arXiv Machine Learning

Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation

arXiv:2608. 14676v1 Announce Type: cross Abstract: In 5G/6G wireless systems, accurate and timely channel estimation is critical to ensure reliable communication under complex, fast-changing radio conditions.

arXiv Machine Learning

1d ago

Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation

arXiv:2608. 14709v1 Announce Type: cross Abstract: This demo presents real-time AI-based uplink channel-estimation inference using data collected from a hardware-in-the-loop 5G platform.

By Javad Zolfaghari-Bengar, Rakibul Rony, Elisa Gomez-de-Lope, Alejandro Villena-Rodriguez, Abhinav Mahadevan, Nicolas Kourtellis

efficiency

arXiv Machine Learning

1d ago

Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices

arXiv:2507. 09627v3 Announce Type: replace-cross Abstract: Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced connectivity.

By Muhammad Kamran Saeed, Ashfaq Khokhar, Shakil Ahmed

arXiv Machine Learning

Aug 7

EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver

arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.

By Mikko Honkala, Dani Korpi, Elias Raninen, Janne M. J. Huttunen

arXiv Machine Learning

Jul 31

Neural Network-Assisted CLEAN for Channel Modeling in Low-SNR Regimes

arXiv:2607. 27450v1 Announce Type: new Abstract: Accurate multipath parameter estimation is critical for modern wireless communication systems, particularly in challenging low-SNR environments.

By Chaofan Deng, Linyu Sun, Jaeho Lee, Arijit Raychowdhury

arXiv Machine Learning

Aug 4

An Uncertainty-Driven Hybrid Deep Learning Approach for Broad-Coverage RF Modulation Recognition

arXiv:2608. 00796v1 Announce Type: cross Abstract: Automatic RF modulation recognition is of critical importance in spectrum monitoring, electronic warfare, and cognitive radio applications, where low signal-to-noise ratio (SNR) conditions and the growing diversity of modulation schemes limit the performance of existing methods.

By Nurettin Safak, Durdu Can Yerdeyatar, Muhammet Sefa Demirel, Alperen Marasli, Taha Eren Atmaca, Ozgun Ersoy

arXiv AI

Jul 28

Fast Cross-Scenario Adaptation of CSI Models via Channel Conditional Parameter Generation

arXiv:2607. 22637v1 Announce Type: new Abstract: Deep learning has shown strong potential for massive multiple-input multiple-output (Massive MIMO) physical-layer tasks, including channel state information (CSI) feedback and channel estimation.

By Xudong Zou, Siyu Wu, Zunlei Feng, Jie Song, Yuanyu Wan, Mingli Song, Jiacong Hu

diffusion fine-tuning

arXiv AI

Jul 14

JEPA for AI-Native 6G: Predictive Representations and Open Challenges

arXiv:2607. 09798v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core.

By Sheikh Salman Hassan, Irshad A. Meer, Almoatssimbillah Saifaldawla, Yan Kyaw Tun, Mustafa Ozger, Madyan Alsenwi, Nguyen Van Huynh, Woong-Hee Lee, Cedomir Stefanovic, Mathini Sellathurai, Henk Wymeersch, Tharmalingam Ratnarajah

rag diffusion benchmarks

arXiv Machine Learning

Jun 11

REACH: Interpretability-Driven Feature Identification and Architecture Compression for Multi-Channel Vehicular Channel Estimation

arXiv:2606. 11857v1 Announce Type: cross Abstract: Multi-channel mixed-SNR training improves out-of-distribution (OOD) generalisation of deep learning channel estimators for IEEE 802.

By Simbarashe Aldrin Ngorima, Albert Helberg, Marelie H. Davel

safety

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 AI

Jul 2

Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring

arXiv:2607. 00251v1 Announce Type: cross Abstract: While most image deblurring techniques directly restore the spatial image variable, we propose an amplitude and phase decomposition recognizing the importance of accurate phase estimation in recovering sharp image details.

By Samira Malek, Haichuan Zhang, Chul Lee, Vishal Monga

benchmarks

arXiv Machine Learning

Jun 17

X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation

arXiv:2602. 22277v2 Announce Type: replace Abstract: AI-native architectures are vital for 6G wireless communications.

By Abdul Karim Gizzini, Yahia Medjahdi

fine-tuning safety

arXiv AI

1d ago

A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks

arXiv:2608. 14694v1 Announce Type: new Abstract: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks.

By Naveed Khan, Besan Al Sbeihi, Maryam Alshehhi, Nasir Saeed

efficiency benchmarks safety

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