Machine Learning (ML) is a popular tool that will be pivotal in enabling 6G and beyond communications. This paper focuses on applying ML solutions to address outage probability issues commonly encountered in these systems. In particular, we consider a single-user multi-resource greedy allocation strategy, where an ML binary classification predictor assists in seizing an adequate resource. With no access to future channel state information, this predictor foresees each resource's likely future outage status. When the predictor encounters a resource it believes will be satisfactory, it allocates it to the user. Critically, the goal of the predictor is to ensure that a user avoids an unsatisfactory resource since this is likely to cause an outage. Our main result establishes exact and asymptotic expressions for this system's outage probability. With this, we formulate a theoretically optimal, differentiable loss function to train our predictor. We then compare predictors trained using this and traditional loss functions; namely, binary cross-entropy (BCE), mean squared error (MSE), and mean absolute error (MAE). Predictors trained using our novel loss function provide superior outage probability in all scenarios. Our loss function sometimes outperforms predictors trained with the BCE, MAE, and MSE loss functions by multiple orders of magnitude.

翻译：机器学习（ML）是一种流行工具，将在实现 6G 及未来通信中发挥关键作用。本文聚焦于应用 ML 解决方案来解决此类系统中常见的中断概率问题。具体而言，我们考虑一种单用户多资源贪婪分配策略，其中 ML 二元分类预测器辅助获取合适资源。由于无法获取未来信道状态信息，该预测器会预先判断各资源未来可能的中断状态。当预测器遇到自认为可用的资源时，便将其分配给用户。关键在于，预测器的目标在于确保用户避免使用不可用资源，因为这很可能引发中断。我们的主要结果为该系统中断概率建立了精确与渐近表达式。基于此，我们推导出理论最优的可微损失函数来训练预测器。随后，我们比较了使用此损失函数与传统的二元交叉熵（BCE）、均方误差（MSE）及平均绝对误差（MAE）损失函数训练的预测器。在所有场景下，使用新损失函数训练的预测器均能提供更优越的中断概率。在某些情况下，我们的损失函数相比 BCE、MAE 及 MSE 损失函数训练的预测器，性能提升可达数个数量级。