in environment‑aware learning, propagation modeling, coverage analysis, and ray‑tracing calibration workflows.

By Wilfried Wiedner, Lukas Eller, Mariam Mussbah, Dominik R\"ossler, Valerian Maresch, Philipp Svoboda, Markus Rupp

benchmarks

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