用卷积神经网络动态分配通信优先级，提升工业无线网的可靠低时延性能。
CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC
- 用CNN和图着色动态预测链路优先级，实时适应网络状态。
- 在三种组网下，信干噪比最高提升113%，调度成功率显著提高。
- 适合工业物联网中对时延和可靠性要求极高的场景。
在大规模工业无线网络中，保障分组级通信质量对超可靠低时延通信（URLLC）至关重要。本文通过引入基于卷积神经网络（CNN）的动态优先级预测机制，改进了局部截止时间划分（LDP）算法，以提升多小区、多信道网络中的干扰协调能力。与LDP采用静态优先级不同，本方法根据实时流量、传输机会和网络状况，自适应地分配链路优先级。假设训练阶段离线完成，该方法引入的开销极小，同时实现了更高效的资源分配，提升了网络容量、信号干扰噪声比（SINR）和可调度性。仿真结果表明，在三种网络配置下，相比LDP，SINR分别提升最高达113%、94%和49%，充分验证了其在复杂URLLC场景下的有效性。
原文摘要 · Abstract (English)
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.
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