Work

Attention-Aided Channel Prediction for Efficient Resource Management in Industrial IoT Subnetworks

Year: 2025

Type: article

Abstract: Channel State Information (CSI) is critical for optimizing wireless communication systems, particularly in Industrial Internet of Things (IIoT) networks where real-time performance and reliability are paramount. In high-density 6G-enabled factory environments, the mobility of subnetworks and dense deployments exacerbate interference, necessitating proactive and efficient resource management. This paper introduces a dual attention-based channel prediction framework, combined with an AI-driven resource allocation model, to mitigate the challenges of outdated CSI in IIoT subnetworks. By employing spatio-temporal attention mechanisms, the proposed framework effectively predicts CSI and integrates sub-band allocation with power control for optimized resource utilization. Simulation results highlight significant gains in spectral efficiency, enhanced Quality of Service (QoS) adherence, and superior robustness to delay, outperforming state-of-the-art methods. These advancements make the proposed solution a scalable and reliable choice for next-generation IIoT deployments.

Source: IEEE Internet of Things Journal

DOI: 10.1109/jiot.2025.3604804

Authors: Saeed Hakimi, Gilberto Berardinelli, Ramoni Adeogun

Institution: Aalborg University

Language: English

Topic: Advanced Wireless Network Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,645