Research
Latest academic paper digests, data charts, and research insights from the 6G and AI research community.
62 papers
Wireless Power Transfer for 6G Massive IoT: System Design and Optimization
Dr. Bruno Clerckx, Prof. Rui Zhang — Imperial College London / National University of Singapore
We present a comprehensive system design for wireless power transfer (WPT) in 6G massive IoT networks. Our approach uses multi-antenna energy beamforming to charge thousands of IoT sensors simultaneously using 6G base station infrastructure. We develop an AI-based scheduling algorithm that jointly optimizes energy transfer and data communication, demonstrating that 95% of IoT devices within 20 meters can maintain perpetual operation without batteries.
Extremely Large Aperture Arrays (ELAA) for 6G: Near-Field MIMO
Prof. Luca Sanguinetti, Dr. Andrea de Jesus Torres — University of Pisa
We study extremely large aperture arrays (ELAA) for 6G systems where the array size is so large that many users fall within the near-field region. In this regime, the traditional far-field plane-wave assumption breaks down, and spherical wave propagation must be considered. We derive new capacity expressions for near-field MIMO and show that ELAA provides 2.5x higher spectral efficiency than conventional massive MIMO by exploiting distance-dependent focusing in addition to angular beamforming.
Intelligent Reflecting Surfaces Aided Cell-Free Networks: A Unified Framework
Dr. Trinh Van Chien, Prof. Emil Bjornson — Linkoping University
We develop a unified analytical framework for RIS-aided cell-free massive MIMO networks, combining two of the most promising 6G technologies. Our framework jointly optimizes access point beamforming and RIS phase shifts in a distributed manner suitable for practical deployment. Simulations show that adding RIS to cell-free networks improves energy efficiency by 60% and extends coverage to previously unreachable areas, with the combined system outperforming either technology alone by a significant margin.
Foundation Models for Wireless: Pre-Training on Network Data at Scale
Dr. Ahmed Alkhateeb, Dr. Umut Demirhan — Arizona State University
We introduce WirelessFM, a foundation model pre-trained on 100TB of diverse wireless network data including channel measurements, traffic patterns, KPIs, and configuration parameters from 50 operators worldwide. WirelessFM can be fine-tuned for any downstream wireless task with minimal data, achieving state-of-the-art results on 12 benchmark tasks including channel estimation, traffic prediction, and anomaly detection. The model reduces the data requirement for new task adaptation by 20x.
Causal Inference for Root Cause Analysis in Autonomous Networks
Dr. Fatemeh Ghassemi, Prof. Mung Chiang — Purdue University
We apply causal inference methods to network root cause analysis, moving beyond correlational ML approaches that often mislead. Our causal discovery algorithm constructs a directed acyclic graph of KPI dependencies from observational network data, enabling identification of true root causes rather than symptoms. On a dataset from a tier-1 operator with 10,000 fault incidents, causal analysis correctly identifies root causes in 88% of cases, compared to 62% for correlation-based methods.
Multi-Modal AI for Predictive Network Maintenance Using Telemetry and Images
Dr. Jie Xu, Dr. Olav Tirkkonen — Aalto University / Nokia Bell Labs
We propose a multi-modal AI system for predictive maintenance that fuses network telemetry data (KPIs, alarms, counters) with visual inspection data (drone images, thermal camera feeds) from cell sites. A cross-modal attention mechanism learns correlations between equipment visual condition and network performance degradation patterns. The system predicts equipment failures 14 days in advance with 92% precision, reducing emergency site visits by 55% in a 6-month field trial with a European operator.
Federated Split Learning for Privacy-Preserving AI in Multi-Operator Networks
Dr. Kaibin Huang, Dr. Deniz Gunduz — University of Hong Kong / Imperial College London
We propose federated split learning (FSL) as a privacy-preserving AI framework for multi-operator 6G network optimization. FSL splits the neural network model between operator premises and a neutral aggregation server, with only intermediate representations (not raw data) shared. This provides stronger privacy than standard federated learning while reducing on-device computation. Applied to multi-operator spectrum sharing, FSL achieves 95% of the performance of centralized training while provably protecting each operator's proprietary data.
AI-Driven Semantic-Aware Network Slicing for 6G Services
Dr. Petar Popovski, Dr. Elli Stai — Aalborg University / NTUA Athens
We introduce semantic-aware network slicing where AI models understand the semantic importance of data flows to dynamically allocate resources. Unlike QoS-based slicing that treats all bits equally, our approach prioritizes semantically important information within each slice. A semantic extraction module identifies critical content in video, speech, and sensor data streams, enabling 3x bandwidth reduction while maintaining task-specific quality metrics. Evaluation on a multi-service 5G testbed shows 40% improvement in perceived quality under congestion.
AI Agents for Autonomous Network Operations: Architecture and Evaluation
Dr. Laurent Ciavaglia, Dr. Razvan Beuran — Rakuten Mobile / NICT Japan
We present an AI agent architecture for autonomous network operations where LLM-based agents plan and execute multi-step network management tasks. Our system decomposes high-level operator intents into sequences of actions, executes them with safety guardrails, and learns from outcomes to improve future performance. In a production trial managing 200 base stations, the AI agent successfully resolved 78% of common network incidents autonomously, reducing mean time to repair by 65%.