arXiv AI July 13, 2026 By Changpeng He, Yang Lu, Yanqing Xu, Chong-Yung Chi, Arumugam Nallanathan

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

2d ago

Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization

arXiv:2609.09708v2 Announce Type: replace-cross Abstract: 6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast band...

By Beier Li, Mai Vu

benchmarks

arXiv AI

Jul 9

AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems

arXiv:2607. 06768v1 Announce Type: cross Abstract: This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching antenna system (PASS).

By Seyed Mohammad Azimi-Abarghouyi, Christopher G. Brinton

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

arXiv AI

3d ago

A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks

The paper presents a large‑scale, 3GPP TR 38.901‑compliant dataset for RIS‑aided millimeter‑wave B5G networks, covering 20 deployment variants with diverse user densities, fading, and blockage conditions. Each sample includes oracle RIS phase configurations from a brute‑force search, along with full CSI, per‑link channel decomposition, optimal phase matrices, and CQI labels, enabling a wide range of machine‑learning tasks. The authors also introduce a novel CSI‑to‑CQI mapping as a benchmark for scalable link‑quality prediction and evaluate it against state‑of‑the‑art models under various conditions.

By Pujitha Mamillapalli, Pankaj Singh Rathour, Abhinav Kumar

efficiency benchmarks

arXiv Machine Learning

Jul 27

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression

arXiv:2506. 18748v2 Announce Type: replace-cross Abstract: We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users.

By Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro

arXiv AI

Jul 1

Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models

arXiv:2606. 31334v1 Announce Type: new Abstract: Joint OFDM-RIS optimization for 6G is a mixed-integer nonlinear programming (MINLP) problem covering sum-rate maximization, energy efficiency, max-min fairness, and peak-to-average power ratio (PAPR)-constrained objectives.

By Ahmet Kaplan

llms diffusion reinforcement-learning benchmarks safety

One email a morning, machine-written

Related stories

Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization

AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems

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

A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression

Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models