To trust or not to trust: on calibration in ml-based resource allocation for wireless networks

In the next generation communications and networks, machine learning (ML) models are expected to deliver not only highly accurate predictions, but also well-calibrated confidence scores that reflect the true likelihood of correct decisions. In this paper, we study the calibration performance of an ML-based outage predictor within a single-user, multi-resource allocation framework. We begin by establishing key theoretical properties of this system’s outage probability (OP) under perfect calibration. Importantly, we show that as the number of resources grows, the OP of a perfectly calibrated predictor approaches the expected output conditioned on it being below the classification threshold. In contrast, when only a single resource is available, the system’s OP equals the model’s overall expected output. We then derive the OP conditions for a perfectly calibrated predictor. These findings guide the choice of the classification threshold to achieve a desired OP, helping system designers meet specific reliability requirements. We further demonstrate that post-processing calibration cannot improve the system’s minimum achievable OP, as it does not introduce additional information about future channel states. Additionally, we show that well-calibrated models are part of a broader class of predictors that necessarily improve OP. In particular, we establish a monotonicity condition that the accuracy-confidence function must satisfy for such improvement to occur. To demonstrate these theoretical properties, we conduct a rigorous simulation-based analysis using post-processing calibration techniques, namely, Platt scaling and isotonic regression. As part of this framework, the predictor is trained using an outage loss function specifically designed for this system. Furthermore, this analysis is performed on Rayleigh fading channels with temporal correlation captured by Clarke’s 2D model, which accounts for receiver mobility. Notably, the outage investigated refers to the required resource failing to achieve the transmission capacity requested by the user.