CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC

TL;DR

This paper introduces a CNN-based dynamic priority scheduling method for URLLC in industrial wireless networks, significantly improving interference management, resource allocation, and network performance over traditional static approaches.

Contribution

It presents a novel CNN-enabled scheduling mechanism that adaptively predicts link priorities, enhancing interference coordination and resource efficiency in URLLC networks.

Findings

Achieved up to 113% SINR improvement over LDP.

Enabled more efficient resource allocation and network capacity.

Demonstrated effectiveness in complex URLLC scenarios through simulations.

Abstract

Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a CNN-based dynamic priority prediction mechanism for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach uses a Convolutional Neural Network and graph coloring to adaptively assign link priorities based on real-time traffic, transmission opportunities, and network conditions. Assuming that first training phase is performed offline, our approach introduced minimal overhead, while enabling more efficient resource allocation, boosting network capacity, SINR, and schedulability. Simulation results show SINR gains of up to 113\%, 94\%, and 49\% over LDP across three network configurations, highlighting its effectiveness for complex URLLC scenarios.