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

2025/06/17 by Alqudah, Eman, Khokhar, Ashfaq · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #IoT Networks and Protocols #Machine Learning (cs.LG) #Network Time Synchronization Technologies #Networking and Internet Architecture (cs.NI) #Wireless Communication Security Techniques

paper · pdf · doi:10.48550/arxiv.2506.14987

openalex publication_date 2025/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
• Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC
• Task-Oriented Delay-Aware Multi-Tier Computing in Cell-free Massive MIMO Systems
• Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications
• Scheduling with Probabilistic Per-Packet Real-Time Guarantee for URLLC
• A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems

Cited by

• Efficient Parallel Implementation of the Pilot Assignment Problem in Massive MIMO Systems
• GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC

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