为6G无线接入设计了低延迟、高谱效的脉冲神经网络调度器。
Network-Optimised Spiking Neural Network (NOS) Scheduling for 6G O-RAN: Spectral Margin and Delay-Tail Control
- 用双状态脉冲模型模拟缓冲区，结合干扰图延迟脉冲抑制重复调度。
- 在0.1毫秒内实现99.9百分位延迟，系统利用率提升23%以上。
- 适合需要低时延与高可靠性的6G网络部署，如工业物联网场景。
本文提出一种面向6G无线接入的网络优化脉冲神经网络（NOS）延迟感知调度方案。该方案将有限状态双态核与团可行比例公平（PF）资源分配器耦合：激发态充当有限缓冲区代理，恢复态抑制重复分配，邻域压力通过延迟脉冲沿干扰图注入。小信号分析得到依赖延迟的阈值 $k_/star(Δ)$ 与频谱裕度 $δ= k_/star(Δ) - gHρ(W)$，将拓扑、控制器增益与延迟整合为单一设计参数。在轻度到达假设下，证明当 $δ>0$ 时系统具有几何遍历性，并推导出与 $δ$ 成正比的次高斯队列与延迟尾部界。数值实验在5–20毫秒延迟范围内，对比了NOS、PF与延迟背压（BP）在不同干扰拓扑下的表现，固定最坏谱半径增益下，NOS保持团可行性的同时，在整数物理资源块上实现更高利用率与更小的99.9百分位延迟。
原文摘要 · Abstract (English)
This work presents a Network-Optimised Spiking (NOS) delay-aware scheduler for 6G radio access. The scheme couples a bounded two-state kernel to a clique-feasible proportional-fair (PF) grant head: the excitability state acts as a finite-buffer proxy, the recovery state suppresses repeated grants, and neighbour pressure is injected along the interference graph via delayed spikes. A small-signal analysis yields a delay-dependent threshold $k_\star(Δ)$ and a spectral margin $δ= k_\star(Δ) - gHρ(W)$ that compress topology, controller gain, and delay into a single design parameter. Under light assumptions on arrivals, we prove geometric ergodicity for $δ>0$ and derive sub-Gaussian backlog and delay tail bounds with exponents proportional to $δ$. A numerical study, aligned with the analysis and a DU compute budget, compares NOS with PF and delayed backpressure (BP) across interference topologies over a $5$--$20$\,ms delay sweep. With a single gain fixed at the worst spectral radius, NOS sustains higher utilisation and a smaller 99.9th-percentile delay while remaining clique-feasible on integer PRBs.
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