Network-Optimised Spiking Neural Network (NOS) Scheduling for 6G O-RAN: Spectral Margin and Delay-Tail Control
This work addresses scheduling challenges for 6G networks, offering improved performance in terms of delay and utilization, but it appears incremental as it builds on existing scheduling frameworks with novel adaptations.
The paper tackles the problem of scheduling in 6G radio access networks by proposing a Network-Optimised Spiking (NOS) scheduler that controls spectral margin and delay-tail, resulting in higher utilization and smaller 99.9th-percentile delays compared to existing methods like proportional-fair and delayed backpressure.
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.