arXiv Machine Learning

Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization

arXiv Machine Learning

Sep 1

Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI

arXiv:2608.30524v1 Announce Type: cross Abstract: Beamforming methods in millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (CFmMIMO) systems require accurate channel state inf...

By Sina Tavakolian, Abolfazl Zakeri, Ahmed Alkhateeb, Markku Juntti, Nhan Thanh Nguyen

arXiv AI

Jul 13

RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approaches

arXiv:2511. 20305v2 Announce Type: replace-cross Abstract: This paper investigates a reconfigurable intelligent surface (RIS)-assisted multi-waveguide pinching-antenna (PA) system (PASS) for multi-user downlink information transmission, motivated by the unknown impact of the integration of emerging PASS and RIS on wireless communications.

By Changpeng He, Yang Lu, Yanqing Xu, Chong-Yung Chi, Arumugam Nallanathan

arXiv Machine Learning

Jun 2

GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising

arXiv:2511. 06663v2 Announce Type: replace-cross Abstract: Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks.

By Yuhang Li, Yang Lu, Bo Ai, Zhiguo Ding, Arumugam Nallanathan

llms

arXiv Machine Learning

Jun 2

BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization

arXiv:2509. 11056v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is anticipated to emerge as a pivotal enabler for the forthcoming sixth-generation (6G) wireless communication systems.

By Yuhang Li, Yang Lu, Wei Chen, Bo Ai, Zhiguo Ding

llms nlp fine-tuning

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

Jun 15

Generalizing GNNs with Tokenized Mixture of Experts

arXiv:2602. 09258v2 Announce Type: replace Abstract: Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations.

By Xiaoguang Guo, Zehong Wang, Jiazheng Li, Shawn Spitzel, Qi Yang, Kaize Ding, Jundong Li, Chuxu Zhang

efficiency benchmarks

arXiv Machine Learning

Aug 18

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.

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

arXiv Machine Learning

2d ago

MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs

arXiv:2609.40170v1 Announce Type: new Abstract: MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple fr...

By Tomer Alter, Nir Shlezinger, Michael Segal

efficiency

arXiv Machine Learning

Jul 16

RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction

arXiv:2508. 16403v3 Announce Type: replace Abstract: Accurately predicting the performance of active radio frequency (RF) circuits is essential for modern wireless systems but remains challenging due to highly nonlinear behavior and the high computational cost of traditional simulation tools.

By Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband

multimodal benchmarks

arXiv Machine Learning

Sep 22

TARGet: Topology-Aware Fusion-based Radio Frequency Circuit Functional Modeling using Graph Neural Networks

TARGet is an open‑source, topology‑aware machine‑learning framework for modeling RF circuits. It uses S‑parameter representations of sub‑circuits and a fusion architecture that combines Graph Neural Networks with sub‑circuit connectivity‑aware networks, enabling learning across multiple topologies. Experiments show TARGet achieves sub‑1% prediction error, reduces training data needs by up to 35.5×, and improves accuracy by 9.7× over state‑of‑the‑art models, with zero‑shot transfer to unseen sub‑circuit topologies.

By Soroosh Noorzad, Sebastian Bodero, Morteza Fayazi

benchmarks

Hugging Face Trending Papers

3d ago

MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs

MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple frequency channels and support heterogeneous traffi...

efficiency

arXiv Machine Learning

Aug 18

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

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