arXiv Machine Learning

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.

arXiv AI

1d ago

Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach

arXiv:2608. 14599v1 Announce Type: cross Abstract: The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks.

By Zhenyu Tao, Yuxuan Li, Wei Xu, Yongming Huang, Xiaohu You

diffusion reinforcement-learning benchmarks

arXiv AI

Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci

llms agents reinforcement-learning fine-tuning

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

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 Machine Learning

Aug 11

ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems

arXiv:2608. 08804v1 Announce Type: cross Abstract: With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks.

By Zhanyu Ju, Wenchi Cheng

agents reinforcement-learning

arXiv Machine Learning

Aug 6

FM4WiFi: Flow Matching for Multi-AP Coordination in Dense Deployments of Beyond Wi-Fi 8 Networks

arXiv:2608. 04050v1 Announce Type: cross Abstract: Wi-Fi networks are moving beyond random channel access toward tightly coordinated operation across access points (APs), a shift reflected in Wi-Fi 8's multi-AP coordination (MAPC).

By Maksymilian Wojnar, Krzysztof Rusek, Katarzyna Kosek-Szott, Szymon Szott

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 24

RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning

arXiv:2607. 20909v1 Announce Type: cross Abstract: Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical for spectrum management, interference mitigation, and localization in modern wireless networks.

By Liu Yang, Qiang Li, Zhuo Cao, Weijie Xiong, Guomin Sun, Jingran Lin

diffusion fine-tuning benchmarks

arXiv Machine Learning

Jul 21

EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning

arXiv:2607. 16449v1 Announce Type: new Abstract: Accurate path loss prediction is a critical component of wireless network planning.

By Jonathan O'Shea (DCU School of Electronic Engineering), Conor Brennan (DCU School of Electronic Engineering)

arXiv AI

Jul 7

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.

By Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng

agents reinforcement-learning benchmarks safety

arXiv AI

Jul 28

Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning

arXiv:2607. 22697v1 Announce Type: new Abstract: Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution.

By Nadine Chang, Maying Shen, Shizhe Diao, Jialiang Wang, Jingde Chen, Thomas Breuel, Pavlo Molchanov, Rafid Mahmood, Jose M. Alvarez

llms agents robotics

arXiv AI

Jun 24

Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN

arXiv:2606. 24483v1 Announce Type: cross Abstract: The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage.

By Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi, Mahnaz Arvaneh, Walid Saad, Hamed Ahmadi

fine-tuning

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