ML-Assisted Bulk Resource Allocation: Custom Outage-Based Loss Function and Reliability Analysis

TL;DR

This paper extends outage-based machine learning resource allocation to multiple resources, introducing a novel loss function and analysis that significantly improve reliability in complex wireless systems.

Contribution

It proposes a ranking-aware bulk outage loss and a practical allocation policy for multi-resource allocation, advancing beyond single-resource methods.

Findings

RBOL outperforms traditional losses and baselines in reducing bulk outage probability.

The proposed methods approach the fundamental reliability limits established by the oracle bound.

Extensive simulations validate the effectiveness of the ranking-aware training in various stress regimes.

Abstract

Machine learning (ML)-assisted outage-based resource allocation has recently emerged as an effective alternative to conventional scheduling methods in reliability-critical wireless systems. However, existing approaches are fundamentally limited to single-resource allocation, whereas modern and emerging systems increasingly require the simultaneous allocation of multiple resources to meet aggregate rate and reliability constraints. In this paper, we extend outage-based learning to the bulk resource allocation regime, where a user requires at least $D$ reliable resources from a pool of $R$ candidates. We first introduce a practical allocation policy, termed gate + top-$D$ allocation (GTBA), which combines threshold-based admission control with ranking-based selection. We then propose a novel ranking-aware bulk outage loss (RBOL) that provides a differentiable surrogate for the bulk outage event induced by GTBA, explicitly accounting for both gate failures and ranking errors near the selection boundary. An exact reliability analysis is developed, establishing a decomposition of bulk outage probability (BOP), identifying dominant failure mechanisms and deriving an oracle lower bound that characterizes the fundamental performance limit. Extensive simulations under balanced, light and heavy stress regimes demonstrate that RBOL consistently outperforms conventional pointwise losses and baselines, achieving substantial reductions in BOP and remaining significantly closer to the oracle bound across a wide range of operating conditions. These results confirm that set-level, ranking-aware training objectives are essential for reliable ML-assisted bulk resource allocation.