Papers for

industrial wireless engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Graph neural networks boost energy efficiency in 6g networks

Learning to Maximize Energy Efficiency in 6G in-X Subnetworks

Abstract: This paper investigates energy-efficient power control in 6G in-X subnetworks. We consider a graph neural network (GNN) framework that captures inter-subnetwork interference and the underlying wireless topology to optimize transmit powers. Three energy efficiency (EE) formulations are studied: (i) network-centric, which maximizes total network energy efficiency; (ii) subnetwork-centric, which maximizes the average energy efficiency per subnetwork; and (iii) a multi-objective approach, which balances energy efficiency and sum-rate performance. Extensive simulations in industrial factory settings with 3GPP channel models demonstrate that the GNN effectively learns interference-aware power allocation policies, significantly outperforming maximum power transmission and existing GNN based power control solution. Results showed network EE gains of up to 1341%, average per-device EE improvements of up to 1302%, and sum-rate enhancements up to 24.7% relative to a maximum transmit power policy, depending on the chosen optimization formulation and trade-off settings.

Mon 21 SeptNetworking and Internet Architecture

The gist

Transmitting data wirelessly uses energy, and sometimes devices interfere with each other, wasting power. The paper shows how a type of AI called graph neural networks can learn to manage power use better by understanding connections and interference in 6G wireless networks. This learning helps devices use less energy while still sending data quickly. The authors tested their approach in factory settings and found big improvements over usual methods.

Open → 2609.24263v1