newsResearchers propose WirelessAgent++, an automated framework for designing AI agent workflows in wireless networks using Monte Carlo Tree Search. The system achieves superior performance on wireless tasks with test scores up to 97%, outperforming existing methods by up to 31% while maintaining low computational costs under $5 per task.
Researchers present a novel quantum federated learning framework for large-scale wireless networks that combines quantum computing with privacy-preserving federated learning. The study introduces a sum-rate maximization approach using quantum approximate optimization algorithm (QAOA) that achieves over 100% improvement in performance compared to conventional methods.
This academic survey examines mobility management challenges in 5G networks, focusing on handover processes between base stations. The research addresses issues like handover blocking and unnecessary handovers that affect network performance as mobile users increase.
Researchers propose federated agentic AI approaches for wireless networks to address challenges of centralized AI architectures including high communication overhead and privacy risks. The paper introduces how federated learning can enhance autonomous AI systems in distributed wireless environments through collaborative learning without raw data exchange.